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overflow: hidden; text-overflow: ellipsis; -webkit-line-clamp: 3; -webkit-box-orient: vertical; }</style><div class="col-xs-12 clearfix"><div class="u-floatLeft"><h1 class="PageHeader-title u-m0x u-fs30">Neural Oscillations</h1><div class="u-tcGrayDark">3,355 Followers</div><div class="u-tcGrayDark u-mt2x">Recent papers in <b>Neural Oscillations</b></div></div></div></div></div></div><div class="TabbedNavigation"><div class="container"><div class="row"><div class="col-xs-12 clearfix"><ul class="nav u-m0x u-p0x list-inline u-displayFlex"><li class="active"><a href="https://www.academia.edu/Documents/in/Neural_Oscillations">Top Papers</a></li><li><a href="https://www.academia.edu/Documents/in/Neural_Oscillations/MostCited">Most Cited Papers</a></li><li><a href="https://www.academia.edu/Documents/in/Neural_Oscillations/MostDownloaded">Most Downloaded Papers</a></li><li><a href="https://www.academia.edu/Documents/in/Neural_Oscillations/MostRecent">Newest Papers</a></li><li><a class="" href="https://www.academia.edu/People/Neural_Oscillations">People</a></li></ul></div><style type="text/css">ul.nav{flex-direction:row}@media(max-width: 567px){ul.nav{flex-direction:column}.TabbedNavigation li{max-width:100%}.TabbedNavigation li.active{background-color:var(--background-grey, #dddde2)}.TabbedNavigation li.active:before,.TabbedNavigation li.active:after{display:none}}</style></div></div></div><div class="container"><div class="row"><div class="col-xs-12"><div class="u-displayFlex"><div class="u-flexGrow1"><div class="works"><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_9519008 coauthored" data-work_id="9519008" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/9519008/Tuning_pathological_brain_oscillations_with_neurofeedback_A_systems_neuroscience_framework">Tuning pathological brain oscillations with neurofeedback: A systems neuroscience framework</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">Neurofeedback is emerging as a promising technique that enables self-regulation of ongoing brain oscillations. However, despite a rise in empirical evidence attesting to its clinical benefits, a solid theoretical basis is still lacking on... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_9519008" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">Neurofeedback is emerging as a promising technique that enables self-regulation of ongoing brain oscillations. However, despite a rise in empirical evidence attesting to its clinical benefits, a solid theoretical basis is still lacking on the manner in which neurofeedback is able to achieve these outcomes. The present work attempts to bring together various concepts from neurobiology, engineering, and dynamical systems so as to propose a contemporary theoretical framework for the mechanistic effects of neurofeedback. The objective is to provide a firmly neurophysiological account of neurofeedback, which goes beyond traditional behaviorist interpretations that attempt to explain psychological processes solely from a descriptive standpoint whilst treating the brain as a ‘black box’. To this end, we interlink evidence from experimental findings that encompass a broad range of intrinsic brain phenomena: starting from ‘bottom-up’ mechanisms of neural synchronization, followed by ‘top-down’ regulation of internal brain states, moving to dynamical systems plus control-theoretic principles, and concluding with activity-dependent as well as homeostatic forms of brain plasticity. In support of our framework, we examine the effects of neurofeedback in several brain disorders, including attention-deficit hyperactivity (ADHD) and post-traumatic stress disorder (PTSD). The central thesis put forward is that neurofeedback tunes brain oscillations toward a homeostatic set-point which maintains optimal network flexibility and stability (i.e. self-organized criticality).</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/9519008" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="13554eda480007f0b14d42c46540e409" rel="nofollow" data-download="{"attachment_id":36027844,"asset_id":9519008,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/36027844/download_file?st=MTczNzI5NjIxMyw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="217054" href="https://nsi.academia.edu/BBaars">Bernard J Baars</a><script data-card-contents-for-user="217054" type="text/json">{"id":217054,"first_name":"Bernard","last_name":"Baars","domain_name":"nsi","page_name":"BBaars","display_name":"Bernard J Baars","profile_url":"https://nsi.academia.edu/BBaars?f_ri=12950","photo":"https://0.academia-photos.com/217054/49443/45518/s65_bernard.baars.jpg"}</script></span></span><span class="u-displayInlineBlock InlineList-item-text"> and <span class="u-textDecorationUnderline u-clickable InlineList-item-text js-work-more-authors-9519008">+1</span><div class="hidden js-additional-users-9519008"><div><span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a href="https://unige.academia.edu/TomasRos">Tomas Ros</a></span></div></div></span><script>(function(){ var popoverSettings = { el: $('.js-work-more-authors-9519008'), placement: 'bottom', hide_delay: 200, html: true, content: function(){ return $('.js-additional-users-9519008').html(); 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However, despite a rise in empirical evidence attesting to its clinical benefits, a solid theoretical basis is still lacking on the manner in which neurofeedback is able to achieve these outcomes. The present work attempts to bring together various concepts from neurobiology, engineering, and dynamical systems so as to propose a contemporary theoretical framework for the mechanistic effects of neurofeedback. The objective is to provide a firmly neurophysiological account of neurofeedback, which goes beyond traditional behaviorist interpretations that attempt to explain psychological processes solely from a descriptive standpoint whilst treating the brain as a ‘black box’. To this end, we interlink evidence from experimental findings that encompass a broad range of intrinsic brain phenomena: starting from ‘bottom-up’ mechanisms of neural synchronization, followed by ‘top-down’ regulation of internal brain states, moving to dynamical systems plus control-theoretic principles, and concluding with activity-dependent as well as homeostatic forms of brain plasticity. In support of our framework, we examine the effects of neurofeedback in several brain disorders, including attention-deficit hyperactivity (ADHD) and post-traumatic stress disorder (PTSD). The central thesis put forward is that neurofeedback tunes brain oscillations toward a homeostatic set-point which maintains optimal network flexibility and stability (i.e. self-organized criticality).","downloadable_attachments":[{"id":36027844,"asset_id":9519008,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":217054,"first_name":"Bernard","last_name":"Baars","domain_name":"nsi","page_name":"BBaars","display_name":"Bernard J Baars","profile_url":"https://nsi.academia.edu/BBaars?f_ri=12950","photo":"https://0.academia-photos.com/217054/49443/45518/s65_bernard.baars.jpg"},{"id":2715801,"first_name":"Tomas","last_name":"Ros","domain_name":"unige","page_name":"TomasRos","display_name":"Tomas 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Neuropsychology","url":"https://www.academia.edu/Documents/in/Child_Clinical_Neuropsychology?f_ri=12950"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_35797963" data-work_id="35797963" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/35797963/Troubles_visuo_attentionnels_troubles_de_l_orientation_spatiale_et_de_l_attention_temporelle_dans_les_dyslexies_d%C3%A9veloppementales">Troubles visuo-attentionnels, troubles de l’orientation spatiale et de l’attention temporelle dans les dyslexies développementales</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">Malgré le caractère déterminant des déficits phonologiques pour l’émergence de la dyslexie chez l’enfant, d’autres hypothèses d’explication sont possibles concernant l’origine de ce trouble durable de l’apprentissage de la lecture. La... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_35797963" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">Malgré le caractère déterminant des déficits phonologiques pour l’émergence de la dyslexie chez l’enfant, d’autres hypothèses d’explication sont possibles concernant l’origine de ce trouble durable de l’apprentissage de la lecture. La diversité des profils de dyslexie invite à s’intéresser à une variété de déficits visuo-attentionnels susceptibles de participer à des formes de dyslexies dites de surface, mais aussi phonologique ou mixte. Nous proposons d’en préciser la nature, de comprendre leurs retentissements sur la lecture et de fournir des pistes pour les dépasser. Ces déficits attentionnels seront regroupés en trois rubriques : certains perturbent le système d’analyse visuelle précoce spécialisé pour l’orthographe en amont des procédures d’adressage et d’assemblage, d’autres concernent l’orientation spatiale de l’attention, d’autres relèvent enfin de l’attention temporelle. La présentation de travaux sur les liens entre l’attention et les oscillations cérébrales permettra d’évoquer les bases neuronales de mécanismes attentionnels spatio-temporels, fondamentaux pour la dynamique de la lecture et perturbés dans les dyslexies.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/35797963" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="11413d3d136b5228d7bfa4da7f1101a7" rel="nofollow" data-download="{"attachment_id":55674433,"asset_id":35797963,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/55674433/download_file?st=MTczNzI5NjIxMyw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="1189221" href="https://univ-lyon2.academia.edu/NathalieBedoin">Nathalie Bedoin</a><script data-card-contents-for-user="1189221" type="text/json">{"id":1189221,"first_name":"Nathalie","last_name":"Bedoin","domain_name":"univ-lyon2","page_name":"NathalieBedoin","display_name":"Nathalie Bedoin","profile_url":"https://univ-lyon2.academia.edu/NathalieBedoin?f_ri=12950","photo":"https://0.academia-photos.com/1189221/425572/525369/s65_nathalie.bedoin.jpg"}</script></span></span></li><li class="js-paper-rank-work_35797963 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="35797963"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 35797963, container: ".js-paper-rank-work_35797963", }); 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$(".js-view-count[data-work-id=35797963]").text(description); $(".js-view-count-work_35797963").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_35797963").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="35797963"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">10</a>  </div><span class="InlineList-item-text u-textTruncate u-pl10x"><a class="InlineList-item-text" data-has-card-for-ri="3886" rel="nofollow" href="https://www.academia.edu/Documents/in/Rhythm">Rhythm</a>, <script data-card-contents-for-ri="3886" type="text/json">{"id":3886,"name":"Rhythm","url":"https://www.academia.edu/Documents/in/Rhythm?f_ri=12950","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="9471" rel="nofollow" href="https://www.academia.edu/Documents/in/Reading">Reading</a>, <script data-card-contents-for-ri="9471" type="text/json">{"id":9471,"name":"Reading","url":"https://www.academia.edu/Documents/in/Reading?f_ri=12950","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="12950" rel="nofollow" href="https://www.academia.edu/Documents/in/Neural_Oscillations">Neural Oscillations</a>, <script data-card-contents-for-ri="12950" type="text/json">{"id":12950,"name":"Neural Oscillations","url":"https://www.academia.edu/Documents/in/Neural_Oscillations?f_ri=12950","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="25052" rel="nofollow" href="https://www.academia.edu/Documents/in/Dyslexia">Dyslexia</a><script data-card-contents-for-ri="25052" type="text/json">{"id":25052,"name":"Dyslexia","url":"https://www.academia.edu/Documents/in/Dyslexia?f_ri=12950","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=35797963]'), work: {"id":35797963,"title":"Troubles visuo-attentionnels, troubles de l’orientation spatiale et de l’attention temporelle dans les dyslexies développementales","created_at":"2018-01-30T12:32:48.564-08:00","url":"https://www.academia.edu/35797963/Troubles_visuo_attentionnels_troubles_de_l_orientation_spatiale_et_de_l_attention_temporelle_dans_les_dyslexies_d%C3%A9veloppementales?f_ri=12950","dom_id":"work_35797963","summary":"Malgré le caractère déterminant des déficits phonologiques pour l’émergence de la dyslexie chez l’enfant, d’autres hypothèses d’explication sont possibles concernant l’origine de ce trouble durable de l’apprentissage de la lecture. La diversité des profils de dyslexie invite à s’intéresser à une variété de déficits visuo-attentionnels susceptibles de participer à des formes de dyslexies dites de surface, mais aussi phonologique ou mixte. Nous proposons d’en préciser la nature, de comprendre leurs retentissements sur la lecture et de fournir des pistes pour les dépasser. Ces déficits attentionnels seront regroupés en trois rubriques : certains perturbent le système d’analyse visuelle précoce spécialisé pour l’orthographe en amont des procédures d’adressage et d’assemblage, d’autres concernent l’orientation spatiale de l’attention, d’autres relèvent enfin de l’attention temporelle. La présentation de travaux sur les liens entre l’attention et les oscillations cérébrales permettra d’évoquer les bases neuronales de mécanismes attentionnels spatio-temporels, fondamentaux pour la dynamique de la lecture et perturbés dans les dyslexies.","downloadable_attachments":[{"id":55674433,"asset_id":35797963,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":1189221,"first_name":"Nathalie","last_name":"Bedoin","domain_name":"univ-lyon2","page_name":"NathalieBedoin","display_name":"Nathalie Bedoin","profile_url":"https://univ-lyon2.academia.edu/NathalieBedoin?f_ri=12950","photo":"https://0.academia-photos.com/1189221/425572/525369/s65_nathalie.bedoin.jpg"}],"research_interests":[{"id":3886,"name":"Rhythm","url":"https://www.academia.edu/Documents/in/Rhythm?f_ri=12950","nofollow":true},{"id":9471,"name":"Reading","url":"https://www.academia.edu/Documents/in/Reading?f_ri=12950","nofollow":true},{"id":12950,"name":"Neural Oscillations","url":"https://www.academia.edu/Documents/in/Neural_Oscillations?f_ri=12950","nofollow":true},{"id":25052,"name":"Dyslexia","url":"https://www.academia.edu/Documents/in/Dyslexia?f_ri=12950","nofollow":true},{"id":31879,"name":"Developmental dyslexia","url":"https://www.academia.edu/Documents/in/Developmental_dyslexia?f_ri=12950"},{"id":95348,"name":"Visuospatial Attention","url":"https://www.academia.edu/Documents/in/Visuospatial_Attention?f_ri=12950"},{"id":172706,"name":"Temporal Attention","url":"https://www.academia.edu/Documents/in/Temporal_Attention?f_ri=12950"},{"id":174781,"name":"Oscillations","url":"https://www.academia.edu/Documents/in/Oscillations?f_ri=12950"},{"id":386558,"name":"Surface Dyslexia","url":"https://www.academia.edu/Documents/in/Surface_Dyslexia?f_ri=12950"},{"id":495132,"name":"Speech Therapy","url":"https://www.academia.edu/Documents/in/Speech_Therapy?f_ri=12950"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_67624022" data-work_id="67624022" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/67624022/Probing_of_brain_states_in_real_time_Introducing_the_ConSole_environment">Probing of brain states in real-time: Introducing the ConSole environment</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">Recent years have seen huge advancements in the methods available and used in neuroscience employing EEG or MEG. However, the standard approach is to average a large number of trials for experimentally defined conditions in order to... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_67624022" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">Recent years have seen huge advancements in the methods available and used in neuroscience employing EEG or MEG. However, the standard approach is to average a large number of trials for experimentally defined conditions in order to reduce intertrial-variability, i.e. treating it as a source of &quot;noise&quot;. Yet it is now more and more accepted that trial-to-trial fluctuations bear functional significance, reflecting fluctuations of &quot;brain states&quot; that predispose perception and action. Such effects are often revealed in a pre-stimulus period, when comparing response variability to an invariant stimulus. However such offline analyses are disadvantageous as they are correlational by drawing conclusions in a posthoc-manner and stimulus presentation is random with respect to the feature of interest. A more direct test is to trigger stimulus presentation when the relevant feature is present. The current paper introduces ConSole (CONstance System for OnLine Eeg), a software...</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/67624022" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="caee176c19b5e32fbeb5c1cbabd4af55" rel="nofollow" data-download="{"attachment_id":78373637,"asset_id":67624022,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/78373637/download_file?st=MTczNzI5NjIxMyw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="183590013" href="https://independent.academia.edu/ThomasYukiHartmann">Thomas Yuki Hartmann</a><script data-card-contents-for-user="183590013" type="text/json">{"id":183590013,"first_name":"Thomas","last_name":"Yuki Hartmann","domain_name":"independent","page_name":"ThomasYukiHartmann","display_name":"Thomas Yuki Hartmann","profile_url":"https://independent.academia.edu/ThomasYukiHartmann?f_ri=12950","photo":"/images/s65_no_pic.png"}</script></span></span></li><li class="js-paper-rank-work_67624022 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="67624022"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 67624022, container: ".js-paper-rank-work_67624022", }); });</script></li><li class="js-percentile-work_67624022 InlineList-item InlineList-item--bordered hidden u-tcGrayDark"><span class="percentile-widget hidden"><span class="u-mr2x percentile-widget" style="display: none">•</span><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 67624022; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-percentile-work_67624022"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></li><li class="js-view-count-work_67624022 InlineList-item InlineList-item--bordered hidden"><div><span><span class="js-view-count view-count u-mr2x" data-work-id="67624022"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 67624022; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=67624022]").text(description); $(".js-view-count-work_67624022").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_67624022").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="67624022"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">17</a>  </div><span class="InlineList-item-text u-textTruncate u-pl10x"><a class="InlineList-item-text" data-has-card-for-ri="161" rel="nofollow" href="https://www.academia.edu/Documents/in/Neuroscience">Neuroscience</a>, <script data-card-contents-for-ri="161" type="text/json">{"id":161,"name":"Neuroscience","url":"https://www.academia.edu/Documents/in/Neuroscience?f_ri=12950","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="221" rel="nofollow" href="https://www.academia.edu/Documents/in/Psychology">Psychology</a>, <script data-card-contents-for-ri="221" type="text/json">{"id":221,"name":"Psychology","url":"https://www.academia.edu/Documents/in/Psychology?f_ri=12950","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="251" rel="nofollow" href="https://www.academia.edu/Documents/in/Neuropsychology">Neuropsychology</a>, <script data-card-contents-for-ri="251" type="text/json">{"id":251,"name":"Neuropsychology","url":"https://www.academia.edu/Documents/in/Neuropsychology?f_ri=12950","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="422" rel="nofollow" href="https://www.academia.edu/Documents/in/Computer_Science">Computer Science</a><script data-card-contents-for-ri="422" type="text/json">{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science?f_ri=12950","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=67624022]'), work: {"id":67624022,"title":"Probing of brain states in real-time: Introducing the ConSole environment","created_at":"2022-01-08T09:14:01.966-08:00","url":"https://www.academia.edu/67624022/Probing_of_brain_states_in_real_time_Introducing_the_ConSole_environment?f_ri=12950","dom_id":"work_67624022","summary":"Recent years have seen huge advancements in the methods available and used in neuroscience employing EEG or MEG. However, the standard approach is to average a large number of trials for experimentally defined conditions in order to reduce intertrial-variability, i.e. treating it as a source of \u0026quot;noise\u0026quot;. Yet it is now more and more accepted that trial-to-trial fluctuations bear functional significance, reflecting fluctuations of \u0026quot;brain states\u0026quot; that predispose perception and action. Such effects are often revealed in a pre-stimulus period, when comparing response variability to an invariant stimulus. However such offline analyses are disadvantageous as they are correlational by drawing conclusions in a posthoc-manner and stimulus presentation is random with respect to the feature of interest. A more direct test is to trigger stimulus presentation when the relevant feature is present. The current paper introduces ConSole (CONstance System for OnLine Eeg), a software...","downloadable_attachments":[{"id":78373637,"asset_id":67624022,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":183590013,"first_name":"Thomas","last_name":"Yuki Hartmann","domain_name":"independent","page_name":"ThomasYukiHartmann","display_name":"Thomas Yuki Hartmann","profile_url":"https://independent.academia.edu/ThomasYukiHartmann?f_ri=12950","photo":"/images/s65_no_pic.png"}],"research_interests":[{"id":161,"name":"Neuroscience","url":"https://www.academia.edu/Documents/in/Neuroscience?f_ri=12950","nofollow":true},{"id":221,"name":"Psychology","url":"https://www.academia.edu/Documents/in/Psychology?f_ri=12950","nofollow":true},{"id":251,"name":"Neuropsychology","url":"https://www.academia.edu/Documents/in/Neuropsychology?f_ri=12950","nofollow":true},{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science?f_ri=12950","nofollow":true},{"id":1026,"name":"Psychophysiology","url":"https://www.academia.edu/Documents/in/Psychophysiology?f_ri=12950"},{"id":5356,"name":"Magnetoencephalography","url":"https://www.academia.edu/Documents/in/Magnetoencephalography?f_ri=12950"},{"id":9749,"name":"Neurofeedback","url":"https://www.academia.edu/Documents/in/Neurofeedback?f_ri=12950"},{"id":10402,"name":"EEG","url":"https://www.academia.edu/Documents/in/EEG?f_ri=12950"},{"id":12950,"name":"Neural Oscillations","url":"https://www.academia.edu/Documents/in/Neural_Oscillations?f_ri=12950"},{"id":21548,"name":"Cognitive Neuroscience","url":"https://www.academia.edu/Documents/in/Cognitive_Neuroscience?f_ri=12950"},{"id":26327,"name":"Medicine","url":"https://www.academia.edu/Documents/in/Medicine?f_ri=12950"},{"id":40187,"name":"Real Time EEG","url":"https://www.academia.edu/Documents/in/Real_Time_EEG?f_ri=12950"},{"id":42361,"name":"TMS","url":"https://www.academia.edu/Documents/in/TMS?f_ri=12950"},{"id":42362,"name":"Alpha Oscillations","url":"https://www.academia.edu/Documents/in/Alpha_Oscillations?f_ri=12950"},{"id":82957,"name":"MEG","url":"https://www.academia.edu/Documents/in/MEG?f_ri=12950"},{"id":1357280,"name":"Oscillation","url":"https://www.academia.edu/Documents/in/Oscillation?f_ri=12950"},{"id":2498386,"name":"Frontiers in Psychology","url":"https://www.academia.edu/Documents/in/Frontiers_in_Psychology?f_ri=12950"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_2096241" data-work_id="2096241" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/2096241/The_pairwise_phase_consistency_a_bias_free_measure_of_rhythmic_neuronal_synchronization">The pairwise phase consistency: a bias-free measure of rhythmic neuronal synchronization</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">Oscillatory activity is a widespread phenomenon in nervous systems and has been implicated in numerous functions. Signals that are generated by two separate neuronal sources often demonstrate a consistent phase-relationship in a... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_2096241" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">Oscillatory activity is a widespread phenomenon in nervous systems and has been implicated in numerous functions. Signals that are generated by two separate neuronal sources often demonstrate a consistent phase-relationship in a particular frequency-band, i.e., they demonstrate rhythmic neuronal synchronization. This consistency is conventionally measured by the PLV (phase-locking value) or the spectral coherence measure. Both statistical measures suffer from significant bias, in that their sample estimates overestimate the population statistics for finite sample sizes. This is a significant problem in the neurosciences where statistical comparisons are often made between conditions with a different number of trials or between neurons with a different number of spikes. We introduce a new circular statistic, the PPC (pairwise phase consistency). We demonstrate that the sample estimate of the PPC is a bias-free and consistent estimator of its corresponding population parameter. We show, both analytically and by means of numerical simulations, that the population statistic of the PPC is equivalent to the population statistic of the squared PLV. The variance and mean squared error of the PPC and PLV are compared. Finally, we demonstrate the practical relevance of the method in actual neuronal data recorded from the orbitofrontal cortex of rats that engage in a two-odour discrimination task. We find a strong increase in rhythmic synchronization of spikes relative to the local field potential (as measured by the PPC) for a wide range of low frequencies (including the theta-band) during the anticipation of sucrose delivery in comparison to the anticipation of quinine delivery.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/2096241" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="37cf78fbb7808e51c263e737753092e2" rel="nofollow" data-download="{"attachment_id":30099266,"asset_id":2096241,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/30099266/download_file?st=MTczNzI5NjIxMyw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="2731997" href="https://uni-duesseldorf.academia.edu/MarijnvanWingerden">Marijn van Wingerden</a><script data-card-contents-for-user="2731997" type="text/json">{"id":2731997,"first_name":"Marijn","last_name":"van Wingerden","domain_name":"uni-duesseldorf","page_name":"MarijnvanWingerden","display_name":"Marijn van Wingerden","profile_url":"https://uni-duesseldorf.academia.edu/MarijnvanWingerden?f_ri=12950","photo":"https://0.academia-photos.com/2731997/882460/11831019/s65_marijn.van_wingerden.jpg"}</script></span></span></li><li class="js-paper-rank-work_2096241 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="2096241"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 2096241, container: ".js-paper-rank-work_2096241", }); });</script></li><li class="js-percentile-work_2096241 InlineList-item InlineList-item--bordered hidden u-tcGrayDark"><span class="percentile-widget hidden"><span class="u-mr2x percentile-widget" style="display: none">•</span><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 2096241; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-percentile-work_2096241"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></li><li class="js-view-count-work_2096241 InlineList-item InlineList-item--bordered hidden"><div><span><span class="js-view-count view-count u-mr2x" data-work-id="2096241"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 2096241; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=2096241]").text(description); $(".js-view-count-work_2096241").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_2096241").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="2096241"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">4</a>  </div><span class="InlineList-item-text u-textTruncate u-pl9x"><a class="InlineList-item-text" data-has-card-for-ri="161" rel="nofollow" href="https://www.academia.edu/Documents/in/Neuroscience">Neuroscience</a>, <script data-card-contents-for-ri="161" type="text/json">{"id":161,"name":"Neuroscience","url":"https://www.academia.edu/Documents/in/Neuroscience?f_ri=12950","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="12950" rel="nofollow" href="https://www.academia.edu/Documents/in/Neural_Oscillations">Neural Oscillations</a>, <script data-card-contents-for-ri="12950" type="text/json">{"id":12950,"name":"Neural Oscillations","url":"https://www.academia.edu/Documents/in/Neural_Oscillations?f_ri=12950","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="274476" rel="nofollow" href="https://www.academia.edu/Documents/in/Phase_Locking">Phase Locking</a>, <script data-card-contents-for-ri="274476" type="text/json">{"id":274476,"name":"Phase Locking","url":"https://www.academia.edu/Documents/in/Phase_Locking?f_ri=12950","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="732917" rel="nofollow" href="https://www.academia.edu/Documents/in/Neural_Synchronization">Neural Synchronization</a><script data-card-contents-for-ri="732917" type="text/json">{"id":732917,"name":"Neural Synchronization","url":"https://www.academia.edu/Documents/in/Neural_Synchronization?f_ri=12950","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=2096241]'), work: {"id":2096241,"title":"The pairwise phase consistency: a bias-free measure of rhythmic neuronal synchronization","created_at":"2012-11-06T03:57:26.626-08:00","url":"https://www.academia.edu/2096241/The_pairwise_phase_consistency_a_bias_free_measure_of_rhythmic_neuronal_synchronization?f_ri=12950","dom_id":"work_2096241","summary":"Oscillatory activity is a widespread phenomenon in nervous systems and has been implicated in numerous functions. Signals that are generated by two separate neuronal sources often demonstrate a consistent phase-relationship in a particular frequency-band, i.e., they demonstrate rhythmic neuronal synchronization. This consistency is conventionally measured by the PLV (phase-locking value) or the spectral coherence measure. Both statistical measures suffer from significant bias, in that their sample estimates overestimate the population statistics for finite sample sizes. This is a significant problem in the neurosciences where statistical comparisons are often made between conditions with a different number of trials or between neurons with a different number of spikes. We introduce a new circular statistic, the PPC (pairwise phase consistency). We demonstrate that the sample estimate of the PPC is a bias-free and consistent estimator of its corresponding population parameter. We show, both analytically and by means of numerical simulations, that the population statistic of the PPC is equivalent to the population statistic of the squared PLV. The variance and mean squared error of the PPC and PLV are compared. Finally, we demonstrate the practical relevance of the method in actual neuronal data recorded from the orbitofrontal cortex of rats that engage in a two-odour discrimination task. We find a strong increase in rhythmic synchronization of spikes relative to the local field potential (as measured by the PPC) for a wide range of low frequencies (including the theta-band) during the anticipation of sucrose delivery in comparison to the anticipation of quinine delivery.","downloadable_attachments":[{"id":30099266,"asset_id":2096241,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":2731997,"first_name":"Marijn","last_name":"van Wingerden","domain_name":"uni-duesseldorf","page_name":"MarijnvanWingerden","display_name":"Marijn van Wingerden","profile_url":"https://uni-duesseldorf.academia.edu/MarijnvanWingerden?f_ri=12950","photo":"https://0.academia-photos.com/2731997/882460/11831019/s65_marijn.van_wingerden.jpg"}],"research_interests":[{"id":161,"name":"Neuroscience","url":"https://www.academia.edu/Documents/in/Neuroscience?f_ri=12950","nofollow":true},{"id":12950,"name":"Neural Oscillations","url":"https://www.academia.edu/Documents/in/Neural_Oscillations?f_ri=12950","nofollow":true},{"id":274476,"name":"Phase Locking","url":"https://www.academia.edu/Documents/in/Phase_Locking?f_ri=12950","nofollow":true},{"id":732917,"name":"Neural Synchronization","url":"https://www.academia.edu/Documents/in/Neural_Synchronization?f_ri=12950","nofollow":true}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_37979693" data-work_id="37979693" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/37979693/Interfaces_traveling_oscillations_Recursion_delta_theta_code_Language">Interfaces (traveling oscillations) + Recursion (delta-theta code) = Language</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">Formulating a minimalist model for language, Gärtner and Sauerland (2007) collected a series of papers exploring the possibility that the recursive gen-erative component plus the conceptual and articulatory interfaces provide the... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_37979693" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">Formulating a minimalist model for language, Gärtner and Sauerland (2007) collected a series of papers exploring the possibility that the recursive gen-erative component plus the conceptual and articulatory interfaces provide the essential components of the system. This was summarized as 'Interfaces + Re-cursion = Language'. Over the past decennium a range of linking hypotheses have been drawn up to better ground this architecture within the brain. In the realms of cognitive and systems neuroscience, the search for the neural code across a number of domains has seen a marked transition from the analysis of individual spike timings to larger patterns of synchronization. This chapter argues that the language sciences should embrace these systems-level developments, with recent findings concerning the scope of possible oscillatory synchronization in the human brain revealing the existence of traveling/migrating oscillations, adding further impetus to reject the typical stasis found in cartographic neurolinguistics models. After exploring empirically-motivated revisions to the neural code for hierarchical phrase structure, it is discussed how this code could provide a new perspective on language disorders, fluid intelligence and language acquisition.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/37979693" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="4bd5ad1ca4765510981a0164a622d85e" rel="nofollow" data-download="{"attachment_id":57997130,"asset_id":37979693,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/57997130/download_file?st=MTczNzI5NjIxMyw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="1267216" href="https://tmc.academia.edu/ElliotMurphy">Elliot Murphy</a><script data-card-contents-for-user="1267216" type="text/json">{"id":1267216,"first_name":"Elliot","last_name":"Murphy","domain_name":"tmc","page_name":"ElliotMurphy","display_name":"Elliot Murphy","profile_url":"https://tmc.academia.edu/ElliotMurphy?f_ri=12950","photo":"https://0.academia-photos.com/1267216/763325/39030425/s65_elliot.murphy.jpeg"}</script></span></span></li><li class="js-paper-rank-work_37979693 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="37979693"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 37979693, container: ".js-paper-rank-work_37979693", }); });</script></li><li class="js-percentile-work_37979693 InlineList-item InlineList-item--bordered hidden u-tcGrayDark"><span class="percentile-widget hidden"><span class="u-mr2x percentile-widget" style="display: none">•</span><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 37979693; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-percentile-work_37979693"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></li><li class="js-view-count-work_37979693 InlineList-item InlineList-item--bordered hidden"><div><span><span class="js-view-count view-count u-mr2x" data-work-id="37979693"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 37979693; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=37979693]").text(description); $(".js-view-count-work_37979693").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_37979693").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="37979693"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">12</a>  </div><span class="InlineList-item-text u-textTruncate u-pl10x"><a class="InlineList-item-text" data-has-card-for-ri="155" rel="nofollow" href="https://www.academia.edu/Documents/in/Evolutionary_Biology">Evolutionary Biology</a>, <script data-card-contents-for-ri="155" type="text/json">{"id":155,"name":"Evolutionary Biology","url":"https://www.academia.edu/Documents/in/Evolutionary_Biology?f_ri=12950","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="161" rel="nofollow" href="https://www.academia.edu/Documents/in/Neuroscience">Neuroscience</a>, <script data-card-contents-for-ri="161" type="text/json">{"id":161,"name":"Neuroscience","url":"https://www.academia.edu/Documents/in/Neuroscience?f_ri=12950","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="498" rel="nofollow" href="https://www.academia.edu/Documents/in/Physics">Physics</a>, <script data-card-contents-for-ri="498" type="text/json">{"id":498,"name":"Physics","url":"https://www.academia.edu/Documents/in/Physics?f_ri=12950","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="1200" rel="nofollow" href="https://www.academia.edu/Documents/in/Languages_and_Linguistics">Languages and Linguistics</a><script data-card-contents-for-ri="1200" type="text/json">{"id":1200,"name":"Languages and Linguistics","url":"https://www.academia.edu/Documents/in/Languages_and_Linguistics?f_ri=12950","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=37979693]'), work: {"id":37979693,"title":"Interfaces (traveling oscillations) + Recursion (delta-theta code) = Language","created_at":"2018-12-14T13:08:02.822-08:00","url":"https://www.academia.edu/37979693/Interfaces_traveling_oscillations_Recursion_delta_theta_code_Language?f_ri=12950","dom_id":"work_37979693","summary":"Formulating a minimalist model for language, Gärtner and Sauerland (2007) collected a series of papers exploring the possibility that the recursive gen-erative component plus the conceptual and articulatory interfaces provide the essential components of the system. This was summarized as 'Interfaces + Re-cursion = Language'. Over the past decennium a range of linking hypotheses have been drawn up to better ground this architecture within the brain. In the realms of cognitive and systems neuroscience, the search for the neural code across a number of domains has seen a marked transition from the analysis of individual spike timings to larger patterns of synchronization. This chapter argues that the language sciences should embrace these systems-level developments, with recent findings concerning the scope of possible oscillatory synchronization in the human brain revealing the existence of traveling/migrating oscillations, adding further impetus to reject the typical stasis found in cartographic neurolinguistics models. After exploring empirically-motivated revisions to the neural code for hierarchical phrase structure, it is discussed how this code could provide a new perspective on language disorders, fluid intelligence and language acquisition.","downloadable_attachments":[{"id":57997130,"asset_id":37979693,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":1267216,"first_name":"Elliot","last_name":"Murphy","domain_name":"tmc","page_name":"ElliotMurphy","display_name":"Elliot Murphy","profile_url":"https://tmc.academia.edu/ElliotMurphy?f_ri=12950","photo":"https://0.academia-photos.com/1267216/763325/39030425/s65_elliot.murphy.jpeg"}],"research_interests":[{"id":155,"name":"Evolutionary Biology","url":"https://www.academia.edu/Documents/in/Evolutionary_Biology?f_ri=12950","nofollow":true},{"id":161,"name":"Neuroscience","url":"https://www.academia.edu/Documents/in/Neuroscience?f_ri=12950","nofollow":true},{"id":498,"name":"Physics","url":"https://www.academia.edu/Documents/in/Physics?f_ri=12950","nofollow":true},{"id":1200,"name":"Languages and Linguistics","url":"https://www.academia.edu/Documents/in/Languages_and_Linguistics?f_ri=12950","nofollow":true},{"id":7710,"name":"Biology","url":"https://www.academia.edu/Documents/in/Biology?f_ri=12950"},{"id":11103,"name":"Neurobiology of Learning and Memory","url":"https://www.academia.edu/Documents/in/Neurobiology_of_Learning_and_Memory?f_ri=12950"},{"id":11598,"name":"Neural Networks","url":"https://www.academia.edu/Documents/in/Neural_Networks?f_ri=12950"},{"id":12950,"name":"Neural Oscillations","url":"https://www.academia.edu/Documents/in/Neural_Oscillations?f_ri=12950"},{"id":15674,"name":"Linguistics","url":"https://www.academia.edu/Documents/in/Linguistics?f_ri=12950"},{"id":25804,"name":"Neurobiology","url":"https://www.academia.edu/Documents/in/Neurobiology?f_ri=12950"},{"id":113158,"name":"Neural Dynamics","url":"https://www.academia.edu/Documents/in/Neural_Dynamics?f_ri=12950"},{"id":174781,"name":"Oscillations","url":"https://www.academia.edu/Documents/in/Oscillations?f_ri=12950"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_40039352" data-work_id="40039352" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/40039352/The_Role_of_Neuronal_Oscillations_in_Visual_Active_Sensing">The Role of Neuronal Oscillations in Visual Active Sensing</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">Visual perception is most often studied as a "passive" process in which an observer fixates steadily at point in space so that stimuli can be delivered to the system with spatial precision. Analysis of neuronal signals related to vision... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_40039352" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">Visual perception is most often studied as a "passive" process in which an observer fixates steadily at point in space so that stimuli can be delivered to the system with spatial precision. Analysis of neuronal signals related to vision is generally keyed to stimulus onset, stimulus movement, etc.; i.e., events external to the observer. In natural "active" vision, however, information is systematically acquired by using eye movements including rapid (saccadic) eye movements, as well as smooth ocular pursuit of moving objects and slower drifts. Here we consider the use of alternating saccades and fixations to gather information from a visual scene. The underlying motor sampling plan contains highly reliable information regarding "where" and "when" the eyes will land, this information can be used predictively to modify firing properties of neurons precisely at the time when this "contextual" information is most useful-when a volley of retinal input enters the system at the onset of each fixation. Analyses focusing on neural events leading to and resulting from shifts in fixation, as well as visual events external to the observer, can provide a more complete and mechanistic understanding of visual information processing. Studies thus far suggest that active vision may be a fundamentally different from that process we usually study with more traditional passive viewing paradigms. In this Perspective we note that active saccadic sampling behavior imposes robust temporal patterning on the activity of neuron ensembles and large-scale neural dynamics throughout the brain's visual pathways whose mechanistic effects on information processing are not yet fully understood. The spatio-temporal sequence of eye movements elicits a succession of temporally predictable quasi-rhythmic sensory inputs, whose encoding is enhanced by entrainment of low frequency oscillations to the rate of eye movements. Review of the pertinent findings underscores the fact that temporal coordination between motor and visual cortices is critical for understanding neural dynamics of active vision and posits that phase entrainment of neuronal oscillations plays a mechanistic role in this process.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/40039352" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="1dc4e8bf6f08ded79cd7764ff20d84fe" rel="nofollow" data-download="{"attachment_id":60237110,"asset_id":40039352,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/60237110/download_file?st=MTczNzI5NjIxMyw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="1245376" href="https://columbia.academia.edu/MarcinLeszczynski">Marcin Leszczynski</a><script data-card-contents-for-user="1245376" type="text/json">{"id":1245376,"first_name":"Marcin","last_name":"Leszczynski","domain_name":"columbia","page_name":"MarcinLeszczynski","display_name":"Marcin Leszczynski","profile_url":"https://columbia.academia.edu/MarcinLeszczynski?f_ri=12950","photo":"/images/s65_no_pic.png"}</script></span></span></li><li class="js-paper-rank-work_40039352 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="40039352"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 40039352, container: ".js-paper-rank-work_40039352", }); });</script></li><li class="js-percentile-work_40039352 InlineList-item InlineList-item--bordered hidden u-tcGrayDark"><span class="percentile-widget hidden"><span class="u-mr2x percentile-widget" style="display: none">•</span><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 40039352; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-percentile-work_40039352"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></li><li class="js-view-count-work_40039352 InlineList-item InlineList-item--bordered hidden"><div><span><span class="js-view-count view-count u-mr2x" data-work-id="40039352"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 40039352; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=40039352]").text(description); $(".js-view-count-work_40039352").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_40039352").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="40039352"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">13</a>  </div><span class="InlineList-item-text u-textTruncate u-pl10x"><a class="InlineList-item-text" data-has-card-for-ri="161" rel="nofollow" href="https://www.academia.edu/Documents/in/Neuroscience">Neuroscience</a>, <script data-card-contents-for-ri="161" type="text/json">{"id":161,"name":"Neuroscience","url":"https://www.academia.edu/Documents/in/Neuroscience?f_ri=12950","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="221" rel="nofollow" href="https://www.academia.edu/Documents/in/Psychology">Psychology</a>, <script data-card-contents-for-ri="221" type="text/json">{"id":221,"name":"Psychology","url":"https://www.academia.edu/Documents/in/Psychology?f_ri=12950","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="237" rel="nofollow" href="https://www.academia.edu/Documents/in/Cognitive_Science">Cognitive Science</a>, <script data-card-contents-for-ri="237" type="text/json">{"id":237,"name":"Cognitive Science","url":"https://www.academia.edu/Documents/in/Cognitive_Science?f_ri=12950","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="623" rel="nofollow" href="https://www.academia.edu/Documents/in/Neurology">Neurology</a><script data-card-contents-for-ri="623" type="text/json">{"id":623,"name":"Neurology","url":"https://www.academia.edu/Documents/in/Neurology?f_ri=12950","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=40039352]'), work: {"id":40039352,"title":"The Role of Neuronal Oscillations in Visual Active Sensing","created_at":"2019-08-08T08:09:26.854-07:00","url":"https://www.academia.edu/40039352/The_Role_of_Neuronal_Oscillations_in_Visual_Active_Sensing?f_ri=12950","dom_id":"work_40039352","summary":"Visual perception is most often studied as a \"passive\" process in which an observer fixates steadily at point in space so that stimuli can be delivered to the system with spatial precision. Analysis of neuronal signals related to vision is generally keyed to stimulus onset, stimulus movement, etc.; i.e., events external to the observer. In natural \"active\" vision, however, information is systematically acquired by using eye movements including rapid (saccadic) eye movements, as well as smooth ocular pursuit of moving objects and slower drifts. Here we consider the use of alternating saccades and fixations to gather information from a visual scene. The underlying motor sampling plan contains highly reliable information regarding \"where\" and \"when\" the eyes will land, this information can be used predictively to modify firing properties of neurons precisely at the time when this \"contextual\" information is most useful-when a volley of retinal input enters the system at the onset of each fixation. Analyses focusing on neural events leading to and resulting from shifts in fixation, as well as visual events external to the observer, can provide a more complete and mechanistic understanding of visual information processing. Studies thus far suggest that active vision may be a fundamentally different from that process we usually study with more traditional passive viewing paradigms. In this Perspective we note that active saccadic sampling behavior imposes robust temporal patterning on the activity of neuron ensembles and large-scale neural dynamics throughout the brain's visual pathways whose mechanistic effects on information processing are not yet fully understood. The spatio-temporal sequence of eye movements elicits a succession of temporally predictable quasi-rhythmic sensory inputs, whose encoding is enhanced by entrainment of low frequency oscillations to the rate of eye movements. Review of the pertinent findings underscores the fact that temporal coordination between motor and visual cortices is critical for understanding neural dynamics of active vision and posits that phase entrainment of neuronal oscillations plays a mechanistic role in this process.","downloadable_attachments":[{"id":60237110,"asset_id":40039352,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":1245376,"first_name":"Marcin","last_name":"Leszczynski","domain_name":"columbia","page_name":"MarcinLeszczynski","display_name":"Marcin Leszczynski","profile_url":"https://columbia.academia.edu/MarcinLeszczynski?f_ri=12950","photo":"/images/s65_no_pic.png"}],"research_interests":[{"id":161,"name":"Neuroscience","url":"https://www.academia.edu/Documents/in/Neuroscience?f_ri=12950","nofollow":true},{"id":221,"name":"Psychology","url":"https://www.academia.edu/Documents/in/Psychology?f_ri=12950","nofollow":true},{"id":237,"name":"Cognitive Science","url":"https://www.academia.edu/Documents/in/Cognitive_Science?f_ri=12950","nofollow":true},{"id":623,"name":"Neurology","url":"https://www.academia.edu/Documents/in/Neurology?f_ri=12950","nofollow":true},{"id":867,"name":"Perception","url":"https://www.academia.edu/Documents/in/Perception?f_ri=12950"},{"id":1755,"name":"Eye tracking","url":"https://www.academia.edu/Documents/in/Eye_tracking?f_ri=12950"},{"id":2354,"name":"Eye Tracking and Oculomotor Control","url":"https://www.academia.edu/Documents/in/Eye_Tracking_and_Oculomotor_Control?f_ri=12950"},{"id":2639,"name":"Neuroimaging","url":"https://www.academia.edu/Documents/in/Neuroimaging?f_ri=12950"},{"id":4212,"name":"Cognition","url":"https://www.academia.edu/Documents/in/Cognition?f_ri=12950"},{"id":4420,"name":"Embodied Cognition","url":"https://www.academia.edu/Documents/in/Embodied_Cognition?f_ri=12950"},{"id":12950,"name":"Neural Oscillations","url":"https://www.academia.edu/Documents/in/Neural_Oscillations?f_ri=12950"},{"id":40247,"name":"Sensation and Perception","url":"https://www.academia.edu/Documents/in/Sensation_and_Perception?f_ri=12950"},{"id":61474,"name":"Brain","url":"https://www.academia.edu/Documents/in/Brain?f_ri=12950"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_75383924" data-work_id="75383924" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/75383924/The_Phonetics_Phonology_Relationship_in_the_Neurobiology_of_Language">The Phonetics-Phonology Relationship in the Neurobiology of Language</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">In this work, I address the connection of phonetic structure with phonological representations. This classical issue is discussed in the light of recent neurophysiological data which – thanks to direct measurements of temporal and spatial... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_75383924" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">In this work, I address the connection of phonetic structure with phonological representations. This classical issue is discussed in the light of recent neurophysiological data which – thanks to direct measurements of temporal and spatial brain activation – provide new avenues to investigate the biological substrate of human language. After describing principal techniques and methods, I critically discuss magnetoencephalographic and electroencephalographic findings of speech processing based on event-related potentials and event-related oscillatory rhythms. The available data do not permit us to clearly disambiguate between neural evidence suggesting pure acoustic patterns and those indicating abstract phonological features. Starting from this evidence, which only at the surface represents a limit, I develop a preliminary proposal where discretization and phonological abstraction are the result of a continuous process that converts spectro-temporal (acoustic) states into neurophysio...</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/75383924" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="54376899971225bdf5f6c34ca22fa916" rel="nofollow" data-download="{"attachment_id":83174143,"asset_id":75383924,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/83174143/download_file?st=MTczNzI5NjIxMyw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="7047094" href="https://unisalento.academia.edu/MGrimaldi">Mirko Grimaldi</a><script data-card-contents-for-user="7047094" type="text/json">{"id":7047094,"first_name":"Mirko","last_name":"Grimaldi","domain_name":"unisalento","page_name":"MGrimaldi","display_name":"Mirko Grimaldi","profile_url":"https://unisalento.academia.edu/MGrimaldi?f_ri=12950","photo":"https://0.academia-photos.com/7047094/4101595/18107465/s65_mirko.grimaldi.png"}</script></span></span></li><li class="js-paper-rank-work_75383924 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="75383924"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 75383924, container: ".js-paper-rank-work_75383924", }); });</script></li><li class="js-percentile-work_75383924 InlineList-item InlineList-item--bordered hidden u-tcGrayDark"><span class="percentile-widget hidden"><span class="u-mr2x percentile-widget" style="display: none">•</span><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 75383924; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-percentile-work_75383924"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></li><li class="js-view-count-work_75383924 InlineList-item InlineList-item--bordered hidden"><div><span><span class="js-view-count view-count u-mr2x" data-work-id="75383924"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 75383924; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=75383924]").text(description); $(".js-view-count-work_75383924").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_75383924").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="75383924"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">5</a>  </div><span class="InlineList-item-text u-textTruncate u-pl9x"><a class="InlineList-item-text" data-has-card-for-ri="12950" rel="nofollow" href="https://www.academia.edu/Documents/in/Neural_Oscillations">Neural Oscillations</a>, <script data-card-contents-for-ri="12950" type="text/json">{"id":12950,"name":"Neural Oscillations","url":"https://www.academia.edu/Documents/in/Neural_Oscillations?f_ri=12950","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="60338" rel="nofollow" href="https://www.academia.edu/Documents/in/Phonetics_and_Phonology">Phonetics and Phonology</a>, <script data-card-contents-for-ri="60338" type="text/json">{"id":60338,"name":"Phonetics and Phonology","url":"https://www.academia.edu/Documents/in/Phonetics_and_Phonology?f_ri=12950","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="150884" rel="nofollow" href="https://www.academia.edu/Documents/in/Event_Related_Potentials">Event Related Potentials</a>, <script data-card-contents-for-ri="150884" type="text/json">{"id":150884,"name":"Event Related Potentials","url":"https://www.academia.edu/Documents/in/Event_Related_Potentials?f_ri=12950","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="174781" rel="nofollow" href="https://www.academia.edu/Documents/in/Oscillations">Oscillations</a><script data-card-contents-for-ri="174781" type="text/json">{"id":174781,"name":"Oscillations","url":"https://www.academia.edu/Documents/in/Oscillations?f_ri=12950","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=75383924]'), work: {"id":75383924,"title":"The Phonetics-Phonology Relationship in the Neurobiology of Language","created_at":"2022-04-03T23:53:00.228-07:00","url":"https://www.academia.edu/75383924/The_Phonetics_Phonology_Relationship_in_the_Neurobiology_of_Language?f_ri=12950","dom_id":"work_75383924","summary":"In this work, I address the connection of phonetic structure with phonological representations. This classical issue is discussed in the light of recent neurophysiological data which – thanks to direct measurements of temporal and spatial brain activation – provide new avenues to investigate the biological substrate of human language. After describing principal techniques and methods, I critically discuss magnetoencephalographic and electroencephalographic findings of speech processing based on event-related potentials and event-related oscillatory rhythms. The available data do not permit us to clearly disambiguate between neural evidence suggesting pure acoustic patterns and those indicating abstract phonological features. Starting from this evidence, which only at the surface represents a limit, I develop a preliminary proposal where discretization and phonological abstraction are the result of a continuous process that converts spectro-temporal (acoustic) states into neurophysio...","downloadable_attachments":[{"id":83174143,"asset_id":75383924,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":7047094,"first_name":"Mirko","last_name":"Grimaldi","domain_name":"unisalento","page_name":"MGrimaldi","display_name":"Mirko Grimaldi","profile_url":"https://unisalento.academia.edu/MGrimaldi?f_ri=12950","photo":"https://0.academia-photos.com/7047094/4101595/18107465/s65_mirko.grimaldi.png"}],"research_interests":[{"id":12950,"name":"Neural Oscillations","url":"https://www.academia.edu/Documents/in/Neural_Oscillations?f_ri=12950","nofollow":true},{"id":60338,"name":"Phonetics and Phonology","url":"https://www.academia.edu/Documents/in/Phonetics_and_Phonology?f_ri=12950","nofollow":true},{"id":150884,"name":"Event Related Potentials","url":"https://www.academia.edu/Documents/in/Event_Related_Potentials?f_ri=12950","nofollow":true},{"id":174781,"name":"Oscillations","url":"https://www.academia.edu/Documents/in/Oscillations?f_ri=12950","nofollow":true},{"id":383381,"name":"Distinctive Features","url":"https://www.academia.edu/Documents/in/Distinctive_Features?f_ri=12950"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_20083868" data-work_id="20083868" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/20083868/Meditations_on_Consciousness">Meditations on Consciousness</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">This paper explores the role that "information" may play in a new theory of consciousness. Through the convergence of physics and neuroscience, I will argue that information is the monistic "common denominator" through which the... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_20083868" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">This paper explores the role that "information" may play in a new theory of consciousness.  Through the convergence of physics and neuroscience, I will argue that information is the monistic "common denominator" through which the Cartesian mind-body problem may be resolved.  The paper begins with a speculative quantum mechanical model that potentially provides the information reservoir the philosopher David Chalmers believes a "fundamental theory of consciousness" requires.  The paper next examines the model's underlying assumptions and provides supporting arguments based on Black Hole cosmology and Reversible Computers.  In the following "meditations," the model is restated, first as modes of thought and next in the context of the Hindu Aum (or Om).  Finally, the paper briefly explores the model's implications in our lives, including how prayer and intentionality may alter the probabilities of what occurs in our lives.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/20083868" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="823e5b98263a642cc7a5d557a1b5ae65" rel="nofollow" data-download="{"attachment_id":40997233,"asset_id":20083868,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/40997233/download_file?st=MTczNzI5NjIxMyw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="1851885" href="https://gmu.academia.edu/JonTrevathan">Jon Trevathan</a><script data-card-contents-for-user="1851885" type="text/json">{"id":1851885,"first_name":"Jon","last_name":"Trevathan","domain_name":"gmu","page_name":"JonTrevathan","display_name":"Jon Trevathan","profile_url":"https://gmu.academia.edu/JonTrevathan?f_ri=12950","photo":"https://0.academia-photos.com/1851885/5908973/20925177/s65_jon.trevathan.jpg"}</script></span></span></li><li class="js-paper-rank-work_20083868 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="20083868"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 20083868, container: ".js-paper-rank-work_20083868", }); });</script></li><li class="js-percentile-work_20083868 InlineList-item InlineList-item--bordered hidden u-tcGrayDark"><span class="percentile-widget hidden"><span class="u-mr2x percentile-widget" style="display: none">•</span><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 20083868; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-percentile-work_20083868"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></li><li class="js-view-count-work_20083868 InlineList-item InlineList-item--bordered hidden"><div><span><span class="js-view-count view-count u-mr2x" data-work-id="20083868"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 20083868; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=20083868]").text(description); $(".js-view-count-work_20083868").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_20083868").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="20083868"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">11</a>  </div><span class="InlineList-item-text u-textTruncate u-pl10x"><a class="InlineList-item-text" data-has-card-for-ri="806" rel="nofollow" href="https://www.academia.edu/Documents/in/Philosophy_of_Mind">Philosophy of Mind</a>, <script data-card-contents-for-ri="806" type="text/json">{"id":806,"name":"Philosophy of Mind","url":"https://www.academia.edu/Documents/in/Philosophy_of_Mind?f_ri=12950","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="3344" rel="nofollow" href="https://www.academia.edu/Documents/in/Metaphysics_of_Consciousness">Metaphysics of Consciousness</a>, <script data-card-contents-for-ri="3344" type="text/json">{"id":3344,"name":"Metaphysics of Consciousness","url":"https://www.academia.edu/Documents/in/Metaphysics_of_Consciousness?f_ri=12950","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="4937" rel="nofollow" href="https://www.academia.edu/Documents/in/Theory_of_Mind">Theory of Mind</a>, <script data-card-contents-for-ri="4937" type="text/json">{"id":4937,"name":"Theory of Mind","url":"https://www.academia.edu/Documents/in/Theory_of_Mind?f_ri=12950","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="5419" rel="nofollow" href="https://www.academia.edu/Documents/in/Metaphysics_of_Time">Metaphysics of Time</a><script data-card-contents-for-ri="5419" type="text/json">{"id":5419,"name":"Metaphysics of Time","url":"https://www.academia.edu/Documents/in/Metaphysics_of_Time?f_ri=12950","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=20083868]'), work: {"id":20083868,"title":"Meditations on Consciousness","created_at":"2016-01-07T09:25:06.011-08:00","url":"https://www.academia.edu/20083868/Meditations_on_Consciousness?f_ri=12950","dom_id":"work_20083868","summary":"This paper explores the role that \"information\" may play in a new theory of consciousness. Through the convergence of physics and neuroscience, I will argue that information is the monistic \"common denominator\" through which the Cartesian mind-body problem may be resolved. The paper begins with a speculative quantum mechanical model that potentially provides the information reservoir the philosopher David Chalmers believes a \"fundamental theory of consciousness\" requires. The paper next examines the model's underlying assumptions and provides supporting arguments based on Black Hole cosmology and Reversible Computers. In the following \"meditations,\" the model is restated, first as modes of thought and next in the context of the Hindu Aum (or Om). Finally, the paper briefly explores the model's implications in our lives, including how prayer and intentionality may alter the probabilities of what occurs in our lives.\r\n","downloadable_attachments":[{"id":40997233,"asset_id":20083868,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":1851885,"first_name":"Jon","last_name":"Trevathan","domain_name":"gmu","page_name":"JonTrevathan","display_name":"Jon Trevathan","profile_url":"https://gmu.academia.edu/JonTrevathan?f_ri=12950","photo":"https://0.academia-photos.com/1851885/5908973/20925177/s65_jon.trevathan.jpg"}],"research_interests":[{"id":806,"name":"Philosophy of Mind","url":"https://www.academia.edu/Documents/in/Philosophy_of_Mind?f_ri=12950","nofollow":true},{"id":3344,"name":"Metaphysics of Consciousness","url":"https://www.academia.edu/Documents/in/Metaphysics_of_Consciousness?f_ri=12950","nofollow":true},{"id":4937,"name":"Theory of Mind","url":"https://www.academia.edu/Documents/in/Theory_of_Mind?f_ri=12950","nofollow":true},{"id":5419,"name":"Metaphysics of Time","url":"https://www.academia.edu/Documents/in/Metaphysics_of_Time?f_ri=12950","nofollow":true},{"id":9040,"name":"Consciousness","url":"https://www.academia.edu/Documents/in/Consciousness?f_ri=12950"},{"id":10148,"name":"Metaphysics of Mind","url":"https://www.academia.edu/Documents/in/Metaphysics_of_Mind?f_ri=12950"},{"id":12950,"name":"Neural Oscillations","url":"https://www.academia.edu/Documents/in/Neural_Oscillations?f_ri=12950"},{"id":14989,"name":"Philosophy of Time","url":"https://www.academia.edu/Documents/in/Philosophy_of_Time?f_ri=12950"},{"id":111330,"name":"Philosophy of Mind (the hard problem of consciousness)","url":"https://www.academia.edu/Documents/in/Philosophy_of_Mind_the_hard_problem_of_consciousness_?f_ri=12950"},{"id":129332,"name":"Precognition","url":"https://www.academia.edu/Documents/in/Precognition?f_ri=12950"},{"id":339710,"name":"David Chalmers","url":"https://www.academia.edu/Documents/in/David_Chalmers?f_ri=12950"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_10834831" data-work_id="10834831" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/10834831/Hypothesis_driven_methods_to_augment_human_cognition_by_optimizing_cortical_oscillations">Hypothesis-driven methods to augment human cognition by optimizing cortical oscillations</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">Cortical oscillations have been shown to represent fundamental functions of a working brain, e.g., communication, stimulus binding, error monitoring, and inhibition, and are directly linked to behavior. Recent studies intervening with... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_10834831" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">Cortical oscillations have been shown to represent fundamental functions of a working brain, e.g., communication, stimulus binding, error monitoring, and inhibition, and are directly linked to behavior. Recent studies intervening with these oscillations have demonstrated effective modulation of both the oscillations and behavior. In this review, we collect evidence in favor of how hypothesis-driven methods can be used to augment cognition by optimizing cortical oscillations. We elaborate their potential usefulness for three target groups: healthy elderly, patients with attention deficit/hyperactivity disorder, and healthy young adults. We discuss the relevance of neuronal oscillations in each group and show how each of them can benefit from the manipulation of functionally-related oscillations. Further, we describe methods for manipulation of neuronal oscillations including direct brain stimulation as well as indirect task alterations. We also discuss practical considerations about the proposed techniques. In conclusion, we propose that insights from neuroscience should guide techniques to augment human cognition, which in turn can provide a better understanding of how the human brain works.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/10834831" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="29bddb8705e745e610f6d2d9c5498581" rel="nofollow" data-download="{"attachment_id":36654375,"asset_id":10834831,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/36654375/download_file?st=MTczNzI5NjIxMyw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="26333879" href="https://radboud.academia.edu/HorschigJ%C3%B6rnM">Horschig Jörn M.</a><script data-card-contents-for-user="26333879" type="text/json">{"id":26333879,"first_name":"Horschig","last_name":"Jörn M.","domain_name":"radboud","page_name":"HorschigJörnM","display_name":"Horschig Jörn M.","profile_url":"https://radboud.academia.edu/HorschigJ%C3%B6rnM?f_ri=12950","photo":"/images/s65_no_pic.png"}</script></span></span></li><li class="js-paper-rank-work_10834831 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="10834831"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 10834831, container: ".js-paper-rank-work_10834831", }); });</script></li><li class="js-percentile-work_10834831 InlineList-item InlineList-item--bordered hidden u-tcGrayDark"><span class="percentile-widget hidden"><span class="u-mr2x percentile-widget" style="display: none">•</span><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 10834831; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-percentile-work_10834831"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></li><li class="js-view-count-work_10834831 InlineList-item InlineList-item--bordered hidden"><div><span><span class="js-view-count view-count u-mr2x" data-work-id="10834831"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 10834831; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=10834831]").text(description); $(".js-view-count-work_10834831").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_10834831").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="10834831"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">7</a>  </div><span class="InlineList-item-text u-textTruncate u-pl9x"><a class="InlineList-item-text" data-has-card-for-ri="4624" rel="nofollow" href="https://www.academia.edu/Documents/in/Brain-computer_interfaces">Brain-computer interfaces</a>, <script data-card-contents-for-ri="4624" type="text/json">{"id":4624,"name":"Brain-computer interfaces","url":"https://www.academia.edu/Documents/in/Brain-computer_interfaces?f_ri=12950","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="9749" rel="nofollow" href="https://www.academia.edu/Documents/in/Neurofeedback">Neurofeedback</a>, <script data-card-contents-for-ri="9749" type="text/json">{"id":9749,"name":"Neurofeedback","url":"https://www.academia.edu/Documents/in/Neurofeedback?f_ri=12950","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="10402" rel="nofollow" href="https://www.academia.edu/Documents/in/EEG">EEG</a>, <script data-card-contents-for-ri="10402" type="text/json">{"id":10402,"name":"EEG","url":"https://www.academia.edu/Documents/in/EEG?f_ri=12950","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="11882" rel="nofollow" href="https://www.academia.edu/Documents/in/Non-Invasive_BCI">Non-Invasive BCI</a><script data-card-contents-for-ri="11882" type="text/json">{"id":11882,"name":"Non-Invasive BCI","url":"https://www.academia.edu/Documents/in/Non-Invasive_BCI?f_ri=12950","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=10834831]'), work: {"id":10834831,"title":"Hypothesis-driven methods to augment human cognition by optimizing cortical oscillations","created_at":"2015-02-16T04:45:59.957-08:00","url":"https://www.academia.edu/10834831/Hypothesis_driven_methods_to_augment_human_cognition_by_optimizing_cortical_oscillations?f_ri=12950","dom_id":"work_10834831","summary":"Cortical oscillations have been shown to represent fundamental functions of a working brain, e.g., communication, stimulus binding, error monitoring, and inhibition, and are directly linked to behavior. Recent studies intervening with these oscillations have demonstrated effective modulation of both the oscillations and behavior. In this review, we collect evidence in favor of how hypothesis-driven methods can be used to augment cognition by optimizing cortical oscillations. We elaborate their potential usefulness for three target groups: healthy elderly, patients with attention deficit/hyperactivity disorder, and healthy young adults. We discuss the relevance of neuronal oscillations in each group and show how each of them can benefit from the manipulation of functionally-related oscillations. Further, we describe methods for manipulation of neuronal oscillations including direct brain stimulation as well as indirect task alterations. We also discuss practical considerations about the proposed techniques. In conclusion, we propose that insights from neuroscience should guide techniques to augment human cognition, which in turn can provide a better understanding of how the human brain works.","downloadable_attachments":[{"id":36654375,"asset_id":10834831,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":26333879,"first_name":"Horschig","last_name":"Jörn M.","domain_name":"radboud","page_name":"HorschigJörnM","display_name":"Horschig Jörn M.","profile_url":"https://radboud.academia.edu/HorschigJ%C3%B6rnM?f_ri=12950","photo":"/images/s65_no_pic.png"}],"research_interests":[{"id":4624,"name":"Brain-computer interfaces","url":"https://www.academia.edu/Documents/in/Brain-computer_interfaces?f_ri=12950","nofollow":true},{"id":9749,"name":"Neurofeedback","url":"https://www.academia.edu/Documents/in/Neurofeedback?f_ri=12950","nofollow":true},{"id":10402,"name":"EEG","url":"https://www.academia.edu/Documents/in/EEG?f_ri=12950","nofollow":true},{"id":11882,"name":"Non-Invasive BCI","url":"https://www.academia.edu/Documents/in/Non-Invasive_BCI?f_ri=12950","nofollow":true},{"id":12950,"name":"Neural Oscillations","url":"https://www.academia.edu/Documents/in/Neural_Oscillations?f_ri=12950"},{"id":31959,"name":"Non Invasive Brain Stimulation","url":"https://www.academia.edu/Documents/in/Non_Invasive_Brain_Stimulation?f_ri=12950"},{"id":318293,"name":"Brain Stimulation","url":"https://www.academia.edu/Documents/in/Brain_Stimulation?f_ri=12950"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_34922858" data-work_id="34922858" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/34922858/GEOLOGICAL_AND_BIOLOGICAL_OSCILLATIONS_A_HOLISTIC_CONTINUOUS_SPECTRUM">GEOLOGICAL AND BIOLOGICAL OSCILLATIONS: A HOLISTIC CONTINUOUS SPECTRUM</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">Primary scientific literature documents frequency oscillations in both the geosphere and the biosphere arising from a host of different phenomena. At certain frequencies there are known associations between geological oscillations and... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_34922858" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">Primary scientific literature documents frequency oscillations in both the geosphere and the biosphere arising from a host of different phenomena. At certain frequencies there are known associations between geological oscillations and biological oscillations. This raises the question as to whether these phenomena are functionally related. A compilation of the literature was undertaken, covering a spectral range of 18 orders-of-magnitude extending from 955 Ky/cycle (1.29E-14 Hz) to 0.10 ms/cycle (1.00E+4 Hz), noting those frequencies where there was geological-biological concordance. Concordance was determined for 28 frequency groupings distributed in 10 defined cycle bands within the geological-biological spectrum. The ultradian component of the circadian cycle band was the only band showing little geological-biological concordance, with concordance only at its extreme high-frequency values. Of the 384 references examined reporting geological or biological frequencies, 21 specifically mention concordances between geological and biological oscillations. Further, there are geological and biological frequencies where there are no apparent concordances between the two data sets, indicating that the histogram presented is far from a random distribution. At frequencies where there exists geological and biological concordance, a geological frequency can entrain biological frequencies and provoke a biological response. Lower frequencies affect populations; higher frequencies affect individual organisms. Frequencies of geological-biological concordance provide opportunity for the establishment of multiple interrelated zeitgebers, with disruption of these relationships potentially resulting in physiologic dysfunction, predisposing organisms toward the development of pathology and/or extinction. Précis: Oscillations across eighteen orders-of-magnitude in both the geosphere and the biosphere exhibit concordances where there may be entrainment with resultant geological-biological communication.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/34922858" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="4e032b1b5306e8e1f540c98891aaa105" rel="nofollow" data-download="{"attachment_id":54784265,"asset_id":34922858,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/54784265/download_file?st=MTczNzI5NjIxMyw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="18129602" href="https://midwestern.academia.edu/KenNelson">Ken Nelson, DO</a><script data-card-contents-for-user="18129602" type="text/json">{"id":18129602,"first_name":"Ken","last_name":"Nelson, DO","domain_name":"midwestern","page_name":"KenNelson","display_name":"Ken Nelson, DO","profile_url":"https://midwestern.academia.edu/KenNelson?f_ri=12950","photo":"https://0.academia-photos.com/18129602/5653717/6432387/s65_ken.nelson.png"}</script></span></span></li><li class="js-paper-rank-work_34922858 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="34922858"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 34922858, container: ".js-paper-rank-work_34922858", }); });</script></li><li class="js-percentile-work_34922858 InlineList-item InlineList-item--bordered hidden u-tcGrayDark"><span class="percentile-widget hidden"><span class="u-mr2x percentile-widget" style="display: none">•</span><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 34922858; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-percentile-work_34922858"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></li><li class="js-view-count-work_34922858 InlineList-item InlineList-item--bordered hidden"><div><span><span class="js-view-count view-count u-mr2x" data-work-id="34922858"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 34922858; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=34922858]").text(description); $(".js-view-count-work_34922858").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_34922858").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="34922858"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">14</a>  </div><span class="InlineList-item-text u-textTruncate u-pl10x"><a class="InlineList-item-text" data-has-card-for-ri="161" rel="nofollow" href="https://www.academia.edu/Documents/in/Neuroscience">Neuroscience</a>, <script data-card-contents-for-ri="161" type="text/json">{"id":161,"name":"Neuroscience","url":"https://www.academia.edu/Documents/in/Neuroscience?f_ri=12950","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="167" rel="nofollow" href="https://www.academia.edu/Documents/in/Physiology">Physiology</a>, <script data-card-contents-for-ri="167" type="text/json">{"id":167,"name":"Physiology","url":"https://www.academia.edu/Documents/in/Physiology?f_ri=12950","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="400" rel="nofollow" href="https://www.academia.edu/Documents/in/Earth_Sciences">Earth Sciences</a>, <script data-card-contents-for-ri="400" type="text/json">{"id":400,"name":"Earth Sciences","url":"https://www.academia.edu/Documents/in/Earth_Sciences?f_ri=12950","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="409" rel="nofollow" href="https://www.academia.edu/Documents/in/Geophysics">Geophysics</a><script data-card-contents-for-ri="409" type="text/json">{"id":409,"name":"Geophysics","url":"https://www.academia.edu/Documents/in/Geophysics?f_ri=12950","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=34922858]'), work: {"id":34922858,"title":"GEOLOGICAL AND BIOLOGICAL OSCILLATIONS: A HOLISTIC CONTINUOUS SPECTRUM","created_at":"2017-10-22T06:02:11.591-07:00","url":"https://www.academia.edu/34922858/GEOLOGICAL_AND_BIOLOGICAL_OSCILLATIONS_A_HOLISTIC_CONTINUOUS_SPECTRUM?f_ri=12950","dom_id":"work_34922858","summary":"Primary scientific literature documents frequency oscillations in both the geosphere and the biosphere arising from a host of different phenomena. At certain frequencies there are known associations between geological oscillations and biological oscillations. This raises the question as to whether these phenomena are functionally related. A compilation of the literature was undertaken, covering a spectral range of 18 orders-of-magnitude extending from 955 Ky/cycle (1.29E-14 Hz) to 0.10 ms/cycle (1.00E+4 Hz), noting those frequencies where there was geological-biological concordance. Concordance was determined for 28 frequency groupings distributed in 10 defined cycle bands within the geological-biological spectrum. The ultradian component of the circadian cycle band was the only band showing little geological-biological concordance, with concordance only at its extreme high-frequency values. Of the 384 references examined reporting geological or biological frequencies, 21 specifically mention concordances between geological and biological oscillations. Further, there are geological and biological frequencies where there are no apparent concordances between the two data sets, indicating that the histogram presented is far from a random distribution. At frequencies where there exists geological and biological concordance, a geological frequency can entrain biological frequencies and provoke a biological response. Lower frequencies affect populations; higher frequencies affect individual organisms. Frequencies of geological-biological concordance provide opportunity for the establishment of multiple interrelated zeitgebers, with disruption of these relationships potentially resulting in physiologic dysfunction, predisposing organisms toward the development of pathology and/or extinction. Précis: Oscillations across eighteen orders-of-magnitude in both the geosphere and the biosphere exhibit concordances where there may be entrainment with resultant geological-biological communication.","downloadable_attachments":[{"id":54784265,"asset_id":34922858,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":18129602,"first_name":"Ken","last_name":"Nelson, DO","domain_name":"midwestern","page_name":"KenNelson","display_name":"Ken Nelson, DO","profile_url":"https://midwestern.academia.edu/KenNelson?f_ri=12950","photo":"https://0.academia-photos.com/18129602/5653717/6432387/s65_ken.nelson.png"}],"research_interests":[{"id":161,"name":"Neuroscience","url":"https://www.academia.edu/Documents/in/Neuroscience?f_ri=12950","nofollow":true},{"id":167,"name":"Physiology","url":"https://www.academia.edu/Documents/in/Physiology?f_ri=12950","nofollow":true},{"id":400,"name":"Earth Sciences","url":"https://www.academia.edu/Documents/in/Earth_Sciences?f_ri=12950","nofollow":true},{"id":409,"name":"Geophysics","url":"https://www.academia.edu/Documents/in/Geophysics?f_ri=12950","nofollow":true},{"id":498,"name":"Physics","url":"https://www.academia.edu/Documents/in/Physics?f_ri=12950"},{"id":4315,"name":"Origins of Life","url":"https://www.academia.edu/Documents/in/Origins_of_Life?f_ri=12950"},{"id":12950,"name":"Neural Oscillations","url":"https://www.academia.edu/Documents/in/Neural_Oscillations?f_ri=12950"},{"id":23179,"name":"Astrophysics","url":"https://www.academia.edu/Documents/in/Astrophysics?f_ri=12950"},{"id":37537,"name":"Cardiovascular Physiology","url":"https://www.academia.edu/Documents/in/Cardiovascular_Physiology?f_ri=12950"},{"id":97696,"name":"Entrainment","url":"https://www.academia.edu/Documents/in/Entrainment?f_ri=12950"},{"id":174781,"name":"Oscillations","url":"https://www.academia.edu/Documents/in/Oscillations?f_ri=12950"},{"id":209034,"name":"Brainwave Entrainment","url":"https://www.academia.edu/Documents/in/Brainwave_Entrainment?f_ri=12950"},{"id":1681026,"name":"Biochemistry and cell biology","url":"https://www.academia.edu/Documents/in/Biochemistry_and_cell_biology?f_ri=12950"},{"id":1809888,"name":"Schumann resonance","url":"https://www.academia.edu/Documents/in/Schumann_resonance?f_ri=12950"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_24070876 coauthored" data-work_id="24070876" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/24070876/Serotonin_Amygdala_and_Fear_Assembling_the_Puzzle">Serotonin, Amygdala and Fear: Assembling the Puzzle</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">The fear circuitry orchestrates defense mechanisms in response to environmental threats. This circuitry is evolutionarily crucial for survival, but its dysregulation is thought to play a major role in the pathophysiology of psychiatric... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_24070876" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">The fear circuitry orchestrates defense mechanisms in response to environmental threats. This circuitry is evolutionarily crucial for survival, but its dysregulation is thought to play a major role in the pathophysiology of psychiatric conditions in humans. The amygdala is a key player in the processing of fear. This brain area is prominently modulated by the neurotransmitter serotonin (5-hydroxytryptamine, 5-HT). The 5-HT input to the amygdala has drawn particular interest because genetic and pharmacological alterations of the 5-HT transporter (5-HTT) affect amygdala activation in response to emotional stimuli. Nonetheless, the impact of 5-HT on fear processing remains poorly understood.The aim of this review is to elucidate the physiological role of 5-HT in fear learning via its action on the neuronal circuits of the amygdala. Since 5-HT release increases in the basolateral amygdala (BLA) during both fear memory acquisition and expression, we examine whether and how 5-HT neurons encode aversive stimuli and aversive cues. Next, we describe pharmacological and genetic alterations of 5-HT neurotransmission that, in both rodents and humans, lead to altered fear learning. To explore the mechanisms through which 5-HT could modulate conditioned fear, we focus on the rodent BLA. We propose that a circuit-based approach taking into account the localization of specific 5-HT receptors on neurochemically-defined neurons in the BLA may be essential to decipher the role of 5-HT in emotional behavior. In keeping with a 5-HT control of fear learning, we review electrophysiological data suggesting that 5-HT regulates synaptic plasticity, spike synchrony and theta oscillations in the BLA via actions on different subcellular compartments of principal neurons and distinct GABAergic interneuron populations. Finally, we discuss how recently developed optogenetic tools combined with electrophysiological recordings and behavior could progress the knowledge of the mechanisms underlying 5-HT modulation of fear learning via action on amygdala circuits. Such advancement could pave the way for a deeper understanding of 5-HT in emotional behavior in both health and disease.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/24070876" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="b72354f9394cfed6fbc7202cc1ec5400" rel="nofollow" data-download="{"attachment_id":44441541,"asset_id":24070876,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/44441541/download_file?st=MTczNzI5NjIxNCw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="40867177" href="https://univ-amu.academia.edu/MarcoBocchio">Marco Bocchio</a><script data-card-contents-for-user="40867177" type="text/json">{"id":40867177,"first_name":"Marco","last_name":"Bocchio","domain_name":"univ-amu","page_name":"MarcoBocchio","display_name":"Marco Bocchio","profile_url":"https://univ-amu.academia.edu/MarcoBocchio?f_ri=12950","photo":"/images/s65_no_pic.png"}</script></span></span><span class="u-displayInlineBlock InlineList-item-text"> and <span class="u-textDecorationUnderline u-clickable InlineList-item-text js-work-more-authors-24070876">+1</span><div class="hidden js-additional-users-24070876"><div><span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a href="https://independent.academia.edu/DavidBannerman">David Bannerman</a></span></div></div></span><script>(function(){ var popoverSettings = { el: $('.js-work-more-authors-24070876'), placement: 'bottom', hide_delay: 200, html: true, content: function(){ return $('.js-additional-users-24070876').html(); } } new HoverPopover(popoverSettings); })();</script></li><li class="js-paper-rank-work_24070876 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="24070876"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 24070876, container: ".js-paper-rank-work_24070876", }); });</script></li><li class="js-percentile-work_24070876 InlineList-item InlineList-item--bordered hidden u-tcGrayDark"><span class="percentile-widget hidden"><span class="u-mr2x percentile-widget" style="display: none">•</span><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 24070876; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-percentile-work_24070876"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></li><li class="js-view-count-work_24070876 InlineList-item InlineList-item--bordered hidden"><div><span><span class="js-view-count view-count u-mr2x" data-work-id="24070876"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 24070876; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=24070876]").text(description); $(".js-view-count-work_24070876").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_24070876").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="24070876"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">15</a>  </div><span class="InlineList-item-text u-textTruncate u-pl10x"><a class="InlineList-item-text" data-has-card-for-ri="2007" rel="nofollow" href="https://www.academia.edu/Documents/in/Electrophysiology">Electrophysiology</a>, <script data-card-contents-for-ri="2007" type="text/json">{"id":2007,"name":"Electrophysiology","url":"https://www.academia.edu/Documents/in/Electrophysiology?f_ri=12950","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="3931" rel="nofollow" href="https://www.academia.edu/Documents/in/Fear">Fear</a>, <script data-card-contents-for-ri="3931" type="text/json">{"id":3931,"name":"Fear","url":"https://www.academia.edu/Documents/in/Fear?f_ri=12950","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="12950" rel="nofollow" href="https://www.academia.edu/Documents/in/Neural_Oscillations">Neural Oscillations</a>, <script data-card-contents-for-ri="12950" type="text/json">{"id":12950,"name":"Neural Oscillations","url":"https://www.academia.edu/Documents/in/Neural_Oscillations?f_ri=12950","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="13485" rel="nofollow" href="https://www.academia.edu/Documents/in/GABAergic_Neurotransmission">GABAergic Neurotransmission</a><script data-card-contents-for-ri="13485" type="text/json">{"id":13485,"name":"GABAergic Neurotransmission","url":"https://www.academia.edu/Documents/in/GABAergic_Neurotransmission?f_ri=12950","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=24070876]'), work: {"id":24070876,"title":"Serotonin, Amygdala and Fear: Assembling the Puzzle","created_at":"2016-04-05T09:44:10.241-07:00","url":"https://www.academia.edu/24070876/Serotonin_Amygdala_and_Fear_Assembling_the_Puzzle?f_ri=12950","dom_id":"work_24070876","summary":"The fear circuitry orchestrates defense mechanisms in response to environmental threats. This circuitry is evolutionarily crucial for survival, but its dysregulation is thought to play a major role in the pathophysiology of psychiatric conditions in humans. The amygdala is a key player in the processing of fear. This brain area is prominently modulated by the neurotransmitter serotonin (5-hydroxytryptamine, 5-HT). The 5-HT input to the amygdala has drawn particular interest because genetic and pharmacological alterations of the 5-HT transporter (5-HTT) affect amygdala activation in response to emotional stimuli. Nonetheless, the impact of 5-HT on fear processing remains poorly understood.The aim of this review is to elucidate the physiological role of 5-HT in fear learning via its action on the neuronal circuits of the amygdala. Since 5-HT release increases in the basolateral amygdala (BLA) during both fear memory acquisition and expression, we examine whether and how 5-HT neurons encode aversive stimuli and aversive cues. Next, we describe pharmacological and genetic alterations of 5-HT neurotransmission that, in both rodents and humans, lead to altered fear learning. To explore the mechanisms through which 5-HT could modulate conditioned fear, we focus on the rodent BLA. We propose that a circuit-based approach taking into account the localization of specific 5-HT receptors on neurochemically-defined neurons in the BLA may be essential to decipher the role of 5-HT in emotional behavior. In keeping with a 5-HT control of fear learning, we review electrophysiological data suggesting that 5-HT regulates synaptic plasticity, spike synchrony and theta oscillations in the BLA via actions on different subcellular compartments of principal neurons and distinct GABAergic interneuron populations. Finally, we discuss how recently developed optogenetic tools combined with electrophysiological recordings and behavior could progress the knowledge of the mechanisms underlying 5-HT modulation of fear learning via action on amygdala circuits. Such advancement could pave the way for a deeper understanding of 5-HT in emotional behavior in both health and disease.","downloadable_attachments":[{"id":44441541,"asset_id":24070876,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":40867177,"first_name":"Marco","last_name":"Bocchio","domain_name":"univ-amu","page_name":"MarcoBocchio","display_name":"Marco Bocchio","profile_url":"https://univ-amu.academia.edu/MarcoBocchio?f_ri=12950","photo":"/images/s65_no_pic.png"},{"id":57938452,"first_name":"David","last_name":"Bannerman","domain_name":"independent","page_name":"DavidBannerman","display_name":"David Bannerman","profile_url":"https://independent.academia.edu/DavidBannerman?f_ri=12950","photo":"/images/s65_no_pic.png"}],"research_interests":[{"id":2007,"name":"Electrophysiology","url":"https://www.academia.edu/Documents/in/Electrophysiology?f_ri=12950","nofollow":true},{"id":3931,"name":"Fear","url":"https://www.academia.edu/Documents/in/Fear?f_ri=12950","nofollow":true},{"id":12950,"name":"Neural Oscillations","url":"https://www.academia.edu/Documents/in/Neural_Oscillations?f_ri=12950","nofollow":true},{"id":13485,"name":"GABAergic Neurotransmission","url":"https://www.academia.edu/Documents/in/GABAergic_Neurotransmission?f_ri=12950","nofollow":true},{"id":51565,"name":"Serotonin","url":"https://www.academia.edu/Documents/in/Serotonin?f_ri=12950"},{"id":58484,"name":"Inhibitory Interneurons","url":"https://www.academia.edu/Documents/in/Inhibitory_Interneurons?f_ri=12950"},{"id":61926,"name":"Neuronal Network","url":"https://www.academia.edu/Documents/in/Neuronal_Network?f_ri=12950"},{"id":66513,"name":"Optogenetics","url":"https://www.academia.edu/Documents/in/Optogenetics?f_ri=12950"},{"id":102339,"name":"Serotonin Transporter","url":"https://www.academia.edu/Documents/in/Serotonin_Transporter?f_ri=12950"},{"id":102344,"name":"5-HTTLPR","url":"https://www.academia.edu/Documents/in/5-HTTLPR?f_ri=12950"},{"id":159962,"name":"Amygdala","url":"https://www.academia.edu/Documents/in/Amygdala?f_ri=12950"},{"id":167036,"name":"Fear conditioning","url":"https://www.academia.edu/Documents/in/Fear_conditioning?f_ri=12950"},{"id":193974,"name":"Neurons","url":"https://www.academia.edu/Documents/in/Neurons?f_ri=12950"},{"id":289265,"name":"Antidepressants","url":"https://www.academia.edu/Documents/in/Antidepressants?f_ri=12950"},{"id":600591,"name":"SSRIs","url":"https://www.academia.edu/Documents/in/SSRIs?f_ri=12950"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_2633850 coauthored" data-work_id="2633850" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/2633850/Rhythmic_Auditory_Stimulation_Influences_Syntactic_Processing_in_Children_With_Developmental_Language_Disorders">Rhythmic Auditory Stimulation Influences Syntactic Processing in Children With Developmental Language Disorders</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">Przybylski, L., Bedoin, N., Herbillon, V., Krifi-Papoz, S., Léculier, L., Roch, D., Kotz, S., & Tillmann, B. (2013). Rhythmic auditory stimulation influences syntactic processing in children with developmental language disorders.... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_2633850" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">Przybylski, L., Bedoin, N., Herbillon, V., Krifi-Papoz, S., Léculier, L., Roch, D., Kotz, S., & Tillmann, B. (2013). Rhythmic auditory stimulation influences syntactic processing in children with developmental language disorders. Neuropsychology, 27(1), 121-131. <br /> <br />Abstract. <br />Objective: Children with developmental language disorders have been shown to be impaired not only in <br />language processing (including syntax), but also in rhythm and meter perception. Our study tested the <br />influence of external rhythmic auditory stimulation (i.e., musical rhythm) on syntax processing in <br />children with specific language impairment (SLI; Experiment 1A) and dyslexia (Experiment 1B). <br />Method: Children listened to either regular or irregular musical prime sequences followed by blocks of <br />grammatically correct and incorrect sentences. They were required to perform grammaticality judgments <br />for each auditorily presented sentence. Results: Performance of all children (SLI, dyslexia, and controls) <br />in the grammaticality judgments was better after regular prime sequences than after irregular prime <br />sequences, as shown by d= data. The benefit of the regular prime was stronger for SLI children (partial <br />2 .34) than for dyslexic children (partial 2 .14), who reached higher performance levels. <br />Conclusion: Together with previous findings on deficits in temporal processing and sequencing, as well <br />as with the recent proposition of a temporal sampling (oscillatory) framework for developmental <br />language disorders (U. A. Goswami, 2011, Temporal sampling framework for developmental dyslexia, <br />Trends in Cognitive Sciences, Vol. 15, pp. 3–10), our results point to potential avenues in using rhythmic <br />structures (even in nonverbal materials) to boost linguistic structure processing.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/2633850" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="964322613dcc622833d47fd15ce49257" rel="nofollow" data-download="{"attachment_id":30636435,"asset_id":2633850,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/30636435/download_file?st=MTczNzI5NjIxNCw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="1189221" href="https://univ-lyon2.academia.edu/NathalieBedoin">Nathalie Bedoin</a><script data-card-contents-for-user="1189221" type="text/json">{"id":1189221,"first_name":"Nathalie","last_name":"Bedoin","domain_name":"univ-lyon2","page_name":"NathalieBedoin","display_name":"Nathalie Bedoin","profile_url":"https://univ-lyon2.academia.edu/NathalieBedoin?f_ri=12950","photo":"https://0.academia-photos.com/1189221/425572/525369/s65_nathalie.bedoin.jpg"}</script></span></span><span class="u-displayInlineBlock InlineList-item-text"> and <span class="u-textDecorationUnderline u-clickable InlineList-item-text js-work-more-authors-2633850">+2</span><div class="hidden js-additional-users-2633850"><div><span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a href="https://independent.academia.edu/DidierRoch">Didier Roch</a></span></div><div><span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a href="https://independent.academia.edu/LaureLeculier">Laure Leculier</a></span></div></div></span><script>(function(){ var popoverSettings = { el: $('.js-work-more-authors-2633850'), placement: 'bottom', hide_delay: 200, html: true, content: function(){ return $('.js-additional-users-2633850').html(); 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(2013). Rhythmic auditory stimulation influences syntactic processing in children with developmental language disorders. Neuropsychology, 27(1), 121-131.\r\n\r\nAbstract. \r\nObjective: Children with developmental language disorders have been shown to be impaired not only in\r\nlanguage processing (including syntax), but also in rhythm and meter perception. Our study tested the\r\ninfluence of external rhythmic auditory stimulation (i.e., musical rhythm) on syntax processing in\r\nchildren with specific language impairment (SLI; Experiment 1A) and dyslexia (Experiment 1B).\r\nMethod: Children listened to either regular or irregular musical prime sequences followed by blocks of\r\ngrammatically correct and incorrect sentences. They were required to perform grammaticality judgments\r\nfor each auditorily presented sentence. Results: Performance of all children (SLI, dyslexia, and controls)\r\nin the grammaticality judgments was better after regular prime sequences than after irregular prime\r\nsequences, as shown by d= data. The benefit of the regular prime was stronger for SLI children (partial\r\n\u00012 \u0002 .34) than for dyslexic children (partial \u00012 \u0002 .14), who reached higher performance levels.\r\nConclusion: Together with previous findings on deficits in temporal processing and sequencing, as well\r\nas with the recent proposition of a temporal sampling (oscillatory) framework for developmental\r\nlanguage disorders (U. A. Goswami, 2011, Temporal sampling framework for developmental dyslexia,\r\nTrends in Cognitive Sciences, Vol. 15, pp. 3–10), our results point to potential avenues in using rhythmic\r\nstructures (even in nonverbal materials) to boost linguistic structure processing.","downloadable_attachments":[{"id":30636435,"asset_id":2633850,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":1189221,"first_name":"Nathalie","last_name":"Bedoin","domain_name":"univ-lyon2","page_name":"NathalieBedoin","display_name":"Nathalie Bedoin","profile_url":"https://univ-lyon2.academia.edu/NathalieBedoin?f_ri=12950","photo":"https://0.academia-photos.com/1189221/425572/525369/s65_nathalie.bedoin.jpg"},{"id":54946607,"first_name":"Didier","last_name":"Roch","domain_name":"independent","page_name":"DidierRoch","display_name":"Didier 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Phonetics-Phonology Relationship in the Neurobiology of Language</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">In this work, I address the connection of phonetic structure with phonological representations. This classical issue is discussed in the light of recent neurophysiological data which – thanks to direct measurements of temporal and spatial... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_34877933" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">In this work, I address the connection of phonetic structure with phonological representations. This classical issue is discussed in the light of recent neurophysiological data which – thanks to direct measurements of temporal and spatial brain activation – provide new avenues to investigate the biological substrate of human language. After describing principal techniques and methods, I critically discuss magnetoencephalographic and electroencephalographic findings of speech processing based on event-related potentials and event-related oscillatory rhythms. The available data do not permit us to clearly disambiguate between neural evidence suggesting pure acoustic patterns and those indicating abstract phonological features. Starting from this evidence, which only at the surface represents a limit, I develop a preliminary proposal where discretization and phonological abstraction are the result of a continuous process that converts spectro-temporal (acoustic) states into neurophysiological states such that some properties of the former undergo changes interacting with the latter until a new equilibrium is reached. I assume that – at the end of the process – phonological segments (and the related categorical processes) take the form of continuous neural states represented by nested cortical oscillatory rhythms spatially distributed in the auditory cortex. Within this perspective, distinctive features (i.e., the relevant representational linguistic primitives) are represented by both spatially local and distributed neural selectivity. I suggest that this hypothesis is suitable to explain hierarchical layout of auditory cortex highly specialized in analyzing different aspects of the speech signal and to explain learning and memory processes during the acquisition of phonological systems.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/34877933" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="8d5e52dd4799e2c23cba618c239f4695" rel="nofollow" data-download="{"attachment_id":54737547,"asset_id":34877933,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/54737547/download_file?st=MTczNzI5NjIxNCw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="7047094" href="https://unisalento.academia.edu/MGrimaldi">Mirko Grimaldi</a><script data-card-contents-for-user="7047094" type="text/json">{"id":7047094,"first_name":"Mirko","last_name":"Grimaldi","domain_name":"unisalento","page_name":"MGrimaldi","display_name":"Mirko Grimaldi","profile_url":"https://unisalento.academia.edu/MGrimaldi?f_ri=12950","photo":"https://0.academia-photos.com/7047094/4101595/18107465/s65_mirko.grimaldi.png"}</script></span></span></li><li class="js-paper-rank-work_34877933 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="34877933"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 34877933, container: ".js-paper-rank-work_34877933", }); 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$(".js-view-count[data-work-id=34877933]").text(description); $(".js-view-count-work_34877933").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_34877933").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="34877933"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">5</a>  </div><span class="InlineList-item-text u-textTruncate u-pl9x"><a class="InlineList-item-text" data-has-card-for-ri="12950" rel="nofollow" href="https://www.academia.edu/Documents/in/Neural_Oscillations">Neural Oscillations</a>, <script data-card-contents-for-ri="12950" type="text/json">{"id":12950,"name":"Neural Oscillations","url":"https://www.academia.edu/Documents/in/Neural_Oscillations?f_ri=12950","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="51870" rel="nofollow" href="https://www.academia.edu/Documents/in/Event-Related_Potentials">Event-Related Potentials</a>, <script data-card-contents-for-ri="51870" type="text/json">{"id":51870,"name":"Event-Related Potentials","url":"https://www.academia.edu/Documents/in/Event-Related_Potentials?f_ri=12950","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="60338" rel="nofollow" href="https://www.academia.edu/Documents/in/Phonetics_and_Phonology">Phonetics and Phonology</a>, <script data-card-contents-for-ri="60338" type="text/json">{"id":60338,"name":"Phonetics and Phonology","url":"https://www.academia.edu/Documents/in/Phonetics_and_Phonology?f_ri=12950","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="174781" rel="nofollow" href="https://www.academia.edu/Documents/in/Oscillations">Oscillations</a><script data-card-contents-for-ri="174781" type="text/json">{"id":174781,"name":"Oscillations","url":"https://www.academia.edu/Documents/in/Oscillations?f_ri=12950","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=34877933]'), work: {"id":34877933,"title":"The Phonetics-Phonology Relationship in the Neurobiology of Language","created_at":"2017-10-17T00:08:46.411-07:00","url":"https://www.academia.edu/34877933/The_Phonetics_Phonology_Relationship_in_the_Neurobiology_of_Language?f_ri=12950","dom_id":"work_34877933","summary":"In this work, I address the connection of phonetic structure with phonological representations. This classical issue is discussed in the light of recent neurophysiological data which – thanks to direct measurements of temporal and spatial brain activation – provide new avenues to investigate the biological substrate of human language. After describing principal techniques and methods, I critically discuss magnetoencephalographic and electroencephalographic findings of speech processing based on event-related potentials and event-related oscillatory rhythms. The available data do not permit us to clearly disambiguate between neural evidence suggesting pure acoustic patterns and those indicating abstract phonological features. Starting from this evidence, which only at the surface represents a limit, I develop a preliminary proposal where discretization and phonological abstraction are the result of a continuous process that converts spectro-temporal (acoustic) states into neurophysiological states such that some properties of the former undergo changes interacting with the latter until a new equilibrium is reached. I assume that – at the end of the process – phonological segments (and the related categorical processes) take the form of continuous neural states represented by nested cortical oscillatory rhythms spatially distributed in the auditory cortex. Within this perspective, distinctive features (i.e., the relevant representational linguistic primitives) are represented by both spatially local and distributed neural selectivity. I suggest that this hypothesis is suitable to explain hierarchical layout of auditory cortex highly specialized in analyzing different aspects of the speech signal and to explain learning and memory processes during the acquisition of phonological systems.","downloadable_attachments":[{"id":54737547,"asset_id":34877933,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":7047094,"first_name":"Mirko","last_name":"Grimaldi","domain_name":"unisalento","page_name":"MGrimaldi","display_name":"Mirko Grimaldi","profile_url":"https://unisalento.academia.edu/MGrimaldi?f_ri=12950","photo":"https://0.academia-photos.com/7047094/4101595/18107465/s65_mirko.grimaldi.png"}],"research_interests":[{"id":12950,"name":"Neural Oscillations","url":"https://www.academia.edu/Documents/in/Neural_Oscillations?f_ri=12950","nofollow":true},{"id":51870,"name":"Event-Related Potentials","url":"https://www.academia.edu/Documents/in/Event-Related_Potentials?f_ri=12950","nofollow":true},{"id":60338,"name":"Phonetics and Phonology","url":"https://www.academia.edu/Documents/in/Phonetics_and_Phonology?f_ri=12950","nofollow":true},{"id":174781,"name":"Oscillations","url":"https://www.academia.edu/Documents/in/Oscillations?f_ri=12950","nofollow":true},{"id":383381,"name":"Distinctive Features","url":"https://www.academia.edu/Documents/in/Distinctive_Features?f_ri=12950"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_34338062 coauthored" data-work_id="34338062" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/34338062/Synaptic_Plasticity_Engrams_and_Network_Oscillations_in_Amygdala_Circuits_for_Storage_and_Retrieval_of_Emotional_Memories">Synaptic Plasticity, Engrams, and Network Oscillations in Amygdala Circuits for Storage and Retrieval of Emotional Memories</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">The neuronal circuits of the basolateral amygdala (BLA) are crucial for acquisition, consolidation, retrieval, and extinction of associative emotional memories. Synaptic plasticity in BLA neurons is essential for asso-ciative emotional... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_34338062" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">The neuronal circuits of the basolateral amygdala (BLA) are crucial for acquisition, consolidation, retrieval, and extinction of associative emotional memories. Synaptic plasticity in BLA neurons is essential for asso-ciative emotional learning and is a candidate mechanism through which subsets of BLA neurons (commonly termed ''engram'') are recruited during learning and reactivated during memory retrieval. In parallel, synchronous oscillations in the theta and gamma bands between the BLA and interconnected structures have been shown to occur during consolidation and retrieval of emotional memories. Understanding how these cellular and network phenomena interact is vital to decipher the roles of emotional memory formation and storage in the healthy and pathological brain. Here, we review data on synaptic plasticity, engrams, and network oscillations in the rodent BLA. We explore mechanisms through which synaptic plasticity, engrams, and long-range synchrony might be interconnected.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/34338062" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="08e3e324b41bb282cca47565d8ac8271" rel="nofollow" data-download="{"attachment_id":54232644,"asset_id":34338062,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/54232644/download_file?st=MTczNzI5NjIxNCw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="40867177" href="https://univ-amu.academia.edu/MarcoBocchio">Marco Bocchio</a><script data-card-contents-for-user="40867177" type="text/json">{"id":40867177,"first_name":"Marco","last_name":"Bocchio","domain_name":"univ-amu","page_name":"MarcoBocchio","display_name":"Marco Bocchio","profile_url":"https://univ-amu.academia.edu/MarcoBocchio?f_ri=12950","photo":"/images/s65_no_pic.png"}</script></span></span><span class="u-displayInlineBlock InlineList-item-text"> and <span class="u-textDecorationUnderline u-clickable InlineList-item-text js-work-more-authors-34338062">+1</span><div class="hidden js-additional-users-34338062"><div><span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a href="https://independent.academia.edu/MarcoCapogna">Marco Capogna</a></span></div></div></span><script>(function(){ var popoverSettings = { el: $('.js-work-more-authors-34338062'), placement: 'bottom', hide_delay: 200, html: true, content: function(){ return $('.js-additional-users-34338062').html(); } } new HoverPopover(popoverSettings); })();</script></li><li class="js-paper-rank-work_34338062 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="34338062"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 34338062, container: ".js-paper-rank-work_34338062", }); });</script></li><li class="js-percentile-work_34338062 InlineList-item InlineList-item--bordered hidden u-tcGrayDark"><span class="percentile-widget hidden"><span class="u-mr2x percentile-widget" style="display: none">•</span><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 34338062; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-percentile-work_34338062"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></li><li class="js-view-count-work_34338062 InlineList-item InlineList-item--bordered hidden"><div><span><span class="js-view-count view-count u-mr2x" data-work-id="34338062"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 34338062; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=34338062]").text(description); $(".js-view-count-work_34338062").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_34338062").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="34338062"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">8</a>  </div><span class="InlineList-item-text u-textTruncate u-pl9x"><a class="InlineList-item-text" data-has-card-for-ri="254" rel="nofollow" href="https://www.academia.edu/Documents/in/Emotion">Emotion</a>, <script data-card-contents-for-ri="254" type="text/json">{"id":254,"name":"Emotion","url":"https://www.academia.edu/Documents/in/Emotion?f_ri=12950","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="3931" rel="nofollow" href="https://www.academia.edu/Documents/in/Fear">Fear</a>, <script data-card-contents-for-ri="3931" type="text/json">{"id":3931,"name":"Fear","url":"https://www.academia.edu/Documents/in/Fear?f_ri=12950","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="12950" rel="nofollow" href="https://www.academia.edu/Documents/in/Neural_Oscillations">Neural Oscillations</a>, <script data-card-contents-for-ri="12950" type="text/json">{"id":12950,"name":"Neural Oscillations","url":"https://www.academia.edu/Documents/in/Neural_Oscillations?f_ri=12950","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="46858" rel="nofollow" href="https://www.academia.edu/Documents/in/Memory">Memory</a><script data-card-contents-for-ri="46858" type="text/json">{"id":46858,"name":"Memory","url":"https://www.academia.edu/Documents/in/Memory?f_ri=12950","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=34338062]'), work: {"id":34338062,"title":"Synaptic Plasticity, Engrams, and Network Oscillations in Amygdala Circuits for Storage and Retrieval of Emotional Memories","created_at":"2017-08-24T05:15:06.790-07:00","url":"https://www.academia.edu/34338062/Synaptic_Plasticity_Engrams_and_Network_Oscillations_in_Amygdala_Circuits_for_Storage_and_Retrieval_of_Emotional_Memories?f_ri=12950","dom_id":"work_34338062","summary":"The neuronal circuits of the basolateral amygdala (BLA) are crucial for acquisition, consolidation, retrieval, and extinction of associative emotional memories. Synaptic plasticity in BLA neurons is essential for asso-ciative emotional learning and is a candidate mechanism through which subsets of BLA neurons (commonly termed ''engram'') are recruited during learning and reactivated during memory retrieval. In parallel, synchronous oscillations in the theta and gamma bands between the BLA and interconnected structures have been shown to occur during consolidation and retrieval of emotional memories. Understanding how these cellular and network phenomena interact is vital to decipher the roles of emotional memory formation and storage in the healthy and pathological brain. Here, we review data on synaptic plasticity, engrams, and network oscillations in the rodent BLA. We explore mechanisms through which synaptic plasticity, engrams, and long-range synchrony might be interconnected.","downloadable_attachments":[{"id":54232644,"asset_id":34338062,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":40867177,"first_name":"Marco","last_name":"Bocchio","domain_name":"univ-amu","page_name":"MarcoBocchio","display_name":"Marco Bocchio","profile_url":"https://univ-amu.academia.edu/MarcoBocchio?f_ri=12950","photo":"/images/s65_no_pic.png"},{"id":67589776,"first_name":"Marco","last_name":"Capogna","domain_name":"independent","page_name":"MarcoCapogna","display_name":"Marco Capogna","profile_url":"https://independent.academia.edu/MarcoCapogna?f_ri=12950","photo":"/images/s65_no_pic.png"}],"research_interests":[{"id":254,"name":"Emotion","url":"https://www.academia.edu/Documents/in/Emotion?f_ri=12950","nofollow":true},{"id":3931,"name":"Fear","url":"https://www.academia.edu/Documents/in/Fear?f_ri=12950","nofollow":true},{"id":12950,"name":"Neural Oscillations","url":"https://www.academia.edu/Documents/in/Neural_Oscillations?f_ri=12950","nofollow":true},{"id":46858,"name":"Memory","url":"https://www.academia.edu/Documents/in/Memory?f_ri=12950","nofollow":true},{"id":49021,"name":"Reward","url":"https://www.academia.edu/Documents/in/Reward?f_ri=12950"},{"id":159962,"name":"Amygdala","url":"https://www.academia.edu/Documents/in/Amygdala?f_ri=12950"},{"id":182362,"name":"Neural Circuits and Behavior","url":"https://www.academia.edu/Documents/in/Neural_Circuits_and_Behavior?f_ri=12950"},{"id":400585,"name":"Engram","url":"https://www.academia.edu/Documents/in/Engram?f_ri=12950"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_12205725" data-work_id="12205725" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/12205725/Dynamic_network_communication_as_a_unifying_neural_basis_for_cognition_development_aging_and_disease">Dynamic network communication as a unifying neural basis for cognition, development, aging, and disease</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">Perception, cognition, and social interaction depend upon coordinated neural activity. This coordination operates within noisy, overlapping, and distributed neural networks operating at multiple timescales. These networks are built upon a... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_12205725" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">Perception, cognition, and social interaction depend upon coordinated neural activity. This coordination operates within noisy, overlapping, and distributed neural networks operating at multiple timescales. These networks are built upon a structural scaffolding with intrinsic neuroplasticity that changes with development, aging, disease, and personal experience. In this paper we begin from the perspective that successful interregional communication relies upon the transient synchronization between distinct low frequency (<80 Hz) oscillations, allowing for brief windows of communication via phase-coordinated local neuronal spiking. From this, we construct a theoretical framework for dynamic network communication, arguing that these networks reflect a balance between oscillatory coupling and local population spiking activity, and that these two levels of activity interact. We theorize that when oscillatory coupling is too strong, spike timing within the local neuronal population becomes too synchronous; when oscillatory coupling is too weak, spike timing is too disorganized. Each results in specific disruptions to neural communication. These alterations in communication dynamics may underlie cognitive changes associated with healthy development and aging, in addition to neurological and psychiatric disorders. A number of neurological and psychiatric disorders— including Parkinson’s disease, autism, depression, schizophrenia, and anxiety—are associated with abnormalities in oscillatory activity. Although aging, psychiatric and neurological disease, and experience differ in the biological changes to structural grey or white matter, neurotransmission, and gene expression, our framework suggests that any resultant cognitive and behavioral changes in normal or disordered states, or their treatment, is a product of how these physical processes affect dynamic network communication.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/12205725" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="b17e3c4fc8af761b573368d487275b9f" rel="nofollow" data-download="{"attachment_id":37489103,"asset_id":12205725,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/37489103/download_file?st=MTczNzI5NjIxNCw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="4849622" href="https://ucsd.academia.edu/BradleyVoytek">Bradley Voytek</a><script data-card-contents-for-user="4849622" type="text/json">{"id":4849622,"first_name":"Bradley","last_name":"Voytek","domain_name":"ucsd","page_name":"BradleyVoytek","display_name":"Bradley Voytek","profile_url":"https://ucsd.academia.edu/BradleyVoytek?f_ri=12950","photo":"https://0.academia-photos.com/4849622/2474932/2875354/s65_bradley.voytek.jpeg"}</script></span></span></li><li class="js-paper-rank-work_12205725 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="12205725"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 12205725, container: ".js-paper-rank-work_12205725", }); });</script></li><li class="js-percentile-work_12205725 InlineList-item InlineList-item--bordered hidden u-tcGrayDark"><span class="percentile-widget hidden"><span class="u-mr2x percentile-widget" style="display: none">•</span><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 12205725; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-percentile-work_12205725"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></li><li class="js-view-count-work_12205725 InlineList-item InlineList-item--bordered hidden"><div><span><span class="js-view-count view-count u-mr2x" data-work-id="12205725"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 12205725; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=12205725]").text(description); $(".js-view-count-work_12205725").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_12205725").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="12205725"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">7</a>  </div><span class="InlineList-item-text u-textTruncate u-pl9x"><a class="InlineList-item-text" data-has-card-for-ri="161" rel="nofollow" href="https://www.academia.edu/Documents/in/Neuroscience">Neuroscience</a>, <script data-card-contents-for-ri="161" type="text/json">{"id":161,"name":"Neuroscience","url":"https://www.academia.edu/Documents/in/Neuroscience?f_ri=12950","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="3227" rel="nofollow" href="https://www.academia.edu/Documents/in/Schizophrenia">Schizophrenia</a>, <script data-card-contents-for-ri="3227" type="text/json">{"id":3227,"name":"Schizophrenia","url":"https://www.academia.edu/Documents/in/Schizophrenia?f_ri=12950","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="4241" rel="nofollow" href="https://www.academia.edu/Documents/in/Parkinsons_Disease">Parkinson's Disease</a>, <script data-card-contents-for-ri="4241" type="text/json">{"id":4241,"name":"Parkinson's Disease","url":"https://www.academia.edu/Documents/in/Parkinsons_Disease?f_ri=12950","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="4270" rel="nofollow" href="https://www.academia.edu/Documents/in/Autism_Spectrum_Disorders">Autism Spectrum Disorders</a><script data-card-contents-for-ri="4270" type="text/json">{"id":4270,"name":"Autism Spectrum Disorders","url":"https://www.academia.edu/Documents/in/Autism_Spectrum_Disorders?f_ri=12950","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=12205725]'), work: {"id":12205725,"title":"Dynamic network communication as a unifying neural basis for cognition, development, aging, and disease","created_at":"2015-05-03T07:33:36.254-07:00","url":"https://www.academia.edu/12205725/Dynamic_network_communication_as_a_unifying_neural_basis_for_cognition_development_aging_and_disease?f_ri=12950","dom_id":"work_12205725","summary":"Perception, cognition, and social interaction depend upon coordinated neural activity. This coordination operates within noisy, overlapping, and distributed neural networks operating at multiple timescales. These networks are built upon a structural scaffolding with intrinsic neuroplasticity that changes with development, aging, disease, and personal experience. In this paper we begin from the perspective that successful interregional communication relies upon the transient synchronization between distinct low frequency (\u003c80 Hz) oscillations, allowing for brief windows of communication via phase-coordinated local neuronal spiking. From this, we construct a theoretical framework for dynamic network communication, arguing that these networks reflect a balance between oscillatory coupling and local population spiking activity, and that these two levels of activity interact. We theorize that when oscillatory coupling is too strong, spike timing within the local neuronal population becomes too synchronous; when oscillatory coupling is too weak, spike timing is too disorganized. Each results in specific disruptions to neural communication. These alterations in communication dynamics may underlie cognitive changes associated with healthy development and aging, in addition to neurological and psychiatric disorders. A number of neurological and psychiatric disorders— including Parkinson’s disease, autism, depression, schizophrenia, and anxiety—are associated with abnormalities in oscillatory activity. Although aging, psychiatric and neurological disease, and experience differ in the biological changes to structural grey or white matter, neurotransmission, and gene expression, our framework suggests that any resultant cognitive and behavioral changes in normal or disordered states, or their treatment, is a product of how these physical processes affect dynamic network communication.","downloadable_attachments":[{"id":37489103,"asset_id":12205725,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":4849622,"first_name":"Bradley","last_name":"Voytek","domain_name":"ucsd","page_name":"BradleyVoytek","display_name":"Bradley Voytek","profile_url":"https://ucsd.academia.edu/BradleyVoytek?f_ri=12950","photo":"https://0.academia-photos.com/4849622/2474932/2875354/s65_bradley.voytek.jpeg"}],"research_interests":[{"id":161,"name":"Neuroscience","url":"https://www.academia.edu/Documents/in/Neuroscience?f_ri=12950","nofollow":true},{"id":3227,"name":"Schizophrenia","url":"https://www.academia.edu/Documents/in/Schizophrenia?f_ri=12950","nofollow":true},{"id":4241,"name":"Parkinson's Disease","url":"https://www.academia.edu/Documents/in/Parkinsons_Disease?f_ri=12950","nofollow":true},{"id":4270,"name":"Autism Spectrum Disorders","url":"https://www.academia.edu/Documents/in/Autism_Spectrum_Disorders?f_ri=12950","nofollow":true},{"id":5451,"name":"Computational Neuroscience","url":"https://www.academia.edu/Documents/in/Computational_Neuroscience?f_ri=12950"},{"id":12950,"name":"Neural Oscillations","url":"https://www.academia.edu/Documents/in/Neural_Oscillations?f_ri=12950"},{"id":21548,"name":"Cognitive Neuroscience","url":"https://www.academia.edu/Documents/in/Cognitive_Neuroscience?f_ri=12950"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_40148126" data-work_id="40148126" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/40148126/Why_Brain_Oscillations_Are_Improving_Our_Understanding_of_Language">Why Brain Oscillations Are Improving Our Understanding of Language</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">We explore the potential that brain oscillations have for improving our understanding of how language develops, is processed in the brain, and initially evolved in our species. The different synchronization patterns of brain rhythms can... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_40148126" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">We explore the potential that brain oscillations have for improving our understanding of how language develops, is processed in the brain, and initially evolved in our species. The different synchronization patterns of brain rhythms can account for different perceptual and cognitive functions, and we argue that this includes language. We aim to address six distinct questions-the What, How, Where, Who, Why, and When questions-pertaining to oscillatory investigations of language. Language deficits found in clinical conditions like autism, schizophrenia and dyslexia can be satisfactorily construed in terms of an abnormal, disorder-specific pattern of brain rhythmicity. Lastly, an eco-evo-devo approach to language is defended with explicit reference to brain oscillations, embracing a framework that considers language evolution to be the result of a changing environment surrounding developmental paths of the primate brain.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/40148126" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="c3dd81decd2a2362bdf9fdb658f50832" rel="nofollow" data-download="{"attachment_id":60365844,"asset_id":40148126,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/60365844/download_file?st=MTczNzI5NjIxNCw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="1267216" href="https://tmc.academia.edu/ElliotMurphy">Elliot Murphy</a><script data-card-contents-for-user="1267216" type="text/json">{"id":1267216,"first_name":"Elliot","last_name":"Murphy","domain_name":"tmc","page_name":"ElliotMurphy","display_name":"Elliot Murphy","profile_url":"https://tmc.academia.edu/ElliotMurphy?f_ri=12950","photo":"https://0.academia-photos.com/1267216/763325/39030425/s65_elliot.murphy.jpeg"}</script></span></span></li><li class="js-paper-rank-work_40148126 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="40148126"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 40148126, container: ".js-paper-rank-work_40148126", }); });</script></li><li class="js-percentile-work_40148126 InlineList-item InlineList-item--bordered hidden u-tcGrayDark"><span class="percentile-widget hidden"><span class="u-mr2x percentile-widget" style="display: none">•</span><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 40148126; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-percentile-work_40148126"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></li><li class="js-view-count-work_40148126 InlineList-item InlineList-item--bordered hidden"><div><span><span class="js-view-count view-count u-mr2x" data-work-id="40148126"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 40148126; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=40148126]").text(description); $(".js-view-count-work_40148126").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_40148126").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="40148126"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">20</a>  </div><span class="InlineList-item-text u-textTruncate u-pl10x"><a class="InlineList-item-text" data-has-card-for-ri="155" rel="nofollow" href="https://www.academia.edu/Documents/in/Evolutionary_Biology">Evolutionary Biology</a>, <script data-card-contents-for-ri="155" type="text/json">{"id":155,"name":"Evolutionary Biology","url":"https://www.academia.edu/Documents/in/Evolutionary_Biology?f_ri=12950","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="156" rel="nofollow" href="https://www.academia.edu/Documents/in/Genetics">Genetics</a>, <script data-card-contents-for-ri="156" type="text/json">{"id":156,"name":"Genetics","url":"https://www.academia.edu/Documents/in/Genetics?f_ri=12950","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="161" rel="nofollow" href="https://www.academia.edu/Documents/in/Neuroscience">Neuroscience</a>, <script data-card-contents-for-ri="161" type="text/json">{"id":161,"name":"Neuroscience","url":"https://www.academia.edu/Documents/in/Neuroscience?f_ri=12950","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="165" rel="nofollow" href="https://www.academia.edu/Documents/in/Pathology">Pathology</a><script data-card-contents-for-ri="165" type="text/json">{"id":165,"name":"Pathology","url":"https://www.academia.edu/Documents/in/Pathology?f_ri=12950","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=40148126]'), work: {"id":40148126,"title":"Why Brain Oscillations Are Improving Our Understanding of Language","created_at":"2019-08-22T09:32:38.551-07:00","url":"https://www.academia.edu/40148126/Why_Brain_Oscillations_Are_Improving_Our_Understanding_of_Language?f_ri=12950","dom_id":"work_40148126","summary":"We explore the potential that brain oscillations have for improving our understanding of how language develops, is processed in the brain, and initially evolved in our species. The different synchronization patterns of brain rhythms can account for different perceptual and cognitive functions, and we argue that this includes language. We aim to address six distinct questions-the What, How, Where, Who, Why, and When questions-pertaining to oscillatory investigations of language. Language deficits found in clinical conditions like autism, schizophrenia and dyslexia can be satisfactorily construed in terms of an abnormal, disorder-specific pattern of brain rhythmicity. Lastly, an eco-evo-devo approach to language is defended with explicit reference to brain oscillations, embracing a framework that considers language evolution to be the result of a changing environment surrounding developmental paths of the primate brain.","downloadable_attachments":[{"id":60365844,"asset_id":40148126,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":1267216,"first_name":"Elliot","last_name":"Murphy","domain_name":"tmc","page_name":"ElliotMurphy","display_name":"Elliot Murphy","profile_url":"https://tmc.academia.edu/ElliotMurphy?f_ri=12950","photo":"https://0.academia-photos.com/1267216/763325/39030425/s65_elliot.murphy.jpeg"}],"research_interests":[{"id":155,"name":"Evolutionary Biology","url":"https://www.academia.edu/Documents/in/Evolutionary_Biology?f_ri=12950","nofollow":true},{"id":156,"name":"Genetics","url":"https://www.academia.edu/Documents/in/Genetics?f_ri=12950","nofollow":true},{"id":161,"name":"Neuroscience","url":"https://www.academia.edu/Documents/in/Neuroscience?f_ri=12950","nofollow":true},{"id":165,"name":"Pathology","url":"https://www.academia.edu/Documents/in/Pathology?f_ri=12950","nofollow":true},{"id":221,"name":"Psychology","url":"https://www.academia.edu/Documents/in/Psychology?f_ri=12950"},{"id":252,"name":"Developmental Psychology","url":"https://www.academia.edu/Documents/in/Developmental_Psychology?f_ri=12950"},{"id":1200,"name":"Languages and Linguistics","url":"https://www.academia.edu/Documents/in/Languages_and_Linguistics?f_ri=12950"},{"id":2349,"name":"Semantics","url":"https://www.academia.edu/Documents/in/Semantics?f_ri=12950"},{"id":3227,"name":"Schizophrenia","url":"https://www.academia.edu/Documents/in/Schizophrenia?f_ri=12950"},{"id":3979,"name":"English language","url":"https://www.academia.edu/Documents/in/English_language?f_ri=12950"},{"id":4270,"name":"Autism Spectrum Disorders","url":"https://www.academia.edu/Documents/in/Autism_Spectrum_Disorders?f_ri=12950"},{"id":6671,"name":"Syntax","url":"https://www.academia.edu/Documents/in/Syntax?f_ri=12950"},{"id":7710,"name":"Biology","url":"https://www.academia.edu/Documents/in/Biology?f_ri=12950"},{"id":10882,"name":"Evolution","url":"https://www.academia.edu/Documents/in/Evolution?f_ri=12950"},{"id":12950,"name":"Neural Oscillations","url":"https://www.academia.edu/Documents/in/Neural_Oscillations?f_ri=12950"},{"id":15674,"name":"Linguistics","url":"https://www.academia.edu/Documents/in/Linguistics?f_ri=12950"},{"id":23638,"name":"Specific Language Impairment","url":"https://www.academia.edu/Documents/in/Specific_Language_Impairment?f_ri=12950"},{"id":25052,"name":"Dyslexia","url":"https://www.academia.edu/Documents/in/Dyslexia?f_ri=12950"},{"id":25804,"name":"Neurobiology","url":"https://www.academia.edu/Documents/in/Neurobiology?f_ri=12950"},{"id":31879,"name":"Developmental dyslexia","url":"https://www.academia.edu/Documents/in/Developmental_dyslexia?f_ri=12950"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_10525873" data-work_id="10525873" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/10525873/Cortical_thinning_mediates_age_related_changes_in_NREM_sleep_oscillations_during_adulthood">Cortical thinning mediates age-related changes in NREM sleep oscillations during adulthood</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">Objectives: Magnetic resonance imaging (MRI) studies showed that grey matter integrity is an important determinant of slow waves (SW) properties in young subjects. During aging, SWs become scarce and their amplitude decreases mostly in... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_10525873" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">Objectives: Magnetic resonance imaging (MRI) studies showed that grey matter integrity is an important determinant of slow waves (SW) properties in young subjects. During aging, SWs become scarce and their amplitude decreases mostly in frontal regions, where cortical thinning is also predominant. We sought to assess the role of cortical thinning in age-related changes in SW properties. <br />Methods: Polysomnography was recorded in 30 young(20-30 y/o; 16 men) and 33 middle-aged(50-70 y/o; 15 men) subjects. Mean SW density and amplitude between pairs of frontal electrodes (F3-F4) was calculated for all-night non-rapid-eye-movement sleep. Subjects underwent a brain MRI, and cortical thickness (CT) was calculated. Mediation analyses were performed to investigate the role of cortical thinning in the age-related changes in SW.<br />Results: Compared to the young, middle-aged subjects showed lower SW density and amplitude (p< 0.05). Controlling for the effects of age, SW density was associated with CT in fronto-temporal gyri, while SW amplitude was associated with CT in fronto-parieto-occipital gyri (p< 0.05-corrected). Mediation analyses and effect size measures showed that thinning of the precentral and infero-temporal gyri explained a large proportion of the age-effect on SW density(K2: 0.44), while thinning of the precuneus explained a small proportion of the age-effect on SW amplitude(K2:0.22). <br />Conclusions: Our results shows that thinning of specific cortical regions are involved in SW changes during adulthood. Since these regions are involved in SW generation and propagation, future studies should assess how cortical and subcortical connectivity influences SW propagation during aging.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/10525873" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="4690e5c53b950f916fd25f7813299af6" rel="nofollow" data-download="{"attachment_id":36521146,"asset_id":10525873,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/36521146/download_file?st=MTczNzI5NjIxNCw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="4365615" href="https://umontreal.academia.edu/JonathanDub%C3%A9">Jonathan Dubé</a><script data-card-contents-for-user="4365615" type="text/json">{"id":4365615,"first_name":"Jonathan","last_name":"Dubé","domain_name":"umontreal","page_name":"JonathanDubé","display_name":"Jonathan Dubé","profile_url":"https://umontreal.academia.edu/JonathanDub%C3%A9?f_ri=12950","photo":"https://0.academia-photos.com/4365615/1761773/4236595/s65_jonathan.dub_.jpg"}</script></span></span></li><li class="js-paper-rank-work_10525873 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="10525873"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 10525873, container: ".js-paper-rank-work_10525873", }); });</script></li><li class="js-percentile-work_10525873 InlineList-item InlineList-item--bordered hidden u-tcGrayDark"><span class="percentile-widget hidden"><span class="u-mr2x percentile-widget" style="display: none">•</span><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 10525873; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-percentile-work_10525873"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></li><li class="js-view-count-work_10525873 InlineList-item InlineList-item--bordered hidden"><div><span><span class="js-view-count view-count u-mr2x" data-work-id="10525873"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 10525873; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=10525873]").text(description); $(".js-view-count-work_10525873").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_10525873").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="10525873"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">6</a>  </div><span class="InlineList-item-text u-textTruncate u-pl9x"><a class="InlineList-item-text" data-has-card-for-ri="161" rel="nofollow" href="https://www.academia.edu/Documents/in/Neuroscience">Neuroscience</a>, <script data-card-contents-for-ri="161" type="text/json">{"id":161,"name":"Neuroscience","url":"https://www.academia.edu/Documents/in/Neuroscience?f_ri=12950","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="6200" rel="nofollow" href="https://www.academia.edu/Documents/in/Magnetic_Resonance_Imaging">Magnetic Resonance Imaging</a>, <script data-card-contents-for-ri="6200" type="text/json">{"id":6200,"name":"Magnetic Resonance Imaging","url":"https://www.academia.edu/Documents/in/Magnetic_Resonance_Imaging?f_ri=12950","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="6791" rel="nofollow" href="https://www.academia.edu/Documents/in/Aging">Aging</a>, <script data-card-contents-for-ri="6791" type="text/json">{"id":6791,"name":"Aging","url":"https://www.academia.edu/Documents/in/Aging?f_ri=12950","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="10402" rel="nofollow" href="https://www.academia.edu/Documents/in/EEG">EEG</a><script data-card-contents-for-ri="10402" type="text/json">{"id":10402,"name":"EEG","url":"https://www.academia.edu/Documents/in/EEG?f_ri=12950","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=10525873]'), work: {"id":10525873,"title":"Cortical thinning mediates age-related changes in NREM sleep oscillations during adulthood","created_at":"2015-02-04T20:36:50.357-08:00","url":"https://www.academia.edu/10525873/Cortical_thinning_mediates_age_related_changes_in_NREM_sleep_oscillations_during_adulthood?f_ri=12950","dom_id":"work_10525873","summary":"Objectives: Magnetic resonance imaging (MRI) studies showed that grey matter integrity is an important determinant of slow waves (SW) properties in young subjects. During aging, SWs become scarce and their amplitude decreases mostly in frontal regions, where cortical thinning is also predominant. We sought to assess the role of cortical thinning in age-related changes in SW properties. \nMethods: Polysomnography was recorded in 30 young(20-30 y/o; 16 men) and 33 middle-aged(50-70 y/o; 15 men) subjects. Mean SW density and amplitude between pairs of frontal electrodes (F3-F4) was calculated for all-night non-rapid-eye-movement sleep. Subjects underwent a brain MRI, and cortical thickness (CT) was calculated. Mediation analyses were performed to investigate the role of cortical thinning in the age-related changes in SW.\nResults: Compared to the young, middle-aged subjects showed lower SW density and amplitude (p\u003c 0.05). Controlling for the effects of age, SW density was associated with CT in fronto-temporal gyri, while SW amplitude was associated with CT in fronto-parieto-occipital gyri (p\u003c 0.05-corrected). Mediation analyses and effect size measures showed that thinning of the precentral and infero-temporal gyri explained a large proportion of the age-effect on SW density(K2: 0.44), while thinning of the precuneus explained a small proportion of the age-effect on SW amplitude(K2:0.22). \nConclusions: Our results shows that thinning of specific cortical regions are involved in SW changes during adulthood. Since these regions are involved in SW generation and propagation, future studies should assess how cortical and subcortical connectivity influences SW propagation during aging.","downloadable_attachments":[{"id":36521146,"asset_id":10525873,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":4365615,"first_name":"Jonathan","last_name":"Dubé","domain_name":"umontreal","page_name":"JonathanDubé","display_name":"Jonathan Dubé","profile_url":"https://umontreal.academia.edu/JonathanDub%C3%A9?f_ri=12950","photo":"https://0.academia-photos.com/4365615/1761773/4236595/s65_jonathan.dub_.jpg"}],"research_interests":[{"id":161,"name":"Neuroscience","url":"https://www.academia.edu/Documents/in/Neuroscience?f_ri=12950","nofollow":true},{"id":6200,"name":"Magnetic Resonance Imaging","url":"https://www.academia.edu/Documents/in/Magnetic_Resonance_Imaging?f_ri=12950","nofollow":true},{"id":6791,"name":"Aging","url":"https://www.academia.edu/Documents/in/Aging?f_ri=12950","nofollow":true},{"id":10402,"name":"EEG","url":"https://www.academia.edu/Documents/in/EEG?f_ri=12950","nofollow":true},{"id":12950,"name":"Neural Oscillations","url":"https://www.academia.edu/Documents/in/Neural_Oscillations?f_ri=12950"},{"id":133324,"name":"Sleep","url":"https://www.academia.edu/Documents/in/Sleep?f_ri=12950"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_199299" data-work_id="199299" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/199299/Two_distinct_activity_patterns_of_fast_spiking_interneurons_during_neocortical_UP_states">Two distinct activity patterns of fast-spiking interneurons during neocortical UP states</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">During sleep, neocortical neuronal networks oscillate slowly (<1 Hz) between periods of activity (UP states) and silence (DOWN states). UP states favor the interaction between thalamic-generated spindles (7-14 Hz) and cortically generated... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_199299" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">During sleep, neocortical neuronal networks oscillate slowly (<1 Hz) between periods of activity (UP states) and silence (DOWN states). UP states favor the interaction between thalamic-generated spindles (7-14 Hz) and cortically generated gamma (30-80 Hz) waves. We studied how these three nested oscillations modulate fast-spiking interneuron (FSi) activity in vivo in VGAT-Venus transgenic rats. Our data describe a population of FSi that discharge "early" within UP states and another population that discharge "late." Early FSi tended to be silent during epochs of desynchronization, whereas late FSi were active. We hypothesize that late FSi may be responsible for generating the gamma oscillations associated with cognitive processing during wakefulness. Remarkably, FSi populations were differently modulated by spindle and gamma rhythms. Early FSi were robustly coupled to spindles and always discharged earlier than late FSi within spindle and gamma cycles. The preferred firing phase during spindle and gamma waves was strongly correlated in each cell, suggesting a cross-frequency coupling between oscillations. Our results suggest a precise spatiotemporal pattern of FSi activity during UP states, whereby information rapidly flows between early and late cells, initially promoted by spindles and efficiently extended by local gamma oscillations.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/199299" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="887b1bf93107ae4918e2b9e347516e16" rel="nofollow" data-download="{"attachment_id":531345,"asset_id":199299,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/531345/download_file?st=MTczNzI5NjIxNCw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="44662" href="https://prbb.academia.edu/VickyPuig">Vicky Puig</a><script data-card-contents-for-user="44662" type="text/json">{"id":44662,"first_name":"Vicky","last_name":"Puig","domain_name":"prbb","page_name":"VickyPuig","display_name":"Vicky Puig","profile_url":"https://prbb.academia.edu/VickyPuig?f_ri=12950","photo":"https://0.academia-photos.com/44662/14224/1098715/s65_vicky.puig.jpg"}</script></span></span></li><li class="js-paper-rank-work_199299 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="199299"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 199299, container: ".js-paper-rank-work_199299", }); });</script></li><li class="js-percentile-work_199299 InlineList-item InlineList-item--bordered hidden u-tcGrayDark"><span class="percentile-widget hidden"><span class="u-mr2x percentile-widget" style="display: none">•</span><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 199299; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-percentile-work_199299"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></li><li class="js-view-count-work_199299 InlineList-item InlineList-item--bordered hidden"><div><span><span class="js-view-count view-count u-mr2x" data-work-id="199299"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 199299; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=199299]").text(description); $(".js-view-count-work_199299").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_199299").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="199299"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">7</a>  </div><span class="InlineList-item-text u-textTruncate u-pl9x"><a class="InlineList-item-text" data-has-card-for-ri="1681" rel="nofollow" href="https://www.academia.edu/Documents/in/Decision_Making">Decision Making</a>, <script data-card-contents-for-ri="1681" type="text/json">{"id":1681,"name":"Decision Making","url":"https://www.academia.edu/Documents/in/Decision_Making?f_ri=12950","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="1688" rel="nofollow" href="https://www.academia.edu/Documents/in/Reinforcement_Learning">Reinforcement Learning</a>, <script data-card-contents-for-ri="1688" type="text/json">{"id":1688,"name":"Reinforcement Learning","url":"https://www.academia.edu/Documents/in/Reinforcement_Learning?f_ri=12950","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="12950" rel="nofollow" href="https://www.academia.edu/Documents/in/Neural_Oscillations">Neural Oscillations</a>, <script data-card-contents-for-ri="12950" type="text/json">{"id":12950,"name":"Neural Oscillations","url":"https://www.academia.edu/Documents/in/Neural_Oscillations?f_ri=12950","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="25804" rel="nofollow" href="https://www.academia.edu/Documents/in/Neurobiology">Neurobiology</a><script data-card-contents-for-ri="25804" type="text/json">{"id":25804,"name":"Neurobiology","url":"https://www.academia.edu/Documents/in/Neurobiology?f_ri=12950","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=199299]'), work: {"id":199299,"title":"Two distinct activity patterns of fast-spiking interneurons during neocortical UP states","created_at":"2009-11-27T02:17:11.634-08:00","url":"https://www.academia.edu/199299/Two_distinct_activity_patterns_of_fast_spiking_interneurons_during_neocortical_UP_states?f_ri=12950","dom_id":"work_199299","summary":"During sleep, neocortical neuronal networks oscillate slowly (\u003c1 Hz) between periods of activity (UP states) and silence (DOWN states). UP states favor the interaction between thalamic-generated spindles (7-14 Hz) and cortically generated gamma (30-80 Hz) waves. We studied how these three nested oscillations modulate fast-spiking interneuron (FSi) activity in vivo in VGAT-Venus transgenic rats. Our data describe a population of FSi that discharge \"early\" within UP states and another population that discharge \"late.\" Early FSi tended to be silent during epochs of desynchronization, whereas late FSi were active. We hypothesize that late FSi may be responsible for generating the gamma oscillations associated with cognitive processing during wakefulness. Remarkably, FSi populations were differently modulated by spindle and gamma rhythms. Early FSi were robustly coupled to spindles and always discharged earlier than late FSi within spindle and gamma cycles. The preferred firing phase during spindle and gamma waves was strongly correlated in each cell, suggesting a cross-frequency coupling between oscillations. Our results suggest a precise spatiotemporal pattern of FSi activity during UP states, whereby information rapidly flows between early and late cells, initially promoted by spindles and efficiently extended by local gamma oscillations.","downloadable_attachments":[{"id":531345,"asset_id":199299,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":44662,"first_name":"Vicky","last_name":"Puig","domain_name":"prbb","page_name":"VickyPuig","display_name":"Vicky Puig","profile_url":"https://prbb.academia.edu/VickyPuig?f_ri=12950","photo":"https://0.academia-photos.com/44662/14224/1098715/s65_vicky.puig.jpg"}],"research_interests":[{"id":1681,"name":"Decision Making","url":"https://www.academia.edu/Documents/in/Decision_Making?f_ri=12950","nofollow":true},{"id":1688,"name":"Reinforcement Learning","url":"https://www.academia.edu/Documents/in/Reinforcement_Learning?f_ri=12950","nofollow":true},{"id":12950,"name":"Neural Oscillations","url":"https://www.academia.edu/Documents/in/Neural_Oscillations?f_ri=12950","nofollow":true},{"id":25804,"name":"Neurobiology","url":"https://www.academia.edu/Documents/in/Neurobiology?f_ri=12950","nofollow":true},{"id":28576,"name":"Prefrontal Cortex","url":"https://www.academia.edu/Documents/in/Prefrontal_Cortex?f_ri=12950"},{"id":133270,"name":"Brain State","url":"https://www.academia.edu/Documents/in/Brain_State?f_ri=12950"},{"id":195443,"name":"Fast-spiking interneurons","url":"https://www.academia.edu/Documents/in/Fast-spiking_interneurons?f_ri=12950"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_28961546 coauthored" data-work_id="28961546" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/28961546/T_type_calcium_channel_blocker_Z944_restores_cortical_synchrony_and_thalamocortical_connectivity_in_a_rat_model_of_neuropathic_pain">T-type calcium channel blocker Z944 restores cortical synchrony and thalamocortical connectivity in a rat model of neuropathic pain</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">Oscillations are fundamental to communication between neuronal ensembles. We previously reported that pain in awake rats enhances synchrony in primary somatosensory cortex (S1) and attenuates coherence between S1 and ventral... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_28961546" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">Oscillations are fundamental to communication between neuronal ensembles. We previously reported that pain in awake rats enhances synchrony in primary somatosensory cortex (S1) and attenuates coherence between S1 and ventral posterolateral (VPL) thalamus. Here, we asked whether similar changes occur in anesthetized rats and whether pain modulates phase–amplitude coupling between VPL and S1. We also hypothesized that the suppression of burst firing in VPL using Z944, a novel T-type calcium channel blocker, restores S1 synchrony and thalamocortical connectivity. Local field potentials were recorded from S1 and VPL in anesthetized rats 7 days after sciatic chronic constriction injury (CCI). In rats with CCI, low-frequency (4-12 Hz) synchrony in S1 was enhanced, whereas VPL–S1 coherence and theta–gamma phase–amplitude coupling were attenuated. Moreover, Granger causality showed decreased informational flow from VPL to S1. Systemic or intrathalamic delivery of Z944 to rats with CCI normalized these changes. Systemic Z944 also reversed thermal hyperalgesia and conditioned place preference. These data suggest that pain-induced cortical synchrony and thalamocortical disconnectivity are directly related to burst firing in VPL.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/28961546" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="ddae9a80c8b2c29e1ebc8204015a0b8d" rel="nofollow" data-download="{"attachment_id":49402044,"asset_id":28961546,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/49402044/download_file?st=MTczNzI5NjIxNCw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="2011344" href="https://illinois.academia.edu/BrentCross">Brent Cross</a><script data-card-contents-for-user="2011344" type="text/json">{"id":2011344,"first_name":"Brent","last_name":"Cross","domain_name":"illinois","page_name":"BrentCross","display_name":"Brent Cross","profile_url":"https://illinois.academia.edu/BrentCross?f_ri=12950","photo":"https://0.academia-photos.com/2011344/2252120/2633537/s65_brent.cross.jpg"}</script></span></span><span class="u-displayInlineBlock InlineList-item-text"> and <span class="u-textDecorationUnderline u-clickable InlineList-item-text js-work-more-authors-28961546">+1</span><div class="hidden js-additional-users-28961546"><div><span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a href="https://independent.academia.edu/CarlSaab">Carl Saab</a></span></div></div></span><script>(function(){ var popoverSettings = { el: $('.js-work-more-authors-28961546'), placement: 'bottom', hide_delay: 200, html: true, content: function(){ return $('.js-additional-users-28961546').html(); } } new HoverPopover(popoverSettings); })();</script></li><li class="js-paper-rank-work_28961546 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="28961546"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 28961546, container: ".js-paper-rank-work_28961546", }); });</script></li><li class="js-percentile-work_28961546 InlineList-item InlineList-item--bordered hidden u-tcGrayDark"><span class="percentile-widget hidden"><span class="u-mr2x percentile-widget" style="display: none">•</span><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 28961546; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-percentile-work_28961546"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></li><li class="js-view-count-work_28961546 InlineList-item InlineList-item--bordered hidden"><div><span><span class="js-view-count view-count u-mr2x" data-work-id="28961546"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 28961546; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=28961546]").text(description); $(".js-view-count-work_28961546").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_28961546").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="28961546"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">3</a>  </div><span class="InlineList-item-text u-textTruncate u-pl9x"><a class="InlineList-item-text" data-has-card-for-ri="2584" rel="nofollow" href="https://www.academia.edu/Documents/in/Neuromodulation">Neuromodulation</a>, <script data-card-contents-for-ri="2584" type="text/json">{"id":2584,"name":"Neuromodulation","url":"https://www.academia.edu/Documents/in/Neuromodulation?f_ri=12950","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="4479" rel="nofollow" href="https://www.academia.edu/Documents/in/Chronic_Pain">Chronic Pain</a>, <script data-card-contents-for-ri="4479" type="text/json">{"id":4479,"name":"Chronic Pain","url":"https://www.academia.edu/Documents/in/Chronic_Pain?f_ri=12950","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="12950" rel="nofollow" href="https://www.academia.edu/Documents/in/Neural_Oscillations">Neural Oscillations</a><script data-card-contents-for-ri="12950" type="text/json">{"id":12950,"name":"Neural Oscillations","url":"https://www.academia.edu/Documents/in/Neural_Oscillations?f_ri=12950","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=28961546]'), work: {"id":28961546,"title":"T-type calcium channel blocker Z944 restores cortical synchrony and thalamocortical connectivity in a rat model of neuropathic pain","created_at":"2016-10-06T07:23:44.273-07:00","url":"https://www.academia.edu/28961546/T_type_calcium_channel_blocker_Z944_restores_cortical_synchrony_and_thalamocortical_connectivity_in_a_rat_model_of_neuropathic_pain?f_ri=12950","dom_id":"work_28961546","summary":"Oscillations are fundamental to communication between neuronal ensembles. We previously reported that pain in awake rats enhances synchrony in primary somatosensory cortex (S1) and attenuates coherence between S1 and ventral posterolateral (VPL) thalamus. Here, we asked whether similar changes occur in anesthetized rats and whether pain modulates phase–amplitude coupling between VPL and S1. We also hypothesized that the suppression of burst firing in VPL using Z944, a novel T-type calcium channel blocker, restores S1 synchrony and thalamocortical connectivity. Local field potentials were recorded from S1 and VPL in anesthetized rats 7 days after sciatic chronic constriction injury (CCI). In rats with CCI, low-frequency (4-12 Hz) synchrony in S1 was enhanced, whereas VPL–S1 coherence and theta–gamma phase–amplitude coupling were attenuated. Moreover, Granger causality showed decreased informational flow from VPL to S1. Systemic or intrathalamic delivery of Z944 to rats with CCI normalized these changes. Systemic Z944 also reversed thermal hyperalgesia and conditioned place preference. These data suggest that pain-induced cortical synchrony and thalamocortical disconnectivity are directly related to burst firing in VPL.","downloadable_attachments":[{"id":49402044,"asset_id":28961546,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":2011344,"first_name":"Brent","last_name":"Cross","domain_name":"illinois","page_name":"BrentCross","display_name":"Brent Cross","profile_url":"https://illinois.academia.edu/BrentCross?f_ri=12950","photo":"https://0.academia-photos.com/2011344/2252120/2633537/s65_brent.cross.jpg"},{"id":54920443,"first_name":"Carl","last_name":"Saab","domain_name":"independent","page_name":"CarlSaab","display_name":"Carl Saab","profile_url":"https://independent.academia.edu/CarlSaab?f_ri=12950","photo":"/images/s65_no_pic.png"}],"research_interests":[{"id":2584,"name":"Neuromodulation","url":"https://www.academia.edu/Documents/in/Neuromodulation?f_ri=12950","nofollow":true},{"id":4479,"name":"Chronic Pain","url":"https://www.academia.edu/Documents/in/Chronic_Pain?f_ri=12950","nofollow":true},{"id":12950,"name":"Neural Oscillations","url":"https://www.academia.edu/Documents/in/Neural_Oscillations?f_ri=12950","nofollow":true}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_13527954 coauthored" data-work_id="13527954" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/13527954/A_Bidirectional_Link_between_Brain_Oscillations_and_Geometric_Patterns">A Bidirectional Link between Brain Oscillations and Geometric Patterns</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">Like hallucinogenic drugs, full-field flickering visual stimulation produces regular, geometric hallucinations such as radial or spiral patterns. Computational and theoretical models have revealed that the geometry of these hallucinations... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_13527954" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">Like hallucinogenic drugs, full-field flickering visual stimulation produces regular, geometric hallucinations such as radial or spiral patterns. Computational and theoretical models have revealed that the geometry of these hallucinations can be related to functional neuro-anatomy. However, while experimental evidence links both visual flicker and hallucinogenic drugs to upward and downward modulations of brain oscillatory activity, the exact relation between brain oscillations and geometric hallucinations remains a mystery. Here we demonstrate that, in human observers, this link is bidirectional. The same flicker frequencies that preferentially induced radial (<10 Hz) or spiral (10 –20 Hz) hallucinations in a behavioral experiment involving full-field uniform flicker without any actual shape displayed, also showed selective oscillatory EEG enhancement when observers viewed a genuine static image of a radial or spiral pattern without any flicker. This bidirectional property constrains the possible neuronal events at the origin of visual hallucinations, and further suggests that brain oscillations, which are strictly temporal in nature, could nonetheless act as preferential channels for spatial information.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/13527954" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="0214c475bb607141498c1fb301c705ab" rel="nofollow" data-download="{"attachment_id":38067286,"asset_id":13527954,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/38067286/download_file?st=MTczNzI5NjIxNCw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="27650931" href="https://independent.academia.edu/AntoninoRaffone">Antonino Raffone</a><script data-card-contents-for-user="27650931" type="text/json">{"id":27650931,"first_name":"Antonino","last_name":"Raffone","domain_name":"independent","page_name":"AntoninoRaffone","display_name":"Antonino Raffone","profile_url":"https://independent.academia.edu/AntoninoRaffone?f_ri=12950","photo":"https://0.academia-photos.com/27650931/7863598/8810461/s65_antonino.raffone.jpg"}</script></span></span><span class="u-displayInlineBlock InlineList-item-text"> and <span class="u-textDecorationUnderline u-clickable InlineList-item-text js-work-more-authors-13527954">+1</span><div class="hidden js-additional-users-13527954"><div><span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a href="https://uniroma1.academia.edu/FedericaMauro">Federica Mauro</a></span></div></div></span><script>(function(){ var popoverSettings = { el: $('.js-work-more-authors-13527954'), placement: 'bottom', hide_delay: 200, html: true, content: function(){ return $('.js-additional-users-13527954').html(); } } new HoverPopover(popoverSettings); })();</script></li><li class="js-paper-rank-work_13527954 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="13527954"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 13527954, container: ".js-paper-rank-work_13527954", }); });</script></li><li class="js-percentile-work_13527954 InlineList-item InlineList-item--bordered hidden u-tcGrayDark"><span class="percentile-widget hidden"><span class="u-mr2x percentile-widget" style="display: none">•</span><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 13527954; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-percentile-work_13527954"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></li><li class="js-view-count-work_13527954 InlineList-item InlineList-item--bordered hidden"><div><span><span class="js-view-count view-count u-mr2x" data-work-id="13527954"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 13527954; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=13527954]").text(description); $(".js-view-count-work_13527954").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_13527954").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="13527954"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">5</a>  </div><span class="InlineList-item-text u-textTruncate u-pl9x"><a class="InlineList-item-text" data-has-card-for-ri="10402" rel="nofollow" href="https://www.academia.edu/Documents/in/EEG">EEG</a>, <script data-card-contents-for-ri="10402" type="text/json">{"id":10402,"name":"EEG","url":"https://www.academia.edu/Documents/in/EEG?f_ri=12950","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="12950" rel="nofollow" href="https://www.academia.edu/Documents/in/Neural_Oscillations">Neural Oscillations</a>, <script data-card-contents-for-ri="12950" type="text/json">{"id":12950,"name":"Neural Oscillations","url":"https://www.academia.edu/Documents/in/Neural_Oscillations?f_ri=12950","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="85589" rel="nofollow" href="https://www.academia.edu/Documents/in/Hallucinations">Hallucinations</a>, <script data-card-contents-for-ri="85589" type="text/json">{"id":85589,"name":"Hallucinations","url":"https://www.academia.edu/Documents/in/Hallucinations?f_ri=12950","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="189842" rel="nofollow" href="https://www.academia.edu/Documents/in/Flicker">Flicker</a><script data-card-contents-for-ri="189842" type="text/json">{"id":189842,"name":"Flicker","url":"https://www.academia.edu/Documents/in/Flicker?f_ri=12950","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=13527954]'), work: {"id":13527954,"title":"A Bidirectional Link between Brain Oscillations and Geometric Patterns","created_at":"2015-07-02T01:20:25.529-07:00","url":"https://www.academia.edu/13527954/A_Bidirectional_Link_between_Brain_Oscillations_and_Geometric_Patterns?f_ri=12950","dom_id":"work_13527954","summary":"Like hallucinogenic drugs, full-field flickering visual stimulation produces regular, geometric hallucinations such as radial or spiral patterns. Computational and theoretical models have revealed that the geometry of these hallucinations can be related to functional neuro-anatomy. However, while experimental evidence links both visual flicker and hallucinogenic drugs to upward and downward modulations of brain oscillatory activity, the exact relation between brain oscillations and geometric hallucinations remains a mystery. Here we demonstrate that, in human observers, this link is bidirectional. The same flicker frequencies that preferentially induced radial (\u003c10 Hz) or spiral (10 –20 Hz) hallucinations in a behavioral experiment involving full-field uniform flicker without any actual shape displayed, also showed selective oscillatory EEG enhancement when observers viewed a genuine static image of a radial or spiral pattern without any flicker. This bidirectional property constrains the possible neuronal events at the origin of visual hallucinations, and further suggests that brain oscillations, which are strictly temporal in nature, could nonetheless act as preferential channels for spatial information.","downloadable_attachments":[{"id":38067286,"asset_id":13527954,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":27650931,"first_name":"Antonino","last_name":"Raffone","domain_name":"independent","page_name":"AntoninoRaffone","display_name":"Antonino Raffone","profile_url":"https://independent.academia.edu/AntoninoRaffone?f_ri=12950","photo":"https://0.academia-photos.com/27650931/7863598/8810461/s65_antonino.raffone.jpg"},{"id":24028060,"first_name":"Federica","last_name":"Mauro","domain_name":"uniroma1","page_name":"FedericaMauro","display_name":"Federica Mauro","profile_url":"https://uniroma1.academia.edu/FedericaMauro?f_ri=12950","photo":"https://0.academia-photos.com/24028060/6485545/7334507/s65_federica.mauro.jpg"}],"research_interests":[{"id":10402,"name":"EEG","url":"https://www.academia.edu/Documents/in/EEG?f_ri=12950","nofollow":true},{"id":12950,"name":"Neural Oscillations","url":"https://www.academia.edu/Documents/in/Neural_Oscillations?f_ri=12950","nofollow":true},{"id":85589,"name":"Hallucinations","url":"https://www.academia.edu/Documents/in/Hallucinations?f_ri=12950","nofollow":true},{"id":189842,"name":"Flicker","url":"https://www.academia.edu/Documents/in/Flicker?f_ri=12950","nofollow":true},{"id":1258123,"name":"Geometric Pattern","url":"https://www.academia.edu/Documents/in/Geometric_Pattern?f_ri=12950"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_40106845" data-work_id="40106845" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/40106845/Neurodynamics_of_time_consciousness_An_extensionalist_explanation_of_apparent_motion_and_the_specious_present_via_reentrant_oscillatory_multiplexing">Neurodynamics of time consciousness: An extensionalist explanation of apparent motion and the specious present via reentrant oscillatory multiplexing</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">This paper situates an original model of reentrant oscillatory multiplexing within the philosophy of time consciousness to argue for an extensionalist theory of the specious present. I develop a detailed differential latency model of... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_40106845" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">This paper situates an original model of reentrant oscillatory multiplexing within the philosophy of time consciousness to argue for an extensionalist theory of the specious present. I develop a detailed differential latency model of apparent motion to show how the ordinality of experiential content is isomorphic to the ordinality of relevant brain processes. I argue that the theory presented has resources to account for other key features of the specious present, including the representational discreteness between successive conscious moments as well as the phenomen-ological continuity between them. This work not only shows the plausibility of an extensionalist philosophical theory, it also illustrates the utility of differential latency views in squaring temporal illusions with empirically supported neurodynamics.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/40106845" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="e61e9cbdee67140d0ccad81a1eca76e3" rel="nofollow" data-download="{"attachment_id":60360155,"asset_id":40106845,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/60360155/download_file?st=MTczNzI5NjIxNCw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="8377012" href="https://independent.academia.edu/MatthewPiper4">Matthew S Piper</a><script data-card-contents-for-user="8377012" type="text/json">{"id":8377012,"first_name":"Matthew","last_name":"Piper","domain_name":"independent","page_name":"MatthewPiper4","display_name":"Matthew S Piper","profile_url":"https://independent.academia.edu/MatthewPiper4?f_ri=12950","photo":"https://0.academia-photos.com/8377012/5451548/6216052/s65_matthew.piper.jpg"}</script></span></span></li><li class="js-paper-rank-work_40106845 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="40106845"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 40106845, container: ".js-paper-rank-work_40106845", }); 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I develop a detailed differential latency model of apparent motion to show how the ordinality of experiential content is isomorphic to the ordinality of relevant brain processes. I argue that the theory presented has resources to account for other key features of the specious present, including the representational discreteness between successive conscious moments as well as the phenomen-ological continuity between them. This work not only shows the plausibility of an extensionalist philosophical theory, it also illustrates the utility of differential latency views in squaring temporal illusions with empirically supported neurodynamics. ","downloadable_attachments":[{"id":60360155,"asset_id":40106845,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":8377012,"first_name":"Matthew","last_name":"Piper","domain_name":"independent","page_name":"MatthewPiper4","display_name":"Matthew S Piper","profile_url":"https://independent.academia.edu/MatthewPiper4?f_ri=12950","photo":"https://0.academia-photos.com/8377012/5451548/6216052/s65_matthew.piper.jpg"}],"research_interests":[{"id":12950,"name":"Neural Oscillations","url":"https://www.academia.edu/Documents/in/Neural_Oscillations?f_ri=12950","nofollow":true},{"id":48174,"name":"Temporality","url":"https://www.academia.edu/Documents/in/Temporality?f_ri=12950","nofollow":true},{"id":104314,"name":"Specious Present","url":"https://www.academia.edu/Documents/in/Specious_Present?f_ri=12950","nofollow":true},{"id":1037774,"name":"Temporal Perception","url":"https://www.academia.edu/Documents/in/Temporal_Perception?f_ri=12950","nofollow":true}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_12368771" data-work_id="12368771" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/12368771/Labels_Cognomes_and_Cyclic_Computation_An_Ethological_Perspective">Labels, Cognomes and Cyclic Computation: An Ethological Perspective</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">For the past two decades, it has widely been assumed by linguists that there is a single computational operation, Merge, which is unique to language, distinguishing it from other cognitive domains. The intention of this paper is to... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_12368771" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">For the past two decades, it has widely been assumed by linguists that there is a single computational operation, Merge, which is unique to language, distinguishing it from other cognitive domains. The intention of this paper is to progress the discussion of language evolution in two ways: (i) survey what the ethological record reveals about the uniqueness of the human computational system, and (ii) explore how syntactic theories account for what ethology may determine to be human-specific. It is shown that the operation Label, not Merge, constitutes the evolutionary novelty which distinguishes human language from non-human computational systems; a proposal lending weight to a Weak Continuity Hypothesis and leading to the formation of what is termed Computational Ethology. Some directions for future ethological research are suggested.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/12368771" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="ddc77bebb283872c98c8f40e1dd21043" rel="nofollow" data-download="{"attachment_id":37814020,"asset_id":12368771,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/37814020/download_file?st=MTczNzI5NjIxNCw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="1267216" href="https://tmc.academia.edu/ElliotMurphy">Elliot Murphy</a><script data-card-contents-for-user="1267216" type="text/json">{"id":1267216,"first_name":"Elliot","last_name":"Murphy","domain_name":"tmc","page_name":"ElliotMurphy","display_name":"Elliot Murphy","profile_url":"https://tmc.academia.edu/ElliotMurphy?f_ri=12950","photo":"https://0.academia-photos.com/1267216/763325/39030425/s65_elliot.murphy.jpeg"}</script></span></span></li><li class="js-paper-rank-work_12368771 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="12368771"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 12368771, container: ".js-paper-rank-work_12368771", }); 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The intention of this paper is to progress the discussion of language evolution in two ways: (i) survey what the ethological record reveals about the uniqueness of the human computational system, and (ii) explore how syntactic theories account for what ethology may determine to be human-specific. It is shown that the operation Label, not Merge, constitutes the evolutionary novelty which distinguishes human language from non-human computational systems; a proposal lending weight to a Weak Continuity Hypothesis and leading to the formation of what is termed Computational Ethology. Some directions for future ethological research are suggested. ","downloadable_attachments":[{"id":37814020,"asset_id":12368771,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":1267216,"first_name":"Elliot","last_name":"Murphy","domain_name":"tmc","page_name":"ElliotMurphy","display_name":"Elliot Murphy","profile_url":"https://tmc.academia.edu/ElliotMurphy?f_ri=12950","photo":"https://0.academia-photos.com/1267216/763325/39030425/s65_elliot.murphy.jpeg"}],"research_interests":[{"id":161,"name":"Neuroscience","url":"https://www.academia.edu/Documents/in/Neuroscience?f_ri=12950","nofollow":true},{"id":179,"name":"Ethology","url":"https://www.academia.edu/Documents/in/Ethology?f_ri=12950","nofollow":true},{"id":236,"name":"Cognitive Psychology","url":"https://www.academia.edu/Documents/in/Cognitive_Psychology?f_ri=12950","nofollow":true},{"id":237,"name":"Cognitive Science","url":"https://www.academia.edu/Documents/in/Cognitive_Science?f_ri=12950","nofollow":true},{"id":424,"name":"Automata Theory (Formal Languages)","url":"https://www.academia.edu/Documents/in/Automata_Theory_Formal_Languages_?f_ri=12950"},{"id":772,"name":"Human Evolution","url":"https://www.academia.edu/Documents/in/Human_Evolution?f_ri=12950"},{"id":803,"name":"Philosophy","url":"https://www.academia.edu/Documents/in/Philosophy?f_ri=12950"},{"id":806,"name":"Philosophy of Mind","url":"https://www.academia.edu/Documents/in/Philosophy_of_Mind?f_ri=12950"},{"id":807,"name":"Philosophy Of Language","url":"https://www.academia.edu/Documents/in/Philosophy_Of_Language?f_ri=12950"},{"id":1200,"name":"Languages and Linguistics","url":"https://www.academia.edu/Documents/in/Languages_and_Linguistics?f_ri=12950"},{"id":1207,"name":"Historical Linguistics","url":"https://www.academia.edu/Documents/in/Historical_Linguistics?f_ri=12950"},{"id":3979,"name":"English language","url":"https://www.academia.edu/Documents/in/English_language?f_ri=12950"},{"id":7710,"name":"Biology","url":"https://www.academia.edu/Documents/in/Biology?f_ri=12950"},{"id":8014,"name":"Life Sciences","url":"https://www.academia.edu/Documents/in/Life_Sciences?f_ri=12950"},{"id":12950,"name":"Neural Oscillations","url":"https://www.academia.edu/Documents/in/Neural_Oscillations?f_ri=12950"},{"id":15674,"name":"Linguistics","url":"https://www.academia.edu/Documents/in/Linguistics?f_ri=12950"},{"id":16550,"name":"Cognitive Ethology","url":"https://www.academia.edu/Documents/in/Cognitive_Ethology?f_ri=12950"},{"id":21548,"name":"Cognitive Neuroscience","url":"https://www.academia.edu/Documents/in/Cognitive_Neuroscience?f_ri=12950"},{"id":28991,"name":"Formal Semantics","url":"https://www.academia.edu/Documents/in/Formal_Semantics?f_ri=12950"},{"id":49558,"name":"Evolution of Language","url":"https://www.academia.edu/Documents/in/Evolution_of_Language?f_ri=12950"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_2830423" data-work_id="2830423" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/2830423/Tyukin_I_Steur_E_Nijmeijer_H_Fairhurst_D_Song_I_Semyanov_A_and_van_Leeuwen_C_2009_State_and_parameter_estimation_for_canonical_models_of_neural_oscillators_existence_and_performance_issues_Proc_Cyb_Vol_3_pp_70_99_">Tyukin, I., Steur, E., Nijmeijer, H., Fairhurst, D. Song, I., Semyanov, A., & van Leeuwen, C. (2009). State and parameter estimation for canonical models of neural oscillators: existence and performance issues. Proc Cyb, Vol. 3. (pp. 70-99).</a></div></div><div class="u-pb4x u-mt3x"></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/2830423" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="67fb4890bb28a44dcee8b0511744d7dc" rel="nofollow" data-download="{"attachment_id":30778090,"asset_id":2830423,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/30778090/download_file?st=MTczNzI5NjIxNCw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="122908" href="https://kuleuven.academia.edu/CeesvanLeeuwen">Cees van Leeuwen</a><script data-card-contents-for-user="122908" type="text/json">{"id":122908,"first_name":"Cees","last_name":"van Leeuwen","domain_name":"kuleuven","page_name":"CeesvanLeeuwen","display_name":"Cees van Leeuwen","profile_url":"https://kuleuven.academia.edu/CeesvanLeeuwen?f_ri=12950","photo":"https://0.academia-photos.com/122908/32868/1278135/s65_cees.van_leeuwen.png"}</script></span></span></li><li class="js-paper-rank-work_2830423 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="2830423"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 2830423, container: ".js-paper-rank-work_2830423", }); 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Song, I., Semyanov, A., \u0026 van Leeuwen, C. (2009). State and parameter estimation for canonical models of neural oscillators: existence and performance issues. Proc Cyb, Vol. 3. (pp. 70-99).","created_at":"2013-03-03T19:33:36.941-08:00","url":"https://www.academia.edu/2830423/Tyukin_I_Steur_E_Nijmeijer_H_Fairhurst_D_Song_I_Semyanov_A_and_van_Leeuwen_C_2009_State_and_parameter_estimation_for_canonical_models_of_neural_oscillators_existence_and_performance_issues_Proc_Cyb_Vol_3_pp_70_99_?f_ri=12950","dom_id":"work_2830423","summary":null,"downloadable_attachments":[{"id":30778090,"asset_id":2830423,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":122908,"first_name":"Cees","last_name":"van Leeuwen","domain_name":"kuleuven","page_name":"CeesvanLeeuwen","display_name":"Cees van Leeuwen","profile_url":"https://kuleuven.academia.edu/CeesvanLeeuwen?f_ri=12950","photo":"https://0.academia-photos.com/122908/32868/1278135/s65_cees.van_leeuwen.png"}],"research_interests":[{"id":11598,"name":"Neural Networks","url":"https://www.academia.edu/Documents/in/Neural_Networks?f_ri=12950","nofollow":true},{"id":12950,"name":"Neural Oscillations","url":"https://www.academia.edu/Documents/in/Neural_Oscillations?f_ri=12950","nofollow":true},{"id":83606,"name":"Neural Modelling","url":"https://www.academia.edu/Documents/in/Neural_Modelling?f_ri=12950","nofollow":true}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_44594662" data-work_id="44594662" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/44594662/Hippocampus">Hippocampus</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest">The hippocampus (from the Greek "seahorse") is a brain structure located in the medial temporal lobe that is primarily responsible for mapping spatial relations, temporal sequence information, and the formation of episodic memories.</div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/44594662" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="c28ee2b8b1b8929e47968029edbd27c2" rel="nofollow" data-download="{"attachment_id":65054669,"asset_id":44594662,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/65054669/download_file?st=MTczNzI5NjIxNCw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="10444603" href="https://salk.academia.edu/EricJeffreyLeonardis">Eric Jeffrey Leonardis</a><script data-card-contents-for-user="10444603" type="text/json">{"id":10444603,"first_name":"Eric Jeffrey","last_name":"Leonardis","domain_name":"salk","page_name":"EricJeffreyLeonardis","display_name":"Eric Jeffrey Leonardis","profile_url":"https://salk.academia.edu/EricJeffreyLeonardis?f_ri=12950","photo":"https://0.academia-photos.com/10444603/3173389/38291269/s65_eric_jeffrey.leonardis.jpg"}</script></span></span></li><li class="js-paper-rank-work_44594662 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="44594662"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 44594662, container: ".js-paper-rank-work_44594662", }); 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Oftentimes, oscillatory activity is indexed by mean power density in predefined frequency bands.... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_239376" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">Recent years have seen an explosion of interest in using neural oscillations to characterize the mechanisms supporting cognition and emotion. Oftentimes, oscillatory activity is indexed by mean power density in predefined frequency bands. Some investigators use broad bands originally defined by prominent surface features of the spectrum. Others rely on narrower bands originally defined by spectral factor analysis (SFA). Presently, the robustness and sensitivity of these competing band definitions remains unclear. Here, a Monte Carlo-based SFA strategy was used to decompose the tonic (“resting” or “spontaneous”) electroencephalogram (EEG) into five bands: delta (1–5 Hz), alpha-low (6–9 Hz), alpha-high (10–11 Hz), beta (12–19 Hz), and gamma (N21 Hz). This pattern was consistent across SFA methods, artifact correction/rejection procedures, scalp regions, and samples. Subsequent analyses revealed that SFA failed to deliver enhanced sensitivity; narrow alpha sub-bands proved no more sensitive than the classical broadband to individual differences in temperament or mean differences in task-induced activation. Other analyses suggested that residual ocular and muscular artifact was the dominant source of activity during quiescence in the delta and gamma bands. This was observed following threshold-based artifact rejection or independent component analysis (ICA)- based artifact correction, indicating that such procedures do not necessarily confer adequate protection. Collectively, these findings highlight the limitations of several commonly used EEG procedures and underscore the necessity of routinely performing exploratory data analyses, particularly data visualization, prior to hypothesis testing. They also suggest the potential benefits of using techniques other than SFA for interrogating high-dimensional EEG datasets in the frequency or time–frequency (event-related spectral perturbation, event-related synchronization/desynchronization) domains. KEY WORDS: principal components analysis (PCA); exploratory factor analysis (EFA); blind source separation (BSS); resting neural activity; resting EEG; frontal alpha asymmetry; frontal EEG asymmetry.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/239376" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="14535b96c83a8c75a2743f1b2cbe77b6" rel="nofollow" data-download="{"attachment_id":958662,"asset_id":239376,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/958662/download_file?st=MTczNzI5NjIxNCw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="81973" href="https://umcp.academia.edu/AlexanderShackman">Alexander Shackman</a><script data-card-contents-for-user="81973" type="text/json">{"id":81973,"first_name":"Alexander","last_name":"Shackman","domain_name":"umcp","page_name":"AlexanderShackman","display_name":"Alexander Shackman","profile_url":"https://umcp.academia.edu/AlexanderShackman?f_ri=12950","photo":"https://0.academia-photos.com/81973/22792/12115927/s65_alexander.shackman.png"}</script></span></span></li><li class="js-paper-rank-work_239376 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="239376"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 239376, container: ".js-paper-rank-work_239376", }); 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$(".js-view-count[data-work-id=239376]").text(description); $(".js-view-count-work_239376").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_239376").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="239376"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">41</a>  </div><span class="InlineList-item-text u-textTruncate u-pl10x"><a class="InlineList-item-text" data-has-card-for-ri="161" rel="nofollow" href="https://www.academia.edu/Documents/in/Neuroscience">Neuroscience</a>, <script data-card-contents-for-ri="161" type="text/json">{"id":161,"name":"Neuroscience","url":"https://www.academia.edu/Documents/in/Neuroscience?f_ri=12950","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="221" rel="nofollow" href="https://www.academia.edu/Documents/in/Psychology">Psychology</a>, <script data-card-contents-for-ri="221" type="text/json">{"id":221,"name":"Psychology","url":"https://www.academia.edu/Documents/in/Psychology?f_ri=12950","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="254" rel="nofollow" href="https://www.academia.edu/Documents/in/Emotion">Emotion</a>, <script data-card-contents-for-ri="254" type="text/json">{"id":254,"name":"Emotion","url":"https://www.academia.edu/Documents/in/Emotion?f_ri=12950","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="1026" rel="nofollow" href="https://www.academia.edu/Documents/in/Psychophysiology">Psychophysiology</a><script data-card-contents-for-ri="1026" type="text/json">{"id":1026,"name":"Psychophysiology","url":"https://www.academia.edu/Documents/in/Psychophysiology?f_ri=12950","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=239376]'), work: {"id":239376,"title":"Identifying robust and sensitive frequency bands for interrogating neural oscillations","created_at":"2010-05-25T07:03:48.039-07:00","url":"https://www.academia.edu/239376/Identifying_robust_and_sensitive_frequency_bands_for_interrogating_neural_oscillations?f_ri=12950","dom_id":"work_239376","summary":"Recent years have seen an explosion of interest in using neural oscillations to characterize the mechanisms supporting cognition and emotion. Oftentimes, oscillatory activity is indexed by mean power density in predefined frequency bands. Some investigators use broad bands originally defined by prominent surface features of the spectrum. Others rely on narrower bands originally defined by spectral factor analysis (SFA). Presently, the robustness and sensitivity of these competing band definitions remains unclear. Here, a Monte Carlo-based SFA strategy was used to decompose the tonic (“resting” or “spontaneous”) electroencephalogram (EEG) into five bands: delta (1–5 Hz), alpha-low (6–9 Hz), alpha-high (10–11 Hz), beta (12–19 Hz), and gamma (N21 Hz). This pattern was consistent across SFA methods, artifact correction/rejection procedures, scalp regions, and samples. Subsequent analyses revealed that SFA failed to deliver enhanced sensitivity; narrow alpha sub-bands proved no more sensitive than the classical broadband to individual differences in temperament or mean differences in task-induced activation. Other analyses suggested that residual ocular and muscular artifact was the dominant source of activity during quiescence in the delta and gamma bands. This was observed following threshold-based artifact rejection or independent component analysis (ICA)- based artifact correction, indicating that such procedures do not necessarily confer adequate protection. Collectively, these findings highlight the limitations of several commonly used EEG procedures and underscore the necessity of routinely performing exploratory data analyses, particularly data visualization, prior to hypothesis testing. They also suggest the potential benefits of using techniques other than SFA for interrogating high-dimensional EEG datasets in the frequency or time–frequency (event-related spectral perturbation, event-related synchronization/desynchronization) domains. KEY WORDS: principal components analysis (PCA); exploratory factor analysis (EFA); blind source separation (BSS); resting neural activity; resting EEG; frontal alpha asymmetry; frontal EEG asymmetry.","downloadable_attachments":[{"id":958662,"asset_id":239376,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":81973,"first_name":"Alexander","last_name":"Shackman","domain_name":"umcp","page_name":"AlexanderShackman","display_name":"Alexander Shackman","profile_url":"https://umcp.academia.edu/AlexanderShackman?f_ri=12950","photo":"https://0.academia-photos.com/81973/22792/12115927/s65_alexander.shackman.png"}],"research_interests":[{"id":161,"name":"Neuroscience","url":"https://www.academia.edu/Documents/in/Neuroscience?f_ri=12950","nofollow":true},{"id":221,"name":"Psychology","url":"https://www.academia.edu/Documents/in/Psychology?f_ri=12950","nofollow":true},{"id":254,"name":"Emotion","url":"https://www.academia.edu/Documents/in/Emotion?f_ri=12950","nofollow":true},{"id":1026,"name":"Psychophysiology","url":"https://www.academia.edu/Documents/in/Psychophysiology?f_ri=12950","nofollow":true},{"id":2007,"name":"Electrophysiology","url":"https://www.academia.edu/Documents/in/Electrophysiology?f_ri=12950"},{"id":2599,"name":"Psychometrics","url":"https://www.academia.edu/Documents/in/Psychometrics?f_ri=12950"},{"id":9088,"name":"Affective Neuroscience","url":"https://www.academia.edu/Documents/in/Affective_Neuroscience?f_ri=12950"},{"id":10402,"name":"EEG","url":"https://www.academia.edu/Documents/in/EEG?f_ri=12950"},{"id":12950,"name":"Neural Oscillations","url":"https://www.academia.edu/Documents/in/Neural_Oscillations?f_ri=12950"},{"id":17258,"name":"Blind Source Separation","url":"https://www.academia.edu/Documents/in/Blind_Source_Separation?f_ri=12950"},{"id":18044,"name":"Psychometrics (Research Methodology)","url":"https://www.academia.edu/Documents/in/Psychometrics_Research_Methodology_?f_ri=12950"},{"id":21548,"name":"Cognitive Neuroscience","url":"https://www.academia.edu/Documents/in/Cognitive_Neuroscience?f_ri=12950"},{"id":30048,"name":"Individual Differences","url":"https://www.academia.edu/Documents/in/Individual_Differences?f_ri=12950"},{"id":36002,"name":"EEG/MEG Source Localization","url":"https://www.academia.edu/Documents/in/EEG_MEG_Source_Localization?f_ri=12950"},{"id":42362,"name":"Alpha Oscillations","url":"https://www.academia.edu/Documents/in/Alpha_Oscillations?f_ri=12950"},{"id":46030,"name":"ERP","url":"https://www.academia.edu/Documents/in/ERP?f_ri=12950"},{"id":60585,"name":"Factor analysis","url":"https://www.academia.edu/Documents/in/Factor_analysis?f_ri=12950"},{"id":151407,"name":"Exploratory Factor Analysis","url":"https://www.academia.edu/Documents/in/Exploratory_Factor_Analysis?f_ri=12950"},{"id":155521,"name":"Ica","url":"https://www.academia.edu/Documents/in/Ica?f_ri=12950"},{"id":155522,"name":"Ersp","url":"https://www.academia.edu/Documents/in/Ersp?f_ri=12950"},{"id":174775,"name":"Pca","url":"https://www.academia.edu/Documents/in/Pca?f_ri=12950"},{"id":174778,"name":"Principal Components Analysis","url":"https://www.academia.edu/Documents/in/Principal_Components_Analysis?f_ri=12950"},{"id":174781,"name":"Oscillations","url":"https://www.academia.edu/Documents/in/Oscillations?f_ri=12950"},{"id":174782,"name":"Source Modeling","url":"https://www.academia.edu/Documents/in/Source_Modeling?f_ri=12950"},{"id":174783,"name":"Loreta","url":"https://www.academia.edu/Documents/in/Loreta?f_ri=12950"},{"id":174784,"name":"Sloreta","url":"https://www.academia.edu/Documents/in/Sloreta?f_ri=12950"},{"id":174788,"name":"Frontal Alpha Asymmetry","url":"https://www.academia.edu/Documents/in/Frontal_Alpha_Asymmetry?f_ri=12950"},{"id":174789,"name":"Frontal Eeg Asymmetry","url":"https://www.academia.edu/Documents/in/Frontal_Eeg_Asymmetry?f_ri=12950"},{"id":174791,"name":"Resting Eeg","url":"https://www.academia.edu/Documents/in/Resting_Eeg?f_ri=12950"},{"id":174793,"name":"Resting Neural Activity","url":"https://www.academia.edu/Documents/in/Resting_Neural_Activity?f_ri=12950"},{"id":174796,"name":"Alpha Band","url":"https://www.academia.edu/Documents/in/Alpha_Band?f_ri=12950"},{"id":174797,"name":"Theta Band","url":"https://www.academia.edu/Documents/in/Theta_Band?f_ri=12950"},{"id":174799,"name":"Beta Band","url":"https://www.academia.edu/Documents/in/Beta_Band?f_ri=12950"},{"id":174800,"name":"Gamma Band","url":"https://www.academia.edu/Documents/in/Gamma_Band?f_ri=12950"},{"id":174803,"name":"Muscle Artifact","url":"https://www.academia.edu/Documents/in/Muscle_Artifact?f_ri=12950"},{"id":174806,"name":"ERS","url":"https://www.academia.edu/Documents/in/ERS?f_ri=12950"},{"id":174807,"name":"ERD","url":"https://www.academia.edu/Documents/in/ERD?f_ri=12950"},{"id":174811,"name":"Eye Artifact","url":"https://www.academia.edu/Documents/in/Eye_Artifact?f_ri=12950"},{"id":174813,"name":"Ocular Artifact","url":"https://www.academia.edu/Documents/in/Ocular_Artifact?f_ri=12950"},{"id":174816,"name":"Richard Davidson","url":"https://www.academia.edu/Documents/in/Richard_Davidson?f_ri=12950"},{"id":174881,"name":"EGI","url":"https://www.academia.edu/Documents/in/EGI?f_ri=12950"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_333549" data-work_id="333549" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/333549/Flicker_induced_colors_and_forms_Interdependencies_and_relation_to_stimulation_frequency_and_phase">Flicker induced colors and forms: Interdependencies and relation to stimulation frequency and phase.</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">Our understanding of human visual perception generally rests on the assumption that conscious visual states represent the interaction of spatial structures in the environment and our nervous system. This assumption is questioned by... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_333549" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">Our understanding of human visual perception generally rests on the assumption that conscious visual states represent the interaction of spatial structures in the environment and our nervous system. This assumption is questioned by circumstances where conscious visual states can be triggered by external stimulation which is not primarily spatially defined. Here, subjective colors and forms are evoked by flickering light while the precise nature of those experiences varies over flicker frequency and phase. What’s more, the occurrence of one subjective experience appears to be associated with the occurrence of others. While these data indicate that conscious visual experience may be evoked directly by particular variations in the flow of spatially unstructured light over time it must be assumed that the systems responsible are essentially temporal in character and capable of representing a variety of visual forms and colors, coded in different frequencies or at different phases of the same processing rhythm.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/333549" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="ec2197996bc3dc41d85cdcca45d2e6e5" rel="nofollow" data-download="{"attachment_id":4866426,"asset_id":333549,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/4866426/download_file?st=MTczNzI5NjIxNCw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="250842" href="https://dahphd.academia.edu/MarkAElliott">Mark A. Elliott</a><script data-card-contents-for-user="250842" type="text/json">{"id":250842,"first_name":"Mark","last_name":"A. Elliott","domain_name":"dahphd","page_name":"MarkAElliott","display_name":"Mark A. Elliott","profile_url":"https://dahphd.academia.edu/MarkAElliott?f_ri=12950","photo":"https://0.academia-photos.com/250842/53879/17891975/s65_mark.a._elliott.jpg"}</script></span></span></li><li class="js-paper-rank-work_333549 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="333549"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 333549, container: ".js-paper-rank-work_333549", }); });</script></li><li class="js-percentile-work_333549 InlineList-item InlineList-item--bordered hidden u-tcGrayDark"><span class="percentile-widget hidden"><span class="u-mr2x percentile-widget" style="display: none">•</span><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 333549; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-percentile-work_333549"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></li><li class="js-view-count-work_333549 InlineList-item InlineList-item--bordered hidden"><div><span><span class="js-view-count view-count u-mr2x" data-work-id="333549"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 333549; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=333549]").text(description); $(".js-view-count-work_333549").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_333549").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="333549"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">6</a>  </div><span class="InlineList-item-text u-textTruncate u-pl9x"><a class="InlineList-item-text" data-has-card-for-ri="9040" rel="nofollow" href="https://www.academia.edu/Documents/in/Consciousness">Consciousness</a>, <script data-card-contents-for-ri="9040" type="text/json">{"id":9040,"name":"Consciousness","url":"https://www.academia.edu/Documents/in/Consciousness?f_ri=12950","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="12950" rel="nofollow" href="https://www.academia.edu/Documents/in/Neural_Oscillations">Neural Oscillations</a>, <script data-card-contents-for-ri="12950" type="text/json">{"id":12950,"name":"Neural Oscillations","url":"https://www.academia.edu/Documents/in/Neural_Oscillations?f_ri=12950","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="23172" rel="nofollow" href="https://www.academia.edu/Documents/in/Temporal_Cognition">Temporal Cognition</a>, <script data-card-contents-for-ri="23172" type="text/json">{"id":23172,"name":"Temporal Cognition","url":"https://www.academia.edu/Documents/in/Temporal_Cognition?f_ri=12950","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="54606" rel="nofollow" href="https://www.academia.edu/Documents/in/Shape_representation_and_matching_segmentation_and_grouping_object_detection_and_recognition">Shape representation and matching, segmentation and grouping, object detection and recognition.</a><script data-card-contents-for-ri="54606" type="text/json">{"id":54606,"name":"Shape representation and matching, segmentation and grouping, object detection and recognition.","url":"https://www.academia.edu/Documents/in/Shape_representation_and_matching_segmentation_and_grouping_object_detection_and_recognition?f_ri=12950","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=333549]'), work: {"id":333549,"title":"Flicker induced colors and forms: Interdependencies and relation to stimulation frequency and phase.","created_at":"2010-09-20T19:24:16.744-07:00","url":"https://www.academia.edu/333549/Flicker_induced_colors_and_forms_Interdependencies_and_relation_to_stimulation_frequency_and_phase?f_ri=12950","dom_id":"work_333549","summary":"Our understanding of human visual perception generally rests on the assumption that conscious visual states represent the interaction of spatial structures in the environment and our nervous system. This assumption is questioned by circumstances where conscious visual states can be triggered by external stimulation which is not primarily spatially defined. Here, subjective colors and forms are evoked by flickering light while the precise nature of those experiences varies over flicker frequency and phase. What’s more, the occurrence of one subjective experience appears to be associated with the occurrence of others. While these data indicate that conscious visual experience may be evoked directly by particular variations in the flow of spatially unstructured light over time it must be assumed that the systems responsible are essentially temporal in character and capable of representing a variety of visual forms and colors, coded in different frequencies or at different phases of the same processing rhythm.","downloadable_attachments":[{"id":4866426,"asset_id":333549,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":250842,"first_name":"Mark","last_name":"A. Elliott","domain_name":"dahphd","page_name":"MarkAElliott","display_name":"Mark A. Elliott","profile_url":"https://dahphd.academia.edu/MarkAElliott?f_ri=12950","photo":"https://0.academia-photos.com/250842/53879/17891975/s65_mark.a._elliott.jpg"}],"research_interests":[{"id":9040,"name":"Consciousness","url":"https://www.academia.edu/Documents/in/Consciousness?f_ri=12950","nofollow":true},{"id":12950,"name":"Neural Oscillations","url":"https://www.academia.edu/Documents/in/Neural_Oscillations?f_ri=12950","nofollow":true},{"id":23172,"name":"Temporal Cognition","url":"https://www.academia.edu/Documents/in/Temporal_Cognition?f_ri=12950","nofollow":true},{"id":54606,"name":"Shape representation and matching, segmentation and grouping, object detection and recognition.","url":"https://www.academia.edu/Documents/in/Shape_representation_and_matching_segmentation_and_grouping_object_detection_and_recognition?f_ri=12950","nofollow":true},{"id":85589,"name":"Hallucinations","url":"https://www.academia.edu/Documents/in/Hallucinations?f_ri=12950"},{"id":174104,"name":"Purkinje Patterns","url":"https://www.academia.edu/Documents/in/Purkinje_Patterns?f_ri=12950"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_73843984" data-work_id="73843984" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/73843984/Wrong_Brains_at_the_Wrong_Time_Understanding_ADHD_Through_the_Diachronic_Constitution_of_Minds">Wrong Brains at the Wrong Time? Understanding ADHD Through the Diachronic Constitution of Minds</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">Objectives The purpose of this theoretical analysis of current research on ADHD is to provide an account integrating executive functional profiles with its broader structural neurodevelopmental profile. Methods Comparative theoretical... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_73843984" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">Objectives <br />The purpose of this theoretical analysis of current research on ADHD is to provide an account integrating executive functional profiles with its broader structural neurodevelopmental profile. <br /><br />Methods <br />Comparative theoretical analyses between executive functional deficit disorder models of ADHD and results from default mode network fMRI data. This was followed by an analysis of the temporal profile of ADHD and phase synchronous neural assemblies. <br /><br />Results <br />Comparative analyses suggest disparities within executive functional deficit disorder models and discontinuities between executive functional and structural profiles of ADHD. Analysis of the temporal signature of ADHD provides a potential avenue for integrating different profiles by means of anchoring executive functions within inherent diachronic neurocognitive organization. <br /><br />Conclusions <br />The analyses provided suggest that executive functional deficits in ADHD arise from much broader idiosyncrasies, rooted within the inherent diachronic organization of neurocognitive function, and whose challenges must be understood in conjunction with socio cultural environmental factors.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/73843984" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="12014268703aa5f213119071568aa6b0" rel="nofollow" data-download="{"attachment_id":82210784,"asset_id":73843984,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/82210784/download_file?st=MTczNzI5NjIxNCw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="57066141" href="https://uow.academia.edu/MadsDengs%C3%B8">Mads J . Dengsø</a><script data-card-contents-for-user="57066141" type="text/json">{"id":57066141,"first_name":"Mads","last_name":"Dengsø","domain_name":"uow","page_name":"MadsDengsø","display_name":"Mads J . Dengsø","profile_url":"https://uow.academia.edu/MadsDengs%C3%B8?f_ri=12950","photo":"https://0.academia-photos.com/57066141/21051297/134239149/s65_mads.dengs_.jpg"}</script></span></span></li><li class="js-paper-rank-work_73843984 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="73843984"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 73843984, container: ".js-paper-rank-work_73843984", }); });</script></li><li class="js-percentile-work_73843984 InlineList-item InlineList-item--bordered hidden u-tcGrayDark"><span class="percentile-widget hidden"><span class="u-mr2x percentile-widget" style="display: none">•</span><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 73843984; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-percentile-work_73843984"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></li><li class="js-view-count-work_73843984 InlineList-item InlineList-item--bordered hidden"><div><span><span class="js-view-count view-count u-mr2x" data-work-id="73843984"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 73843984; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=73843984]").text(description); $(".js-view-count-work_73843984").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_73843984").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="73843984"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">5</a>  </div><span class="InlineList-item-text u-textTruncate u-pl9x"><a class="InlineList-item-text" data-has-card-for-ri="2351" rel="nofollow" href="https://www.academia.edu/Documents/in/ADHD_Psychology_">ADHD (Psychology)</a>, <script data-card-contents-for-ri="2351" type="text/json">{"id":2351,"name":"ADHD (Psychology)","url":"https://www.academia.edu/Documents/in/ADHD_Psychology_?f_ri=12950","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="5178" rel="nofollow" href="https://www.academia.edu/Documents/in/Phenomenology">Phenomenology</a>, <script data-card-contents-for-ri="5178" type="text/json">{"id":5178,"name":"Phenomenology","url":"https://www.academia.edu/Documents/in/Phenomenology?f_ri=12950","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="11632" rel="nofollow" href="https://www.academia.edu/Documents/in/Executive_Functions_Cognitive_Neuroscience_">Executive Functions (Cognitive Neuroscience)</a>, <script data-card-contents-for-ri="11632" type="text/json">{"id":11632,"name":"Executive Functions (Cognitive Neuroscience)","url":"https://www.academia.edu/Documents/in/Executive_Functions_Cognitive_Neuroscience_?f_ri=12950","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="12950" rel="nofollow" href="https://www.academia.edu/Documents/in/Neural_Oscillations">Neural Oscillations</a><script data-card-contents-for-ri="12950" type="text/json">{"id":12950,"name":"Neural Oscillations","url":"https://www.academia.edu/Documents/in/Neural_Oscillations?f_ri=12950","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=73843984]'), work: {"id":73843984,"title":"Wrong Brains at the Wrong Time? Understanding ADHD Through the Diachronic Constitution of Minds","created_at":"2022-03-15T14:37:50.699-07:00","url":"https://www.academia.edu/73843984/Wrong_Brains_at_the_Wrong_Time_Understanding_ADHD_Through_the_Diachronic_Constitution_of_Minds?f_ri=12950","dom_id":"work_73843984","summary":"Objectives \nThe purpose of this theoretical analysis of current research on ADHD is to provide an account integrating executive functional profiles with its broader structural neurodevelopmental profile. \n\nMethods \nComparative theoretical analyses between executive functional deficit disorder models of ADHD and results from default mode network fMRI data. This was followed by an analysis of the temporal profile of ADHD and phase synchronous neural assemblies. \n\nResults \nComparative analyses suggest disparities within executive functional deficit disorder models and discontinuities between executive functional and structural profiles of ADHD. Analysis of the temporal signature of ADHD provides a potential avenue for integrating different profiles by means of anchoring executive functions within inherent diachronic neurocognitive organization. \n\nConclusions \nThe analyses provided suggest that executive functional deficits in ADHD arise from much broader idiosyncrasies, rooted within the inherent diachronic organization of neurocognitive function, and whose challenges must be understood in conjunction with socio cultural environmental factors.","downloadable_attachments":[{"id":82210784,"asset_id":73843984,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":57066141,"first_name":"Mads","last_name":"Dengsø","domain_name":"uow","page_name":"MadsDengsø","display_name":"Mads J . Dengsø","profile_url":"https://uow.academia.edu/MadsDengs%C3%B8?f_ri=12950","photo":"https://0.academia-photos.com/57066141/21051297/134239149/s65_mads.dengs_.jpg"}],"research_interests":[{"id":2351,"name":"ADHD (Psychology)","url":"https://www.academia.edu/Documents/in/ADHD_Psychology_?f_ri=12950","nofollow":true},{"id":5178,"name":"Phenomenology","url":"https://www.academia.edu/Documents/in/Phenomenology?f_ri=12950","nofollow":true},{"id":11632,"name":"Executive Functions (Cognitive Neuroscience)","url":"https://www.academia.edu/Documents/in/Executive_Functions_Cognitive_Neuroscience_?f_ri=12950","nofollow":true},{"id":12950,"name":"Neural Oscillations","url":"https://www.academia.edu/Documents/in/Neural_Oscillations?f_ri=12950","nofollow":true},{"id":21902,"name":"Time Perception","url":"https://www.academia.edu/Documents/in/Time_Perception?f_ri=12950"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_36699935" data-work_id="36699935" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/36699935/Input_convergence_synaptic_plasticity_and_functional_coupling_across_hippocampal_prefrontal_thalamic_circuits">Input convergence, synaptic plasticity and functional coupling across hippocampal-prefrontal-thalamic circuits</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">Executive functions and working memory are long known to involve the prefrontal cortex (PFC), and two PFC-projecting areas: midline/paramidline thalamus (MLT) and cornus ammonis 1 (CA1)/subiculum of the hippocampal formation (HF). An... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_36699935" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">Executive functions and working memory are long known to involve the prefrontal cortex (PFC), and two PFC-projecting areas: midline/paramidline thalamus (MLT) and cornus ammonis 1 (CA1)/subiculum of the hippocampal formation (HF). An increasing number of rodent electrophysiology studies are examining these substrates together, thus providing circuit-level perspectives on input convergence, synaptic plasticity and functional coupling, as well as insights into cognition mechanisms and brain disorders. Our review article puts this literature into a method-oriented narrative. As revisited throughout the text, limbic thalamic and hippocampal afferents to the PFC gate one another's inputs, which in turn are modulated by PFC interneurons and ascending monoaminergic projections. In addition, long-term synaptic plasticity, paired-pulse facilitation (PPF), and event-related potentials (ERP) dynamically vary across PFC-related circuits during learning paradigms and drug effects. Finally, thalamic-prefrontal loops, which have been shown to amplify both cognitive processes and limbic seizures, are also being implicated as relays in the prefrontal-hippocampal feedback, contributing to spatial navigation and decision making. Based on these issues, we conclude the review with a critical synthesis and some research directions.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/36699935" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="69dd023afb9fc8d1313a7669e0e8a257" rel="nofollow" data-download="{"attachment_id":56639384,"asset_id":36699935,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/56639384/download_file?st=MTczNzI5NjIxNCw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="38391" href="https://med-umich.academia.edu/LezioBuenoJunior">Lezio S Bueno-Junior</a><script data-card-contents-for-user="38391" type="text/json">{"id":38391,"first_name":"Lezio","last_name":"Bueno-Junior","domain_name":"med-umich","page_name":"LezioBuenoJunior","display_name":"Lezio S Bueno-Junior","profile_url":"https://med-umich.academia.edu/LezioBuenoJunior?f_ri=12950","photo":"https://0.academia-photos.com/38391/12791/148180635/s65_lezio.bueno-junior.jpg"}</script></span></span></li><li class="js-paper-rank-work_36699935 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="36699935"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 36699935, container: ".js-paper-rank-work_36699935", }); });</script></li><li class="js-percentile-work_36699935 InlineList-item InlineList-item--bordered hidden u-tcGrayDark"><span class="percentile-widget hidden"><span class="u-mr2x percentile-widget" style="display: none">•</span><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 36699935; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-percentile-work_36699935"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></li><li class="js-view-count-work_36699935 InlineList-item InlineList-item--bordered hidden"><div><span><span class="js-view-count view-count u-mr2x" data-work-id="36699935"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 36699935; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=36699935]").text(description); $(".js-view-count-work_36699935").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_36699935").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="36699935"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">12</a>  </div><span class="InlineList-item-text u-textTruncate u-pl10x"><a class="InlineList-item-text" data-has-card-for-ri="1681" rel="nofollow" href="https://www.academia.edu/Documents/in/Decision_Making">Decision Making</a>, <script data-card-contents-for-ri="1681" type="text/json">{"id":1681,"name":"Decision Making","url":"https://www.academia.edu/Documents/in/Decision_Making?f_ri=12950","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="2583" rel="nofollow" href="https://www.academia.edu/Documents/in/Deep_Brain_Stimulation">Deep Brain Stimulation</a>, <script data-card-contents-for-ri="2583" type="text/json">{"id":2583,"name":"Deep Brain Stimulation","url":"https://www.academia.edu/Documents/in/Deep_Brain_Stimulation?f_ri=12950","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="8538" rel="nofollow" href="https://www.academia.edu/Documents/in/Working_Memory">Working Memory</a>, <script data-card-contents-for-ri="8538" type="text/json">{"id":8538,"name":"Working Memory","url":"https://www.academia.edu/Documents/in/Working_Memory?f_ri=12950","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="12950" rel="nofollow" href="https://www.academia.edu/Documents/in/Neural_Oscillations">Neural Oscillations</a><script data-card-contents-for-ri="12950" type="text/json">{"id":12950,"name":"Neural Oscillations","url":"https://www.academia.edu/Documents/in/Neural_Oscillations?f_ri=12950","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=36699935]'), work: {"id":36699935,"title":"Input convergence, synaptic plasticity and functional coupling across hippocampal-prefrontal-thalamic circuits","created_at":"2018-05-23T10:32:59.844-07:00","url":"https://www.academia.edu/36699935/Input_convergence_synaptic_plasticity_and_functional_coupling_across_hippocampal_prefrontal_thalamic_circuits?f_ri=12950","dom_id":"work_36699935","summary":"Executive functions and working memory are long known to involve the prefrontal cortex (PFC), and two PFC-projecting areas: midline/paramidline thalamus (MLT) and cornus ammonis 1 (CA1)/subiculum of the hippocampal formation (HF). An increasing number of rodent electrophysiology studies are examining these substrates together, thus providing circuit-level perspectives on input convergence, synaptic plasticity and functional coupling, as well as insights into cognition mechanisms and brain disorders. Our review article puts this literature into a method-oriented narrative. As revisited throughout the text, limbic thalamic and hippocampal afferents to the PFC gate one another's inputs, which in turn are modulated by PFC interneurons and ascending monoaminergic projections. In addition, long-term synaptic plasticity, paired-pulse facilitation (PPF), and event-related potentials (ERP) dynamically vary across PFC-related circuits during learning paradigms and drug effects. Finally, thalamic-prefrontal loops, which have been shown to amplify both cognitive processes and limbic seizures, are also being implicated as relays in the prefrontal-hippocampal feedback, contributing to spatial navigation and decision making. Based on these issues, we conclude the review with a critical synthesis and some research directions.","downloadable_attachments":[{"id":56639384,"asset_id":36699935,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":38391,"first_name":"Lezio","last_name":"Bueno-Junior","domain_name":"med-umich","page_name":"LezioBuenoJunior","display_name":"Lezio S Bueno-Junior","profile_url":"https://med-umich.academia.edu/LezioBuenoJunior?f_ri=12950","photo":"https://0.academia-photos.com/38391/12791/148180635/s65_lezio.bueno-junior.jpg"}],"research_interests":[{"id":1681,"name":"Decision Making","url":"https://www.academia.edu/Documents/in/Decision_Making?f_ri=12950","nofollow":true},{"id":2583,"name":"Deep Brain Stimulation","url":"https://www.academia.edu/Documents/in/Deep_Brain_Stimulation?f_ri=12950","nofollow":true},{"id":8538,"name":"Working Memory","url":"https://www.academia.edu/Documents/in/Working_Memory?f_ri=12950","nofollow":true},{"id":12950,"name":"Neural Oscillations","url":"https://www.academia.edu/Documents/in/Neural_Oscillations?f_ri=12950","nofollow":true},{"id":28576,"name":"Prefrontal Cortex","url":"https://www.academia.edu/Documents/in/Prefrontal_Cortex?f_ri=12950"},{"id":32003,"name":"Synaptic Plasticity","url":"https://www.academia.edu/Documents/in/Synaptic_Plasticity?f_ri=12950"},{"id":33732,"name":"Executive Function","url":"https://www.academia.edu/Documents/in/Executive_Function?f_ri=12950"},{"id":38676,"name":"Anxiety","url":"https://www.academia.edu/Documents/in/Anxiety?f_ri=12950"},{"id":57556,"name":"Hippocampus","url":"https://www.academia.edu/Documents/in/Hippocampus?f_ri=12950"},{"id":66228,"name":"Thalamus","url":"https://www.academia.edu/Documents/in/Thalamus?f_ri=12950"},{"id":66513,"name":"Optogenetics","url":"https://www.academia.edu/Documents/in/Optogenetics?f_ri=12950"},{"id":554075,"name":"Spatial navigation","url":"https://www.academia.edu/Documents/in/Spatial_navigation?f_ri=12950"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_19981550 coauthored" data-work_id="19981550" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/19981550/Hippocampal_theta_input_to_the_amygdala_shapes_feedforward_inhibition_to_gate_heterosynaptic_plasticity">Hippocampal theta input to the amygdala shapes feedforward inhibition to gate heterosynaptic plasticity</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">The dynamic interactions between hippocampus and amygdala are critical for emotional memory. Theta synchrony between these structures occurs during fear memory retrieval and may facilitate synaptic plasticity, but the cellular mechanisms... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_19981550" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">The dynamic interactions between hippocampus and amygdala are critical for emotional memory. Theta synchrony between these structures occurs during fear memory retrieval and may facilitate synaptic plasticity, but the cellular mechanisms are unknown. We report that interneurons of the mouse basal amygdala are activated during theta network activity or optogenetic stimulation of ventral CA1 pyramidal cell axons, whereas principal neurons are inhibited. Interneurons provide feedforward inhibition that transiently hyper-polarizes principal neurons. However, synaptic inhibi-tion attenuates during theta frequency stimulation of ventral CA1 fibers, and this broadens excitatory post-synaptic potentials. These effects are mediated by GABA B receptors and change in the Cl- driving force. Pairing theta frequency stimulation of ventral CA1 fibers with coincident stimuli of the lateral amygdala induces long-term potentiation of lateral-basal amygdala excitatory synapses. Hence, feedforward inhibition, known to enforce temporal fidelity of excitatory inputs, dominates hippocampus-amygdala interactions to gate heterosynaptic plasticity.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/19981550" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="9e23ad895006519dc3b9f5510feb65a3" rel="nofollow" data-download="{"attachment_id":40943497,"asset_id":19981550,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/40943497/download_file?st=MTczNzI5NjIxNCw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="41724232" href="https://independent.academia.edu/Micha%C3%ABlBazelot">Michaël Bazelot</a><script data-card-contents-for-user="41724232" type="text/json">{"id":41724232,"first_name":"Michaël","last_name":"Bazelot","domain_name":"independent","page_name":"MichaëlBazelot","display_name":"Michaël Bazelot","profile_url":"https://independent.academia.edu/Micha%C3%ABlBazelot?f_ri=12950","photo":"/images/s65_no_pic.png"}</script></span></span><span class="u-displayInlineBlock InlineList-item-text"> and <span class="u-textDecorationUnderline u-clickable InlineList-item-text js-work-more-authors-19981550">+2</span><div class="hidden js-additional-users-19981550"><div><span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a href="https://univ-amu.academia.edu/MarcoBocchio">Marco Bocchio</a></span></div><div><span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a href="https://i-med.academia.edu/YuKasugai">Yu Kasugai</a></span></div></div></span><script>(function(){ var popoverSettings = { el: $('.js-work-more-authors-19981550'), placement: 'bottom', hide_delay: 200, html: true, content: function(){ return $('.js-additional-users-19981550').html(); } } new HoverPopover(popoverSettings); })();</script></li><li class="js-paper-rank-work_19981550 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="19981550"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 19981550, container: ".js-paper-rank-work_19981550", }); });</script></li><li class="js-percentile-work_19981550 InlineList-item InlineList-item--bordered hidden u-tcGrayDark"><span class="percentile-widget hidden"><span class="u-mr2x percentile-widget" style="display: none">•</span><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 19981550; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-percentile-work_19981550"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></li><li class="js-view-count-work_19981550 InlineList-item InlineList-item--bordered hidden"><div><span><span class="js-view-count view-count u-mr2x" data-work-id="19981550"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 19981550; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=19981550]").text(description); $(".js-view-count-work_19981550").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_19981550").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="19981550"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">11</a>  </div><span class="InlineList-item-text u-textTruncate u-pl10x"><a class="InlineList-item-text" data-has-card-for-ri="12950" rel="nofollow" href="https://www.academia.edu/Documents/in/Neural_Oscillations">Neural Oscillations</a>, <script data-card-contents-for-ri="12950" type="text/json">{"id":12950,"name":"Neural Oscillations","url":"https://www.academia.edu/Documents/in/Neural_Oscillations?f_ri=12950","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="13485" rel="nofollow" href="https://www.academia.edu/Documents/in/GABAergic_Neurotransmission">GABAergic Neurotransmission</a>, <script data-card-contents-for-ri="13485" type="text/json">{"id":13485,"name":"GABAergic Neurotransmission","url":"https://www.academia.edu/Documents/in/GABAergic_Neurotransmission?f_ri=12950","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="32003" rel="nofollow" href="https://www.academia.edu/Documents/in/Synaptic_Plasticity">Synaptic Plasticity</a>, <script data-card-contents-for-ri="32003" type="text/json">{"id":32003,"name":"Synaptic Plasticity","url":"https://www.academia.edu/Documents/in/Synaptic_Plasticity?f_ri=12950","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="57556" rel="nofollow" href="https://www.academia.edu/Documents/in/Hippocampus">Hippocampus</a><script data-card-contents-for-ri="57556" type="text/json">{"id":57556,"name":"Hippocampus","url":"https://www.academia.edu/Documents/in/Hippocampus?f_ri=12950","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=19981550]'), work: {"id":19981550,"title":"Hippocampal theta input to the amygdala shapes feedforward inhibition to gate heterosynaptic plasticity","created_at":"2016-01-03T08:01:12.632-08:00","url":"https://www.academia.edu/19981550/Hippocampal_theta_input_to_the_amygdala_shapes_feedforward_inhibition_to_gate_heterosynaptic_plasticity?f_ri=12950","dom_id":"work_19981550","summary":"The dynamic interactions between hippocampus and amygdala are critical for emotional memory. Theta synchrony between these structures occurs during fear memory retrieval and may facilitate synaptic plasticity, but the cellular mechanisms are unknown. We report that interneurons of the mouse basal amygdala are activated during theta network activity or optogenetic stimulation of ventral CA1 pyramidal cell axons, whereas principal neurons are inhibited. Interneurons provide feedforward inhibition that transiently hyper-polarizes principal neurons. However, synaptic inhibi-tion attenuates during theta frequency stimulation of ventral CA1 fibers, and this broadens excitatory post-synaptic potentials. These effects are mediated by GABA B receptors and change in the Cl- driving force. Pairing theta frequency stimulation of ventral CA1 fibers with coincident stimuli of the lateral amygdala induces long-term potentiation of lateral-basal amygdala excitatory synapses. Hence, feedforward inhibition, known to enforce temporal fidelity of excitatory inputs, dominates hippocampus-amygdala interactions to gate heterosynaptic plasticity.","downloadable_attachments":[{"id":40943497,"asset_id":19981550,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":41724232,"first_name":"Michaël","last_name":"Bazelot","domain_name":"independent","page_name":"MichaëlBazelot","display_name":"Michaël Bazelot","profile_url":"https://independent.academia.edu/Micha%C3%ABlBazelot?f_ri=12950","photo":"/images/s65_no_pic.png"},{"id":40867177,"first_name":"Marco","last_name":"Bocchio","domain_name":"univ-amu","page_name":"MarcoBocchio","display_name":"Marco Bocchio","profile_url":"https://univ-amu.academia.edu/MarcoBocchio?f_ri=12950","photo":"/images/s65_no_pic.png"},{"id":41859355,"first_name":"Yu","last_name":"Kasugai","domain_name":"i-med","page_name":"YuKasugai","display_name":"Yu Kasugai","profile_url":"https://i-med.academia.edu/YuKasugai?f_ri=12950","photo":"/images/s65_no_pic.png"}],"research_interests":[{"id":12950,"name":"Neural Oscillations","url":"https://www.academia.edu/Documents/in/Neural_Oscillations?f_ri=12950","nofollow":true},{"id":13485,"name":"GABAergic Neurotransmission","url":"https://www.academia.edu/Documents/in/GABAergic_Neurotransmission?f_ri=12950","nofollow":true},{"id":32003,"name":"Synaptic Plasticity","url":"https://www.academia.edu/Documents/in/Synaptic_Plasticity?f_ri=12950","nofollow":true},{"id":57556,"name":"Hippocampus","url":"https://www.academia.edu/Documents/in/Hippocampus?f_ri=12950","nofollow":true},{"id":58484,"name":"Inhibitory Interneurons","url":"https://www.academia.edu/Documents/in/Inhibitory_Interneurons?f_ri=12950"},{"id":66513,"name":"Optogenetics","url":"https://www.academia.edu/Documents/in/Optogenetics?f_ri=12950"},{"id":89803,"name":"GABA receptors","url":"https://www.academia.edu/Documents/in/GABA_receptors?f_ri=12950"},{"id":159962,"name":"Amygdala","url":"https://www.academia.edu/Documents/in/Amygdala?f_ri=12950"},{"id":976865,"name":"Theta","url":"https://www.academia.edu/Documents/in/Theta?f_ri=12950"},{"id":1114822,"name":"LTP/LTD","url":"https://www.academia.edu/Documents/in/LTP_LTD?f_ri=12950"},{"id":1959361,"name":"Basolateral Amygdala","url":"https://www.academia.edu/Documents/in/Basolateral_Amygdala?f_ri=12950"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_214365" data-work_id="214365" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/214365/A_model_of_the_olivo_cerebellar_system_as_a_temporal_pattern_generator">A model of the olivo-cerebellar system as a temporal pattern generator</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">The olivo-cerebellar system has been implicated in temporal coordination of task components. Here, we propose a novel model that enables the olivo-cerebellar system to function as a generator of temporal patterns. These patterns could be... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_214365" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">The olivo-cerebellar system has been implicated in temporal coordination of task components. Here, we propose a novel model that enables the olivo-cerebellar system to function as a generator of temporal patterns. These patterns could be used for timing of motor, sensory and cognitive tasks. The proposed mechanism for the generation of these patterns is based on subthreshold oscillations in a network of inferior olivary neurons and their control by the cerebellar cortex and nuclei. Our model, which integrates a large body of anatomical and physiological observations, lends itself to simple, testable predictions and provides a new conceptual framework for olivo-cerebellar research.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/214365" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="515e60e6c19fbebd52db5bbd601138ad" rel="nofollow" data-download="{"attachment_id":708323,"asset_id":214365,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/708323/download_file?st=MTczNzI5NjIxNCw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="131455" href="https://fmi.academia.edu/GiladJacobson">Gilad Jacobson</a><script data-card-contents-for-user="131455" type="text/json">{"id":131455,"first_name":"Gilad","last_name":"Jacobson","domain_name":"fmi","page_name":"GiladJacobson","display_name":"Gilad Jacobson","profile_url":"https://fmi.academia.edu/GiladJacobson?f_ri=12950","photo":"https://0.academia-photos.com/131455/85974/93891/s65_gilad.jacobson.jpg"}</script></span></span></li><li class="js-paper-rank-work_214365 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="214365"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 214365, container: ".js-paper-rank-work_214365", }); });</script></li><li class="js-percentile-work_214365 InlineList-item InlineList-item--bordered hidden u-tcGrayDark"><span class="percentile-widget hidden"><span class="u-mr2x percentile-widget" style="display: none">•</span><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 214365; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-percentile-work_214365"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></li><li class="js-view-count-work_214365 InlineList-item InlineList-item--bordered hidden"><div><span><span class="js-view-count view-count u-mr2x" data-work-id="214365"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 214365; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=214365]").text(description); $(".js-view-count-work_214365").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_214365").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="214365"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">5</a>  </div><span class="InlineList-item-text u-textTruncate u-pl9x"><a class="InlineList-item-text" data-has-card-for-ri="5451" rel="nofollow" href="https://www.academia.edu/Documents/in/Computational_Neuroscience">Computational Neuroscience</a>, <script data-card-contents-for-ri="5451" type="text/json">{"id":5451,"name":"Computational Neuroscience","url":"https://www.academia.edu/Documents/in/Computational_Neuroscience?f_ri=12950","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="9843" rel="nofollow" href="https://www.academia.edu/Documents/in/Timing_Ability_Mutual_Funds_">Timing Ability (Mutual Funds)</a>, <script data-card-contents-for-ri="9843" type="text/json">{"id":9843,"name":"Timing Ability (Mutual Funds)","url":"https://www.academia.edu/Documents/in/Timing_Ability_Mutual_Funds_?f_ri=12950","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="12950" rel="nofollow" href="https://www.academia.edu/Documents/in/Neural_Oscillations">Neural Oscillations</a>, <script data-card-contents-for-ri="12950" type="text/json">{"id":12950,"name":"Neural Oscillations","url":"https://www.academia.edu/Documents/in/Neural_Oscillations?f_ri=12950","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="28120" rel="nofollow" href="https://www.academia.edu/Documents/in/Spatio_Temporal_Analysis">Spatio Temporal Analysis</a><script data-card-contents-for-ri="28120" type="text/json">{"id":28120,"name":"Spatio Temporal Analysis","url":"https://www.academia.edu/Documents/in/Spatio_Temporal_Analysis?f_ri=12950","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=214365]'), work: {"id":214365,"title":"A model of the olivo-cerebellar system as a temporal pattern generator","created_at":"2010-02-11T19:37:31.705-08:00","url":"https://www.academia.edu/214365/A_model_of_the_olivo_cerebellar_system_as_a_temporal_pattern_generator?f_ri=12950","dom_id":"work_214365","summary":"The olivo-cerebellar system has been implicated in temporal coordination of task components. Here, we propose a novel model that enables the olivo-cerebellar system to function as a generator of temporal patterns. These patterns could be used for timing of motor, sensory and cognitive tasks. The proposed mechanism for the generation of these patterns is based on subthreshold oscillations in a network of inferior olivary neurons and their control by the cerebellar cortex and nuclei. Our model, which integrates a large body of anatomical and physiological observations, lends itself to simple, testable predictions and provides a new conceptual framework for olivo-cerebellar research.","downloadable_attachments":[{"id":708323,"asset_id":214365,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":131455,"first_name":"Gilad","last_name":"Jacobson","domain_name":"fmi","page_name":"GiladJacobson","display_name":"Gilad Jacobson","profile_url":"https://fmi.academia.edu/GiladJacobson?f_ri=12950","photo":"https://0.academia-photos.com/131455/85974/93891/s65_gilad.jacobson.jpg"}],"research_interests":[{"id":5451,"name":"Computational Neuroscience","url":"https://www.academia.edu/Documents/in/Computational_Neuroscience?f_ri=12950","nofollow":true},{"id":9843,"name":"Timing Ability (Mutual Funds)","url":"https://www.academia.edu/Documents/in/Timing_Ability_Mutual_Funds_?f_ri=12950","nofollow":true},{"id":12950,"name":"Neural Oscillations","url":"https://www.academia.edu/Documents/in/Neural_Oscillations?f_ri=12950","nofollow":true},{"id":28120,"name":"Spatio Temporal Analysis","url":"https://www.academia.edu/Documents/in/Spatio_Temporal_Analysis?f_ri=12950","nofollow":true},{"id":65615,"name":"Cerebellum","url":"https://www.academia.edu/Documents/in/Cerebellum?f_ri=12950"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_47897170" data-work_id="47897170" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/47897170/The_emergence_of_long_lasting_transients_of_activity_in_simple_neural_networks">The emergence of long-lasting transients of activity in simple neural networks</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">The question was investigated whether longlasting transients of activity, observed to occur in the intact cerebral cortex (EEG slow (6) waves and 'K' complexes) as well as in isolated tissues cultured in vitro, can also emerge in a model... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_47897170" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">The question was investigated whether longlasting transients of activity, observed to occur in the intact cerebral cortex (EEG slow (6) waves and 'K' complexes) as well as in isolated tissues cultured in vitro, can also emerge in a model network of excitatory and inhibitory cells. We show that such transients can indeed occur even if the cells do not have built-in slow kinetics. For certain parameter settings, the network is in a bistable state in which periods of increased activity (long-lasting transients) alternate with minimal activity. Transients are triggered by spontaneously firing cells ('noise'), which, rather than via a build-up of recurrent synaptic inhibition, also initiate their termination. During a transient, the network continually makes transitions from one equilibrium to another as a result of spontaneous firing until it is switched back to the quiescent state, i.e., after a variable period of time of noise-induced transitions the transient is terminated. If the network is small, activity can terminate even without inhibition. In large networks, inhibition keeps the network sensitive to spontaneously firing cells by holding it in the neighbourhood of a critical point between active and quiescent state.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/47897170" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="5099dd96d6a8a45b1c11377afdf6b9ea" rel="nofollow" data-download="{"attachment_id":66788126,"asset_id":47897170,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/66788126/download_file?st=MTczNzI5NjIxNCw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="38416026" href="https://independent.academia.edu/PeltJaapVan">Jaap Van Pelt</a><script data-card-contents-for-user="38416026" type="text/json">{"id":38416026,"first_name":"Jaap Van","last_name":"Pelt","domain_name":"independent","page_name":"PeltJaapVan","display_name":"Jaap Van Pelt","profile_url":"https://independent.academia.edu/PeltJaapVan?f_ri=12950","photo":"/images/s65_no_pic.png"}</script></span></span></li><li class="js-paper-rank-work_47897170 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="47897170"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 47897170, container: ".js-paper-rank-work_47897170", }); });</script></li><li class="js-percentile-work_47897170 InlineList-item InlineList-item--bordered hidden u-tcGrayDark"><span class="percentile-widget hidden"><span class="u-mr2x percentile-widget" style="display: none">•</span><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 47897170; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-percentile-work_47897170"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></li><li class="js-view-count-work_47897170 InlineList-item InlineList-item--bordered hidden"><div><span><span class="js-view-count view-count u-mr2x" data-work-id="47897170"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 47897170; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=47897170]").text(description); $(".js-view-count-work_47897170").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_47897170").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="47897170"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">19</a>  </div><span class="InlineList-item-text u-textTruncate u-pl10x"><a class="InlineList-item-text" data-has-card-for-ri="8" rel="nofollow" href="https://www.academia.edu/Documents/in/Critical_Theory">Critical Theory</a>, <script data-card-contents-for-ri="8" type="text/json">{"id":8,"name":"Critical Theory","url":"https://www.academia.edu/Documents/in/Critical_Theory?f_ri=12950","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="161" rel="nofollow" href="https://www.academia.edu/Documents/in/Neuroscience">Neuroscience</a>, <script data-card-contents-for-ri="161" type="text/json">{"id":161,"name":"Neuroscience","url":"https://www.academia.edu/Documents/in/Neuroscience?f_ri=12950","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="237" rel="nofollow" href="https://www.academia.edu/Documents/in/Cognitive_Science">Cognitive Science</a>, <script data-card-contents-for-ri="237" type="text/json">{"id":237,"name":"Cognitive Science","url":"https://www.academia.edu/Documents/in/Cognitive_Science?f_ri=12950","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="5451" rel="nofollow" href="https://www.academia.edu/Documents/in/Computational_Neuroscience">Computational Neuroscience</a><script data-card-contents-for-ri="5451" type="text/json">{"id":5451,"name":"Computational Neuroscience","url":"https://www.academia.edu/Documents/in/Computational_Neuroscience?f_ri=12950","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=47897170]'), work: {"id":47897170,"title":"The emergence of long-lasting transients of activity in simple neural networks","created_at":"2021-05-03T01:00:35.746-07:00","url":"https://www.academia.edu/47897170/The_emergence_of_long_lasting_transients_of_activity_in_simple_neural_networks?f_ri=12950","dom_id":"work_47897170","summary":"The question was investigated whether longlasting transients of activity, observed to occur in the intact cerebral cortex (EEG slow (6) waves and 'K' complexes) as well as in isolated tissues cultured in vitro, can also emerge in a model network of excitatory and inhibitory cells. We show that such transients can indeed occur even if the cells do not have built-in slow kinetics. For certain parameter settings, the network is in a bistable state in which periods of increased activity (long-lasting transients) alternate with minimal activity. Transients are triggered by spontaneously firing cells ('noise'), which, rather than via a build-up of recurrent synaptic inhibition, also initiate their termination. During a transient, the network continually makes transitions from one equilibrium to another as a result of spontaneous firing until it is switched back to the quiescent state, i.e., after a variable period of time of noise-induced transitions the transient is terminated. If the network is small, activity can terminate even without inhibition. In large networks, inhibition keeps the network sensitive to spontaneously firing cells by holding it in the neighbourhood of a critical point between active and quiescent state.","downloadable_attachments":[{"id":66788126,"asset_id":47897170,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":38416026,"first_name":"Jaap Van","last_name":"Pelt","domain_name":"independent","page_name":"PeltJaapVan","display_name":"Jaap Van Pelt","profile_url":"https://independent.academia.edu/PeltJaapVan?f_ri=12950","photo":"/images/s65_no_pic.png"}],"research_interests":[{"id":8,"name":"Critical Theory","url":"https://www.academia.edu/Documents/in/Critical_Theory?f_ri=12950","nofollow":true},{"id":161,"name":"Neuroscience","url":"https://www.academia.edu/Documents/in/Neuroscience?f_ri=12950","nofollow":true},{"id":237,"name":"Cognitive Science","url":"https://www.academia.edu/Documents/in/Cognitive_Science?f_ri=12950","nofollow":true},{"id":5451,"name":"Computational Neuroscience","url":"https://www.academia.edu/Documents/in/Computational_Neuroscience?f_ri=12950","nofollow":true},{"id":10402,"name":"EEG","url":"https://www.academia.edu/Documents/in/EEG?f_ri=12950"},{"id":11598,"name":"Neural Networks","url":"https://www.academia.edu/Documents/in/Neural_Networks?f_ri=12950"},{"id":12950,"name":"Neural Oscillations","url":"https://www.academia.edu/Documents/in/Neural_Oscillations?f_ri=12950"},{"id":26066,"name":"Neural Network","url":"https://www.academia.edu/Documents/in/Neural_Network?f_ri=12950"},{"id":49731,"name":"Mathematical Neuroscience","url":"https://www.academia.edu/Documents/in/Mathematical_Neuroscience?f_ri=12950"},{"id":54123,"name":"Artificial Neural Networks","url":"https://www.academia.edu/Documents/in/Artificial_Neural_Networks?f_ri=12950"},{"id":55356,"name":"Theoretical Neuroscience","url":"https://www.academia.edu/Documents/in/Theoretical_Neuroscience?f_ri=12950"},{"id":68431,"name":"Noise","url":"https://www.academia.edu/Documents/in/Noise?f_ri=12950"},{"id":113890,"name":"Power Law","url":"https://www.academia.edu/Documents/in/Power_Law?f_ri=12950"},{"id":149891,"name":"Brain oscillations","url":"https://www.academia.edu/Documents/in/Brain_oscillations?f_ri=12950"},{"id":193974,"name":"Neurons","url":"https://www.academia.edu/Documents/in/Neurons?f_ri=12950"},{"id":237951,"name":"System criticality","url":"https://www.academia.edu/Documents/in/System_criticality?f_ri=12950"},{"id":310813,"name":"Criticality","url":"https://www.academia.edu/Documents/in/Criticality?f_ri=12950"},{"id":567727,"name":"Biological cybernetics","url":"https://www.academia.edu/Documents/in/Biological_cybernetics?f_ri=12950"},{"id":955727,"name":"Action Potentials","url":"https://www.academia.edu/Documents/in/Action_Potentials?f_ri=12950"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_24067342" data-work_id="24067342" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/24067342/Brain_oscillations_in_perception_timing_and_action">Brain oscillations in perception, timing and action</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">Catching a thrown ball requires a tight coupling between perception and motor control. In this review, we examine multidimensional information processing across various perceptual and motor tasks. We summarize how perception, timing and... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_24067342" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">Catching a thrown ball requires a tight coupling between perception and motor control. In this review, we examine multidimensional information processing across various perceptual and motor tasks. We summarize how perception, timing and action can be understood in terms of the coupling of gamma band oscillations, which represent the local activities of brain circuits, to a specific phase of long-range low-frequency oscillations. We propose a temporal window of integration that emerges from cross-frequency coupling that serves to produce optimized action.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/24067342" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="35782c9aee72363934f46370f7915adc" rel="nofollow" data-download="{"attachment_id":44438518,"asset_id":24067342,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/44438518/download_file?st=MTczNzI5NjIxNCw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="3039734" href="https://independent.academia.edu/DayaGupta">Daya S Gupta</a><script data-card-contents-for-user="3039734" type="text/json">{"id":3039734,"first_name":"Daya","last_name":"Gupta","domain_name":"independent","page_name":"DayaGupta","display_name":"Daya S Gupta","profile_url":"https://independent.academia.edu/DayaGupta?f_ri=12950","photo":"https://0.academia-photos.com/3039734/8739990/72626189/s65_daya.gupta.jpg"}</script></span></span></li><li class="js-paper-rank-work_24067342 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="24067342"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 24067342, container: ".js-paper-rank-work_24067342", }); });</script></li><li class="js-percentile-work_24067342 InlineList-item InlineList-item--bordered hidden u-tcGrayDark"><span class="percentile-widget hidden"><span class="u-mr2x percentile-widget" style="display: none">•</span><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 24067342; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-percentile-work_24067342"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></li><li class="js-view-count-work_24067342 InlineList-item InlineList-item--bordered hidden"><div><span><span class="js-view-count view-count u-mr2x" data-work-id="24067342"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 24067342; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=24067342]").text(description); $(".js-view-count-work_24067342").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_24067342").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="24067342"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i></div><span class="InlineList-item-text u-textTruncate u-pl6x"><a class="InlineList-item-text" data-has-card-for-ri="12950" rel="nofollow" href="https://www.academia.edu/Documents/in/Neural_Oscillations">Neural Oscillations</a><script data-card-contents-for-ri="12950" type="text/json">{"id":12950,"name":"Neural Oscillations","url":"https://www.academia.edu/Documents/in/Neural_Oscillations?f_ri=12950","nofollow":true}</script></span></li><script>(function(){ if (false) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=24067342]'), work: {"id":24067342,"title":"Brain oscillations in perception, timing and action","created_at":"2016-04-05T08:04:59.400-07:00","url":"https://www.academia.edu/24067342/Brain_oscillations_in_perception_timing_and_action?f_ri=12950","dom_id":"work_24067342","summary":"Catching a thrown ball requires a tight coupling between perception and motor control. In this review, we examine multidimensional information processing across various perceptual and motor tasks. We summarize how perception, timing and action can be understood in terms of the coupling of gamma band oscillations, which represent the local activities of brain circuits, to a specific phase of long-range low-frequency oscillations. We propose a temporal window of integration that emerges from cross-frequency coupling that serves to produce optimized action.","downloadable_attachments":[{"id":44438518,"asset_id":24067342,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":3039734,"first_name":"Daya","last_name":"Gupta","domain_name":"independent","page_name":"DayaGupta","display_name":"Daya S Gupta","profile_url":"https://independent.academia.edu/DayaGupta?f_ri=12950","photo":"https://0.academia-photos.com/3039734/8739990/72626189/s65_daya.gupta.jpg"}],"research_interests":[{"id":12950,"name":"Neural Oscillations","url":"https://www.academia.edu/Documents/in/Neural_Oscillations?f_ri=12950","nofollow":true}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_63639737" data-work_id="63639737" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/63639737/Harmonies_of_the_Mind_Physics_and_Physiology_of_Self">Harmonies of the Mind: Physics and Physiology of Self</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">The brain is an orchestra playing a harmonious symphony of the Mind that we experience as the unity of our picture of the world and ourselves in this world. Violations of this process, which we call mental pathologies, lead to dissonances... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_63639737" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">The brain is an orchestra playing a harmonious symphony of the Mind that we experience as the unity of our picture of the world and ourselves in this world. Violations of this process, which we call mental pathologies, lead to dissonances and even complete disintegration of the picture. <br />How do billions of neurons perform this symphony? In other words, how does the brain create a coherent and integral model of reality while maintaining the identity of each encoded signal? In neuroscience, this question is called the binding problem. <br />The harmony of the Mind is a physical phenomenon, and it must be explained physically. The author solves this riddle, based on the Theory of Energy Harmony and the Teleological Transduction Theory developed in the previous volumes of the series. The book describes the physical binding mechanism that makes the symphony of the Mind harmonious and reveals the subtle nuances of its physiological implementation in the brain.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/63639737" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="13dda55c61a0512833a5c9e582714a09" rel="nofollow" data-download="{"attachment_id":107359184,"asset_id":63639737,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/107359184/download_file?st=MTczNzI5NjIxNCw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="11170632" href="https://independent.academia.edu/stanislavtregubcom">Stanislav Tregub</a><script data-card-contents-for-user="11170632" type="text/json">{"id":11170632,"first_name":"Stanislav","last_name":"Tregub","domain_name":"independent","page_name":"stanislavtregubcom","display_name":"Stanislav Tregub","profile_url":"https://independent.academia.edu/stanislavtregubcom?f_ri=12950","photo":"https://0.academia-photos.com/11170632/40670091/58694311/s65_stanislav.tregub.jpg"}</script></span></span></li><li class="js-paper-rank-work_63639737 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="63639737"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 63639737, container: ".js-paper-rank-work_63639737", }); 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$(".js-view-count[data-work-id=63639737]").text(description); $(".js-view-count-work_63639737").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_63639737").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="63639737"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">20</a>  </div><span class="InlineList-item-text u-textTruncate u-pl10x"><a class="InlineList-item-text" data-has-card-for-ri="161" rel="nofollow" href="https://www.academia.edu/Documents/in/Neuroscience">Neuroscience</a>, <script data-card-contents-for-ri="161" type="text/json">{"id":161,"name":"Neuroscience","url":"https://www.academia.edu/Documents/in/Neuroscience?f_ri=12950","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="168" rel="nofollow" href="https://www.academia.edu/Documents/in/Human_Physiology">Human Physiology</a>, <script data-card-contents-for-ri="168" type="text/json">{"id":168,"name":"Human Physiology","url":"https://www.academia.edu/Documents/in/Human_Physiology?f_ri=12950","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="221" rel="nofollow" href="https://www.academia.edu/Documents/in/Psychology">Psychology</a>, <script data-card-contents-for-ri="221" type="text/json">{"id":221,"name":"Psychology","url":"https://www.academia.edu/Documents/in/Psychology?f_ri=12950","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="242" rel="nofollow" href="https://www.academia.edu/Documents/in/Personality_Psychology">Personality Psychology</a><script data-card-contents-for-ri="242" type="text/json">{"id":242,"name":"Personality Psychology","url":"https://www.academia.edu/Documents/in/Personality_Psychology?f_ri=12950","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=63639737]'), work: {"id":63639737,"title":"Harmonies of the Mind: Physics and Physiology of Self","created_at":"2021-12-09T04:00:21.732-08:00","url":"https://www.academia.edu/63639737/Harmonies_of_the_Mind_Physics_and_Physiology_of_Self?f_ri=12950","dom_id":"work_63639737","summary":"The brain is an orchestra playing a harmonious symphony of the Mind that we experience as the unity of our picture of the world and ourselves in this world. Violations of this process, which we call mental pathologies, lead to dissonances and even complete disintegration of the picture.\r\nHow do billions of neurons perform this symphony? In other words, how does the brain create a coherent and integral model of reality while maintaining the identity of each encoded signal? In neuroscience, this question is called the binding problem.\r\nThe harmony of the Mind is a physical phenomenon, and it must be explained physically. The author solves this riddle, based on the Theory of Energy Harmony and the Teleological Transduction Theory developed in the previous volumes of the series. The book describes the physical binding mechanism that makes the symphony of the Mind harmonious and reveals the subtle nuances of its physiological implementation in the brain.","downloadable_attachments":[{"id":107359184,"asset_id":63639737,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":11170632,"first_name":"Stanislav","last_name":"Tregub","domain_name":"independent","page_name":"stanislavtregubcom","display_name":"Stanislav Tregub","profile_url":"https://independent.academia.edu/stanislavtregubcom?f_ri=12950","photo":"https://0.academia-photos.com/11170632/40670091/58694311/s65_stanislav.tregub.jpg"}],"research_interests":[{"id":161,"name":"Neuroscience","url":"https://www.academia.edu/Documents/in/Neuroscience?f_ri=12950","nofollow":true},{"id":168,"name":"Human Physiology","url":"https://www.academia.edu/Documents/in/Human_Physiology?f_ri=12950","nofollow":true},{"id":221,"name":"Psychology","url":"https://www.academia.edu/Documents/in/Psychology?f_ri=12950","nofollow":true},{"id":242,"name":"Personality Psychology","url":"https://www.academia.edu/Documents/in/Personality_Psychology?f_ri=12950","nofollow":true},{"id":465,"name":"Artificial Intelligence","url":"https://www.academia.edu/Documents/in/Artificial_Intelligence?f_ri=12950"},{"id":491,"name":"Information Technology","url":"https://www.academia.edu/Documents/in/Information_Technology?f_ri=12950"},{"id":502,"name":"Biophysics","url":"https://www.academia.edu/Documents/in/Biophysics?f_ri=12950"},{"id":635,"name":"Psychiatry","url":"https://www.academia.edu/Documents/in/Psychiatry?f_ri=12950"},{"id":671,"name":"Music","url":"https://www.academia.edu/Documents/in/Music?f_ri=12950"},{"id":806,"name":"Philosophy of Mind","url":"https://www.academia.edu/Documents/in/Philosophy_of_Mind?f_ri=12950"},{"id":2639,"name":"Neuroimaging","url":"https://www.academia.edu/Documents/in/Neuroimaging?f_ri=12950"},{"id":3723,"name":"History of Science","url":"https://www.academia.edu/Documents/in/History_of_Science?f_ri=12950"},{"id":5398,"name":"Biotechnology","url":"https://www.academia.edu/Documents/in/Biotechnology?f_ri=12950"},{"id":8014,"name":"Life Sciences","url":"https://www.academia.edu/Documents/in/Life_Sciences?f_ri=12950"},{"id":9038,"name":"Digital Signal Processing","url":"https://www.academia.edu/Documents/in/Digital_Signal_Processing?f_ri=12950"},{"id":12950,"name":"Neural Oscillations","url":"https://www.academia.edu/Documents/in/Neural_Oscillations?f_ri=12950"},{"id":15250,"name":"Synchronization","url":"https://www.academia.edu/Documents/in/Synchronization?f_ri=12950"},{"id":38831,"name":"Signal Transduction","url":"https://www.academia.edu/Documents/in/Signal_Transduction?f_ri=12950"},{"id":85618,"name":"Theory of Harmony","url":"https://www.academia.edu/Documents/in/Theory_of_Harmony?f_ri=12950"},{"id":174781,"name":"Oscillations","url":"https://www.academia.edu/Documents/in/Oscillations?f_ri=12950"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_47937444" data-work_id="47937444" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/47937444/Hippocampal_oscillatory_dynamics_and_sleep_atonia_are_altered_in_an_animal_model_of_fibromyalgia_implications_in_the_search_for_biomarkers">Hippocampal oscillatory dynamics and sleep atonia are altered in an animal model of fibromyalgia: implications in the search for biomarkers</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">The pathogenesis of fibromyalgia is still unknown. Core symptoms include pain, depression and sleep disturbances with high comorbidity, suggesting alterations in the monoaminergic system as a common origin of this disease. The... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_47937444" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">The pathogenesis of fibromyalgia is still unknown. Core symptoms include pain, depression and sleep disturbances with high comorbidity, suggesting alterations in the monoaminergic system as a common origin of this disease. The reserpine-induced myalgia model (RIM) lowers pain thresholds and produces depressive-like symptoms. The present work aims to evaluate temporal dynamics in the oscillatory profiles and motor activity during sleep in this model and to evaluate if the model mimics the sleep disorders that occur in fibromyalgia patients. Hippocampal and EMG activity were recorded in chronically implanted rats. Following 3 days of basal recordings, reserpine was administered on 3 consecutive days to achieve the RIM. Post-reserpine recordings were taken on alternate days for 21 days. Reserpine induced changes in the sleep architecture with more transitions between states, and a different pattern between the administration period and post-reserpine weeks. Administration days were characterized by a larger amount of REM sleep with dominant theta waves without atonia. Following the reserpinization, theta oscillations were always more fragmented and with lower frequency. On the post-reserpine days, sleep was dominated by slow-wave sleep with fast intrusions and reduced hierarchical coupling with spindles and ripples. Simultaneous electromyography recordings also showed muscle twitches during sleep and the dissociation of theta activity and muscle atonia. Abnormally high slow waves, alpha/delta intrusions, frequent transitions and muscle twitches are common traits in fibromyalgia. Therefore, our analyses support the validity of the reserpine-induced myalgia model to study sleep disorders in fibromyalgia, and provide new insights into the research of oscillographic biomarkers. This article is protected by copyright. All rights reserved.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/47937444" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="d64343493b59e3456a10328a84acc342" rel="nofollow" data-download="{"attachment_id":68219284,"asset_id":47937444,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/68219284/download_file?st=MTczNzI5NjIxNCw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="4734661" href="https://uv.academia.edu/AnaSecas">Ana Cervera Ferri</a><script data-card-contents-for-user="4734661" type="text/json">{"id":4734661,"first_name":"Ana","last_name":"Cervera Ferri","domain_name":"uv","page_name":"AnaSecas","display_name":"Ana Cervera Ferri","profile_url":"https://uv.academia.edu/AnaSecas?f_ri=12950","photo":"https://0.academia-photos.com/4734661/2004245/16845048/s65_ana.cervera_ferri.jpg"}</script></span></span></li><li class="js-paper-rank-work_47937444 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="47937444"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 47937444, container: ".js-paper-rank-work_47937444", }); 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$(".js-view-count[data-work-id=47937444]").text(description); $(".js-view-count-work_47937444").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_47937444").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="47937444"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">7</a>  </div><span class="InlineList-item-text u-textTruncate u-pl9x"><a class="InlineList-item-text" data-has-card-for-ri="12950" rel="nofollow" href="https://www.academia.edu/Documents/in/Neural_Oscillations">Neural Oscillations</a>, <script data-card-contents-for-ri="12950" type="text/json">{"id":12950,"name":"Neural Oscillations","url":"https://www.academia.edu/Documents/in/Neural_Oscillations?f_ri=12950","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="57556" rel="nofollow" href="https://www.academia.edu/Documents/in/Hippocampus">Hippocampus</a>, <script data-card-contents-for-ri="57556" type="text/json">{"id":57556,"name":"Hippocampus","url":"https://www.academia.edu/Documents/in/Hippocampus?f_ri=12950","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="88061" rel="nofollow" href="https://www.academia.edu/Documents/in/Fibromyalgia">Fibromyalgia</a>, <script data-card-contents-for-ri="88061" type="text/json">{"id":88061,"name":"Fibromyalgia","url":"https://www.academia.edu/Documents/in/Fibromyalgia?f_ri=12950","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="133324" rel="nofollow" href="https://www.academia.edu/Documents/in/Sleep">Sleep</a><script data-card-contents-for-ri="133324" type="text/json">{"id":133324,"name":"Sleep","url":"https://www.academia.edu/Documents/in/Sleep?f_ri=12950","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=47937444]'), work: {"id":47937444,"title":"Hippocampal oscillatory dynamics and sleep atonia are altered in an animal model of fibromyalgia: implications in the search for biomarkers","created_at":"2021-05-03T13:16:17.882-07:00","url":"https://www.academia.edu/47937444/Hippocampal_oscillatory_dynamics_and_sleep_atonia_are_altered_in_an_animal_model_of_fibromyalgia_implications_in_the_search_for_biomarkers?f_ri=12950","dom_id":"work_47937444","summary":"The pathogenesis of fibromyalgia is still unknown. Core symptoms include pain, depression and sleep disturbances with high comorbidity, suggesting alterations in the monoaminergic system as a common origin of this disease. The reserpine-induced myalgia model (RIM) lowers pain thresholds and produces depressive-like symptoms. The present work aims to evaluate temporal dynamics in the oscillatory profiles and motor activity during sleep in this model and to evaluate if the model mimics the sleep disorders that occur in fibromyalgia patients. Hippocampal and EMG activity were recorded in chronically implanted rats. Following 3 days of basal recordings, reserpine was administered on 3 consecutive days to achieve the RIM. Post-reserpine recordings were taken on alternate days for 21 days. Reserpine induced changes in the sleep architecture with more transitions between states, and a different pattern between the administration period and post-reserpine weeks. Administration days were characterized by a larger amount of REM sleep with dominant theta waves without atonia. Following the reserpinization, theta oscillations were always more fragmented and with lower frequency. On the post-reserpine days, sleep was dominated by slow-wave sleep with fast intrusions and reduced hierarchical coupling with spindles and ripples. Simultaneous electromyography recordings also showed muscle twitches during sleep and the dissociation of theta activity and muscle atonia. Abnormally high slow waves, alpha/delta intrusions, frequent transitions and muscle twitches are common traits in fibromyalgia. Therefore, our analyses support the validity of the reserpine-induced myalgia model to study sleep disorders in fibromyalgia, and provide new insights into the research of oscillographic biomarkers. This article is protected by copyright. All rights reserved.","downloadable_attachments":[{"id":68219284,"asset_id":47937444,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":4734661,"first_name":"Ana","last_name":"Cervera Ferri","domain_name":"uv","page_name":"AnaSecas","display_name":"Ana Cervera Ferri","profile_url":"https://uv.academia.edu/AnaSecas?f_ri=12950","photo":"https://0.academia-photos.com/4734661/2004245/16845048/s65_ana.cervera_ferri.jpg"}],"research_interests":[{"id":12950,"name":"Neural Oscillations","url":"https://www.academia.edu/Documents/in/Neural_Oscillations?f_ri=12950","nofollow":true},{"id":57556,"name":"Hippocampus","url":"https://www.academia.edu/Documents/in/Hippocampus?f_ri=12950","nofollow":true},{"id":88061,"name":"Fibromyalgia","url":"https://www.academia.edu/Documents/in/Fibromyalgia?f_ri=12950","nofollow":true},{"id":133324,"name":"Sleep","url":"https://www.academia.edu/Documents/in/Sleep?f_ri=12950","nofollow":true},{"id":186234,"name":"Medical Physiology","url":"https://www.academia.edu/Documents/in/Medical_Physiology?f_ri=12950"},{"id":1002863,"name":"Comparative Neurology","url":"https://www.academia.edu/Documents/in/Comparative_Neurology?f_ri=12950"},{"id":1239755,"name":"Neurosciences","url":"https://www.academia.edu/Documents/in/Neurosciences?f_ri=12950"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_23324379" data-work_id="23324379" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/23324379/The_Human_Oscillome_and_Its_Explanatory_Potential">The Human Oscillome and Its Explanatory Potential</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">My intention in this piece is to briefly outline a novel hypothesis regarding the neurobiological implementation of feature-set binding, the labeling of feature-sets, and the resolution of linguistic dependencies arising from the cyclic... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_23324379" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">My intention in this piece is to briefly outline a novel hypothesis regarding the neurobiological implementation of feature-set binding, the labeling of feature-sets, and the resolution of linguistic dependencies arising from the cyclic combination of these labeled objects. One of the numerous motivations for this was reading Robert C. Berwick & Noam Chomsky's (B&C) recent book Why Only Us: Language and Evolution (Berwick & Chomsky 2016; henceforth WOU), which struck me as moderately comprehensive in its interdisciplinary scope (including good critical commentary on recent work in comparative neuroprimatology and theoretical biology) but severely impoverished in its range of linking hypotheses between these disciplines.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/23324379" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="89ddfe2d6625fecf661b13b3c23eaf97" rel="nofollow" data-download="{"attachment_id":43782961,"asset_id":23324379,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/43782961/download_file?st=MTczNzI5NjIxNCw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="1267216" href="https://tmc.academia.edu/ElliotMurphy">Elliot Murphy</a><script data-card-contents-for-user="1267216" type="text/json">{"id":1267216,"first_name":"Elliot","last_name":"Murphy","domain_name":"tmc","page_name":"ElliotMurphy","display_name":"Elliot Murphy","profile_url":"https://tmc.academia.edu/ElliotMurphy?f_ri=12950","photo":"https://0.academia-photos.com/1267216/763325/39030425/s65_elliot.murphy.jpeg"}</script></span></span></li><li class="js-paper-rank-work_23324379 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="23324379"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 23324379, container: ".js-paper-rank-work_23324379", }); });</script></li><li class="js-percentile-work_23324379 InlineList-item InlineList-item--bordered hidden u-tcGrayDark"><span class="percentile-widget hidden"><span class="u-mr2x percentile-widget" style="display: none">•</span><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 23324379; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-percentile-work_23324379"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></li><li class="js-view-count-work_23324379 InlineList-item InlineList-item--bordered hidden"><div><span><span class="js-view-count view-count u-mr2x" data-work-id="23324379"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 23324379; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=23324379]").text(description); $(".js-view-count-work_23324379").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_23324379").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="23324379"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">31</a>  </div><span class="InlineList-item-text u-textTruncate u-pl10x"><a class="InlineList-item-text" data-has-card-for-ri="158" rel="nofollow" href="https://www.academia.edu/Documents/in/Marine_Biology">Marine Biology</a>, <script data-card-contents-for-ri="158" type="text/json">{"id":158,"name":"Marine Biology","url":"https://www.academia.edu/Documents/in/Marine_Biology?f_ri=12950","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="161" rel="nofollow" href="https://www.academia.edu/Documents/in/Neuroscience">Neuroscience</a>, <script data-card-contents-for-ri="161" type="text/json">{"id":161,"name":"Neuroscience","url":"https://www.academia.edu/Documents/in/Neuroscience?f_ri=12950","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="251" rel="nofollow" href="https://www.academia.edu/Documents/in/Neuropsychology">Neuropsychology</a>, <script data-card-contents-for-ri="251" type="text/json">{"id":251,"name":"Neuropsychology","url":"https://www.academia.edu/Documents/in/Neuropsychology?f_ri=12950","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="422" rel="nofollow" href="https://www.academia.edu/Documents/in/Computer_Science">Computer Science</a><script data-card-contents-for-ri="422" type="text/json">{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science?f_ri=12950","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=23324379]'), work: {"id":23324379,"title":"The Human Oscillome and Its Explanatory Potential","created_at":"2016-03-16T07:10:26.345-07:00","url":"https://www.academia.edu/23324379/The_Human_Oscillome_and_Its_Explanatory_Potential?f_ri=12950","dom_id":"work_23324379","summary":"My intention in this piece is to briefly outline a novel hypothesis regarding the neurobiological implementation of feature-set binding, the labeling of feature-sets, and the resolution of linguistic dependencies arising from the cyclic combination of these labeled objects. One of the numerous motivations for this was reading Robert C. Berwick \u0026 Noam Chomsky's (B\u0026C) recent book Why Only Us: Language and Evolution (Berwick \u0026 Chomsky 2016; henceforth WOU), which struck me as moderately comprehensive in its interdisciplinary scope (including good critical commentary on recent work in comparative neuroprimatology and theoretical biology) but severely impoverished in its range of linking hypotheses between these disciplines. ","downloadable_attachments":[{"id":43782961,"asset_id":23324379,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":1267216,"first_name":"Elliot","last_name":"Murphy","domain_name":"tmc","page_name":"ElliotMurphy","display_name":"Elliot Murphy","profile_url":"https://tmc.academia.edu/ElliotMurphy?f_ri=12950","photo":"https://0.academia-photos.com/1267216/763325/39030425/s65_elliot.murphy.jpeg"}],"research_interests":[{"id":158,"name":"Marine Biology","url":"https://www.academia.edu/Documents/in/Marine_Biology?f_ri=12950","nofollow":true},{"id":161,"name":"Neuroscience","url":"https://www.academia.edu/Documents/in/Neuroscience?f_ri=12950","nofollow":true},{"id":251,"name":"Neuropsychology","url":"https://www.academia.edu/Documents/in/Neuropsychology?f_ri=12950","nofollow":true},{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science?f_ri=12950","nofollow":true},{"id":465,"name":"Artificial Intelligence","url":"https://www.academia.edu/Documents/in/Artificial_Intelligence?f_ri=12950"},{"id":623,"name":"Neurology","url":"https://www.academia.edu/Documents/in/Neurology?f_ri=12950"},{"id":803,"name":"Philosophy","url":"https://www.academia.edu/Documents/in/Philosophy?f_ri=12950"},{"id":806,"name":"Philosophy of Mind","url":"https://www.academia.edu/Documents/in/Philosophy_of_Mind?f_ri=12950"},{"id":807,"name":"Philosophy Of Language","url":"https://www.academia.edu/Documents/in/Philosophy_Of_Language?f_ri=12950"},{"id":813,"name":"Political Philosophy","url":"https://www.academia.edu/Documents/in/Political_Philosophy?f_ri=12950"},{"id":821,"name":"Philosophy of Science","url":"https://www.academia.edu/Documents/in/Philosophy_of_Science?f_ri=12950"},{"id":902,"name":"Philosophy Of Religion","url":"https://www.academia.edu/Documents/in/Philosophy_Of_Religion?f_ri=12950"},{"id":1200,"name":"Languages and Linguistics","url":"https://www.academia.edu/Documents/in/Languages_and_Linguistics?f_ri=12950"},{"id":1237,"name":"Social Sciences","url":"https://www.academia.edu/Documents/in/Social_Sciences?f_ri=12950"},{"id":1432,"name":"Natural Language Processing","url":"https://www.academia.edu/Documents/in/Natural_Language_Processing?f_ri=12950"},{"id":2509,"name":"Philosophy Of Mathematics","url":"https://www.academia.edu/Documents/in/Philosophy_Of_Mathematics?f_ri=12950"},{"id":2513,"name":"Molecular Biology","url":"https://www.academia.edu/Documents/in/Molecular_Biology?f_ri=12950"},{"id":2827,"name":"Mental Health","url":"https://www.academia.edu/Documents/in/Mental_Health?f_ri=12950"},{"id":3979,"name":"English language","url":"https://www.academia.edu/Documents/in/English_language?f_ri=12950"},{"id":4937,"name":"Theory of Mind","url":"https://www.academia.edu/Documents/in/Theory_of_Mind?f_ri=12950"},{"id":6671,"name":"Syntax","url":"https://www.academia.edu/Documents/in/Syntax?f_ri=12950"},{"id":6728,"name":"Philosophy Of Law","url":"https://www.academia.edu/Documents/in/Philosophy_Of_Law?f_ri=12950"},{"id":7710,"name":"Biology","url":"https://www.academia.edu/Documents/in/Biology?f_ri=12950"},{"id":11598,"name":"Neural Networks","url":"https://www.academia.edu/Documents/in/Neural_Networks?f_ri=12950"},{"id":12950,"name":"Neural Oscillations","url":"https://www.academia.edu/Documents/in/Neural_Oscillations?f_ri=12950"},{"id":18981,"name":"Biolinguistics","url":"https://www.academia.edu/Documents/in/Biolinguistics?f_ri=12950"},{"id":21548,"name":"Cognitive Neuroscience","url":"https://www.academia.edu/Documents/in/Cognitive_Neuroscience?f_ri=12950"},{"id":30601,"name":"Behavioral Neuroscience","url":"https://www.academia.edu/Documents/in/Behavioral_Neuroscience?f_ri=12950"},{"id":54123,"name":"Artificial Neural Networks","url":"https://www.academia.edu/Documents/in/Artificial_Neural_Networks?f_ri=12950"},{"id":620285,"name":"History of Philosophy","url":"https://www.academia.edu/Documents/in/History_of_Philosophy?f_ri=12950"},{"id":1239755,"name":"Neurosciences","url":"https://www.academia.edu/Documents/in/Neurosciences?f_ri=12950"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_30838968 coauthored" data-work_id="30838968" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/30838968/Brain_Oscillations_and_the_Importance_of_Waveform_Shape">Brain Oscillations and the Importance of Waveform Shape</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">Oscillations are a prevalent feature of brain recordings. They are believed to play key roles in neural communication and computation. Current analysis methods for studying neural oscillations often implicitly assume the oscillations are... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_30838968" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">Oscillations are a prevalent feature of brain recordings. They are believed to play key roles in neural communication and computation. Current analysis methods for studying neural oscillations often implicitly assume the oscillations are sinusoidal. While these approaches have proven fruitful, here we show that there are numerous instances in which neural oscillations are nonsinusoidal. We highlight approaches to characterize nonsinusoidal features and account for them in traditional spectral analysis. Rather than being a nuisance, we discuss how these nonsinusoidal features may provide critical, heretofore overlooked physiological information related to neural communication, computation, and cognition. Trends • Properties of neural oscillations are commonly correlated to disease or behavior states. These measures are mostly derived using traditional spectral analysis techniques that assume a sinusoidal basis. • Electrical recordings from many brain regions, at multiple spatial scales, exhibit neural oscillations that are nonsinusoidal. • New methods have been developed to quantify the nonsinusoidal features of oscillations and account for these features when using traditional spectral analysis. • Features of oscillatory waveform shape have been related to physiological processes and behaviors. • Manipulating features of stimulation waveforms changes the effects of rhythmic electrical stimulation.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/30838968" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="6c54db474f132390688e430cdd0c32e1" rel="nofollow" data-download="{"attachment_id":51269675,"asset_id":30838968,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/51269675/download_file?st=MTczNzI5NjIxNCw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="4849622" href="https://ucsd.academia.edu/BradleyVoytek">Bradley Voytek</a><script data-card-contents-for-user="4849622" type="text/json">{"id":4849622,"first_name":"Bradley","last_name":"Voytek","domain_name":"ucsd","page_name":"BradleyVoytek","display_name":"Bradley Voytek","profile_url":"https://ucsd.academia.edu/BradleyVoytek?f_ri=12950","photo":"https://0.academia-photos.com/4849622/2474932/2875354/s65_bradley.voytek.jpeg"}</script></span></span><span class="u-displayInlineBlock InlineList-item-text"> and <span class="u-textDecorationUnderline u-clickable InlineList-item-text js-work-more-authors-30838968">+1</span><div class="hidden js-additional-users-30838968"><div><span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a href="https://independent.academia.edu/ScottCole7">Scott Cole</a></span></div></div></span><script>(function(){ var popoverSettings = { el: $('.js-work-more-authors-30838968'), placement: 'bottom', hide_delay: 200, html: true, content: function(){ return $('.js-additional-users-30838968').html(); } } new HoverPopover(popoverSettings); })();</script></li><li class="js-paper-rank-work_30838968 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="30838968"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 30838968, container: ".js-paper-rank-work_30838968", }); });</script></li><li class="js-percentile-work_30838968 InlineList-item InlineList-item--bordered hidden u-tcGrayDark"><span class="percentile-widget hidden"><span class="u-mr2x percentile-widget" style="display: none">•</span><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 30838968; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-percentile-work_30838968"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></li><li class="js-view-count-work_30838968 InlineList-item InlineList-item--bordered hidden"><div><span><span class="js-view-count view-count u-mr2x" data-work-id="30838968"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 30838968; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=30838968]").text(description); $(".js-view-count-work_30838968").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_30838968").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="30838968"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">4</a>  </div><span class="InlineList-item-text u-textTruncate u-pl9x"><a class="InlineList-item-text" data-has-card-for-ri="161" rel="nofollow" href="https://www.academia.edu/Documents/in/Neuroscience">Neuroscience</a>, <script data-card-contents-for-ri="161" type="text/json">{"id":161,"name":"Neuroscience","url":"https://www.academia.edu/Documents/in/Neuroscience?f_ri=12950","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="12950" rel="nofollow" href="https://www.academia.edu/Documents/in/Neural_Oscillations">Neural Oscillations</a>, <script data-card-contents-for-ri="12950" type="text/json">{"id":12950,"name":"Neural Oscillations","url":"https://www.academia.edu/Documents/in/Neural_Oscillations?f_ri=12950","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="15594" rel="nofollow" href="https://www.academia.edu/Documents/in/Systems_Neuroscience">Systems Neuroscience</a>, <script data-card-contents-for-ri="15594" type="text/json">{"id":15594,"name":"Systems Neuroscience","url":"https://www.academia.edu/Documents/in/Systems_Neuroscience?f_ri=12950","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="21548" rel="nofollow" href="https://www.academia.edu/Documents/in/Cognitive_Neuroscience">Cognitive Neuroscience</a><script data-card-contents-for-ri="21548" type="text/json">{"id":21548,"name":"Cognitive Neuroscience","url":"https://www.academia.edu/Documents/in/Cognitive_Neuroscience?f_ri=12950","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=30838968]'), work: {"id":30838968,"title":"Brain Oscillations and the Importance of Waveform Shape","created_at":"2017-01-09T14:36:13.211-08:00","url":"https://www.academia.edu/30838968/Brain_Oscillations_and_the_Importance_of_Waveform_Shape?f_ri=12950","dom_id":"work_30838968","summary":"Oscillations are a prevalent feature of brain recordings. They are believed to play key roles in neural communication and computation. Current analysis methods for studying neural oscillations often implicitly assume the oscillations are sinusoidal. While these approaches have proven fruitful, here we show that there are numerous instances in which neural oscillations are nonsinusoidal. We highlight approaches to characterize nonsinusoidal features and account for them in traditional spectral analysis. Rather than being a nuisance, we discuss how these nonsinusoidal features may provide critical, heretofore overlooked physiological information related to neural communication, computation, and cognition. Trends • Properties of neural oscillations are commonly correlated to disease or behavior states. These measures are mostly derived using traditional spectral analysis techniques that assume a sinusoidal basis. • Electrical recordings from many brain regions, at multiple spatial scales, exhibit neural oscillations that are nonsinusoidal. • New methods have been developed to quantify the nonsinusoidal features of oscillations and account for these features when using traditional spectral analysis. • Features of oscillatory waveform shape have been related to physiological processes and behaviors. • Manipulating features of stimulation waveforms changes the effects of rhythmic electrical stimulation.","downloadable_attachments":[{"id":51269675,"asset_id":30838968,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":4849622,"first_name":"Bradley","last_name":"Voytek","domain_name":"ucsd","page_name":"BradleyVoytek","display_name":"Bradley Voytek","profile_url":"https://ucsd.academia.edu/BradleyVoytek?f_ri=12950","photo":"https://0.academia-photos.com/4849622/2474932/2875354/s65_bradley.voytek.jpeg"},{"id":58712971,"first_name":"Scott","last_name":"Cole","domain_name":"independent","page_name":"ScottCole7","display_name":"Scott Cole","profile_url":"https://independent.academia.edu/ScottCole7?f_ri=12950","photo":"/images/s65_no_pic.png"}],"research_interests":[{"id":161,"name":"Neuroscience","url":"https://www.academia.edu/Documents/in/Neuroscience?f_ri=12950","nofollow":true},{"id":12950,"name":"Neural Oscillations","url":"https://www.academia.edu/Documents/in/Neural_Oscillations?f_ri=12950","nofollow":true},{"id":15594,"name":"Systems Neuroscience","url":"https://www.academia.edu/Documents/in/Systems_Neuroscience?f_ri=12950","nofollow":true},{"id":21548,"name":"Cognitive Neuroscience","url":"https://www.academia.edu/Documents/in/Cognitive_Neuroscience?f_ri=12950","nofollow":true}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_7629357 coauthored" data-work_id="7629357" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/7629357/Beat_induced_fluctuations_in_auditory_cortical_beta_band_activity_using_EEG_to_measure_age_related_changes">Beat-induced fluctuations in auditory cortical beta-band activity: using EEG to measure age-related changes</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">People readily extract regularity in rhythmic auditory patterns, enabling prediction of the onset of the next beat. Recent magnetoencephalography (MEG) research suggests that such prediction is reflected by the entrainment of oscillatory... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_7629357" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">People readily extract regularity in rhythmic auditory patterns, enabling prediction of the onset of the next beat. Recent magnetoencephalography (MEG) research suggests that such prediction is reflected by the entrainment of oscillatory networks in the brain to the tempo of the sequence. In particular, induced beta-band oscillatory activity from auditory cortex decreases after each beat onset and rebounds prior to the onset of the next beat across tempi in a predictive manner. The objective of the present study was to examine the development of such oscillatory activity by comparing electroencephalography (EEG) measures of beta-band fluctuations in 7-year-old children to adults. EEG was recorded while participants listened passively to isochronous tone sequences at three tempi (390, 585, and 780 ms for onset-to-onset interval). In adults, induced power in the high beta-band (20-25 Hz) decreased after each tone onset and rebounded prior to the onset of the next tone across tempo conditions, consistent with MEG findings. In children, a similar pattern was measured in the two slower tempo conditions, but was weaker in the fastest condition.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/7629357" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="56ff0663269b9637fa67fbac7653b9b4" rel="nofollow" data-download="{"attachment_id":34172937,"asset_id":7629357,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/34172937/download_file?st=MTczNzI5NjIxNCw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="13570196" href="https://utoronto.academia.edu/LauraCirelli">Laura Cirelli</a><script data-card-contents-for-user="13570196" type="text/json">{"id":13570196,"first_name":"Laura","last_name":"Cirelli","domain_name":"utoronto","page_name":"LauraCirelli","display_name":"Laura Cirelli","profile_url":"https://utoronto.academia.edu/LauraCirelli?f_ri=12950","photo":"https://0.academia-photos.com/13570196/3790350/4436680/s65_laura.cirelli.jpg"}</script></span></span><span class="u-displayInlineBlock InlineList-item-text"> and <span class="u-textDecorationUnderline u-clickable InlineList-item-text js-work-more-authors-7629357">+3</span><div class="hidden js-additional-users-7629357"><div><span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a href="https://mcmaster.academia.edu/FionaManning">Fiona Manning</a></span></div><div><span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a href="https://independent.academia.edu/LaurelTrainor">Laurel Trainor</a></span></div><div><span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a href="https://mcmaster.academia.edu/DanielBosnyak">Daniel Bosnyak</a></span></div></div></span><script>(function(){ var popoverSettings = { el: $('.js-work-more-authors-7629357'), placement: 'bottom', hide_delay: 200, html: true, content: function(){ return $('.js-additional-users-7629357').html(); 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Recent magnetoencephalography (MEG) research suggests that such prediction is reflected by the entrainment of oscillatory networks in the brain to the tempo of the sequence. In particular, induced beta-band oscillatory activity from auditory cortex decreases after each beat onset and rebounds prior to the onset of the next beat across tempi in a predictive manner. The objective of the present study was to examine the development of such oscillatory activity by comparing electroencephalography (EEG) measures of beta-band fluctuations in 7-year-old children to adults. EEG was recorded while participants listened passively to isochronous tone sequences at three tempi (390, 585, and 780 ms for onset-to-onset interval). In adults, induced power in the high beta-band (20-25 Hz) decreased after each tone onset and rebounded prior to the onset of the next tone across tempo conditions, consistent with MEG findings. In children, a similar pattern was measured in the two slower tempo conditions, but was weaker in the fastest condition.","downloadable_attachments":[{"id":34172937,"asset_id":7629357,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":13570196,"first_name":"Laura","last_name":"Cirelli","domain_name":"utoronto","page_name":"LauraCirelli","display_name":"Laura Cirelli","profile_url":"https://utoronto.academia.edu/LauraCirelli?f_ri=12950","photo":"https://0.academia-photos.com/13570196/3790350/4436680/s65_laura.cirelli.jpg"},{"id":2292014,"first_name":"Fiona","last_name":"Manning","domain_name":"mcmaster","page_name":"FionaManning","display_name":"Fiona Manning","profile_url":"https://mcmaster.academia.edu/FionaManning?f_ri=12950","photo":"https://0.academia-photos.com/2292014/845619/10081813/s65_fiona.manning.jpg"},{"id":21448534,"first_name":"Laurel","last_name":"Trainor","domain_name":"independent","page_name":"LaurelTrainor","display_name":"Laurel Trainor","profile_url":"https://independent.academia.edu/LaurelTrainor?f_ri=12950","photo":"https://0.academia-photos.com/21448534/11325670/12634774/s65_laurel.trainor.jpg"},{"id":40924098,"first_name":"Daniel","last_name":"Bosnyak","domain_name":"mcmaster","page_name":"DanielBosnyak","display_name":"Daniel Bosnyak","profile_url":"https://mcmaster.academia.edu/DanielBosnyak?f_ri=12950","photo":"/images/s65_no_pic.png"}],"research_interests":[{"id":161,"name":"Neuroscience","url":"https://www.academia.edu/Documents/in/Neuroscience?f_ri=12950","nofollow":true},{"id":236,"name":"Cognitive Psychology","url":"https://www.academia.edu/Documents/in/Cognitive_Psychology?f_ri=12950","nofollow":true},{"id":4583,"name":"Child Development","url":"https://www.academia.edu/Documents/in/Child_Development?f_ri=12950","nofollow":true},{"id":12950,"name":"Neural Oscillations","url":"https://www.academia.edu/Documents/in/Neural_Oscillations?f_ri=12950","nofollow":true},{"id":21902,"name":"Time Perception","url":"https://www.academia.edu/Documents/in/Time_Perception?f_ri=12950"},{"id":165039,"name":"Musical Rhythm Theory","url":"https://www.academia.edu/Documents/in/Musical_Rhythm_Theory?f_ri=12950"},{"id":1031021,"name":"Theories of Musical Rhythm","url":"https://www.academia.edu/Documents/in/Theories_of_Musical_Rhythm?f_ri=12950"},{"id":1275856,"name":"Electroencephalography (EEG)","url":"https://www.academia.edu/Documents/in/Electroencephalography_EEG_?f_ri=12950"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_9164415" data-work_id="9164415" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/9164415/The_pairwise_phase_consistency_A_bias_free_measure_of_rhythmic_neuronal_synchronization">The pairwise phase consistency: A bias-free measure of rhythmic neuronal synchronization</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">Oscillatory activity is a widespread phenomenon in nervous systems and has been implicated in numerous functions. Signals that are generated by two separate neuronal sources often demonstrate a consistent phase-relationship in a... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_9164415" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">Oscillatory activity is a widespread phenomenon in nervous systems and has been implicated in numerous functions. Signals that are generated by two separate neuronal sources often demonstrate a consistent phase-relationship in a particular frequency-band, i.e., they demonstrate rhythmic neuronal synchronization. This consistency is conventionally measured by the PLV (phase-locking value) or the spectral coherence measure. Both statistical measures suffer from significant bias, in that their sample estimates overestimate the population statistics for finite sample sizes. This is a significant problem in the neurosciences where statistical comparisons are often made between conditions with a different number of trials or between neurons with a different number of spikes. We introduce a new circular statistic, the PPC (pairwise phase consistency). We demonstrate that the sample estimate of the PPC is a bias-free and consistent estimator of its corresponding population parameter. We show, both analytically and by means of numerical simulations, that the population statistic of the PPC is equivalent to the population statistic of the squared PLV. The variance and mean squared error of the PPC and PLV are compared. Finally, we demonstrate the practical relevance of the method in actual neuronal data recorded from the orbitofrontal cortex of rats that engage in a two-odour discrimination task. We find a strong increase in rhythmic synchronization of spikes relative to the local field potential (as measured by the PPC) for a wide range of low frequencies (including the thetaband) during the anticipation of sucrose delivery in comparison to the anticipation of quinine delivery. address: <a href="mailto:M.A.Vinck@uva.nl" rel="nofollow">M.A.Vinck@uva.nl</a> (M. Vinck). 1 MV conceived the original idea of the pairwise phase consistency and was primarily responsible for theoretical and data analysis.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/9164415" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="b71780eac1c5a43af3d6bc3a611819c9" rel="nofollow" data-download="{"attachment_id":47864906,"asset_id":9164415,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/47864906/download_file?st=MTczNzI5NjIxNCw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="20921616" href="https://yorku.academia.edu/ThiloWomelsdorf">Thilo Womelsdorf</a><script data-card-contents-for-user="20921616" type="text/json">{"id":20921616,"first_name":"Thilo","last_name":"Womelsdorf","domain_name":"yorku","page_name":"ThiloWomelsdorf","display_name":"Thilo Womelsdorf","profile_url":"https://yorku.academia.edu/ThiloWomelsdorf?f_ri=12950","photo":"/images/s65_no_pic.png"}</script></span></span></li><li class="js-paper-rank-work_9164415 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="9164415"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 9164415, container: ".js-paper-rank-work_9164415", }); 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Signals that are generated by two separate neuronal sources often demonstrate a consistent phase-relationship in a particular frequency-band, i.e., they demonstrate rhythmic neuronal synchronization. This consistency is conventionally measured by the PLV (phase-locking value) or the spectral coherence measure. Both statistical measures suffer from significant bias, in that their sample estimates overestimate the population statistics for finite sample sizes. This is a significant problem in the neurosciences where statistical comparisons are often made between conditions with a different number of trials or between neurons with a different number of spikes. We introduce a new circular statistic, the PPC (pairwise phase consistency). We demonstrate that the sample estimate of the PPC is a bias-free and consistent estimator of its corresponding population parameter. We show, both analytically and by means of numerical simulations, that the population statistic of the PPC is equivalent to the population statistic of the squared PLV. The variance and mean squared error of the PPC and PLV are compared. Finally, we demonstrate the practical relevance of the method in actual neuronal data recorded from the orbitofrontal cortex of rats that engage in a two-odour discrimination task. We find a strong increase in rhythmic synchronization of spikes relative to the local field potential (as measured by the PPC) for a wide range of low frequencies (including the thetaband) during the anticipation of sucrose delivery in comparison to the anticipation of quinine delivery. address: M.A.Vinck@uva.nl (M. Vinck). 1 MV conceived the original idea of the pairwise phase consistency and was primarily responsible for theoretical and data analysis.","downloadable_attachments":[{"id":47864906,"asset_id":9164415,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":20921616,"first_name":"Thilo","last_name":"Womelsdorf","domain_name":"yorku","page_name":"ThiloWomelsdorf","display_name":"Thilo Womelsdorf","profile_url":"https://yorku.academia.edu/ThiloWomelsdorf?f_ri=12950","photo":"/images/s65_no_pic.png"}],"research_interests":[{"id":161,"name":"Neuroscience","url":"https://www.academia.edu/Documents/in/Neuroscience?f_ri=12950","nofollow":true},{"id":237,"name":"Cognitive Science","url":"https://www.academia.edu/Documents/in/Cognitive_Science?f_ri=12950","nofollow":true},{"id":428,"name":"Algorithms","url":"https://www.academia.edu/Documents/in/Algorithms?f_ri=12950","nofollow":true},{"id":12950,"name":"Neural Oscillations","url":"https://www.academia.edu/Documents/in/Neural_Oscillations?f_ri=12950","nofollow":true},{"id":15250,"name":"Synchronization","url":"https://www.academia.edu/Documents/in/Synchronization?f_ri=12950"},{"id":30372,"name":"Low Frequency","url":"https://www.academia.edu/Documents/in/Low_Frequency?f_ri=12950"},{"id":49021,"name":"Reward","url":"https://www.academia.edu/Documents/in/Reward?f_ri=12950"},{"id":60658,"name":"Numerical Simulation","url":"https://www.academia.edu/Documents/in/Numerical_Simulation?f_ri=12950"},{"id":69542,"name":"Computer Simulation","url":"https://www.academia.edu/Documents/in/Computer_Simulation?f_ri=12950"},{"id":103260,"name":"Neuroimage","url":"https://www.academia.edu/Documents/in/Neuroimage?f_ri=12950"},{"id":142889,"name":"Mental processes","url":"https://www.academia.edu/Documents/in/Mental_processes?f_ri=12950"},{"id":174781,"name":"Oscillations","url":"https://www.academia.edu/Documents/in/Oscillations?f_ri=12950"},{"id":179028,"name":"Coherence","url":"https://www.academia.edu/Documents/in/Coherence?f_ri=12950"},{"id":190905,"name":"Periodicity","url":"https://www.academia.edu/Documents/in/Periodicity?f_ri=12950"},{"id":193974,"name":"Neurons","url":"https://www.academia.edu/Documents/in/Neurons?f_ri=12950"},{"id":196189,"name":"Sample Size","url":"https://www.academia.edu/Documents/in/Sample_Size?f_ri=12950"},{"id":201456,"name":"Orbitofrontal cortex","url":"https://www.academia.edu/Documents/in/Orbitofrontal_cortex?f_ri=12950"},{"id":274476,"name":"Phase Locking","url":"https://www.academia.edu/Documents/in/Phase_Locking?f_ri=12950"},{"id":375054,"name":"Rats","url":"https://www.academia.edu/Documents/in/Rats?f_ri=12950"},{"id":485404,"name":"Local Field Potential","url":"https://www.academia.edu/Documents/in/Local_Field_Potential?f_ri=12950"},{"id":564879,"name":"Wistar Rats","url":"https://www.academia.edu/Documents/in/Wistar_Rats?f_ri=12950"},{"id":637718,"name":"Nervous System","url":"https://www.academia.edu/Documents/in/Nervous_System?f_ri=12950"},{"id":732917,"name":"Neural Synchronization","url":"https://www.academia.edu/Documents/in/Neural_Synchronization?f_ri=12950"},{"id":850057,"name":"Olfactory perception","url":"https://www.academia.edu/Documents/in/Olfactory_perception?f_ri=12950"},{"id":955727,"name":"Action Potentials","url":"https://www.academia.edu/Documents/in/Action_Potentials?f_ri=12950"},{"id":1239755,"name":"Neurosciences","url":"https://www.academia.edu/Documents/in/Neurosciences?f_ri=12950"},{"id":1257483,"name":"Frontal Lobe","url":"https://www.academia.edu/Documents/in/Frontal_Lobe?f_ri=12950"},{"id":1357280,"name":"Oscillation","url":"https://www.academia.edu/Documents/in/Oscillation?f_ri=12950"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_3338511" data-work_id="3338511" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/3338511/Now_I_am_Ready_Now_I_am_not_The_Influence_of_Pre_TMS_Oscillations_and_Corticomuscular_Coherence_on_Motor_Evoked_Potentials">Now I am Ready--Now I am not: The Influence of Pre-TMS Oscillations and Corticomuscular Coherence on Motor-Evoked Potentials</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">There is a growing body of research on the functional role of oscillatory brain activity. However, its relation to functional connectivity has remained largely obscure. In the sensorimotor system, movement-related changes emerge in the α... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_3338511" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">There is a growing body of research on the functional role of oscillatory brain activity. However, its relation to functional connectivity has remained largely obscure. In the sensorimotor system, movement-related changes emerge in the α (8-14 Hz) and β (15-30 Hz) range (event-related desynchronization, ERD, before and during movement; event-related synchronization, ERS, after movement offset). Some studies suggest that β-ERS may functionally inhibit new movements. According to the gating-by-inhibition framework (Jensen and Mazaheri 2010), we expected that the ERD would go along with increased corticomuscular coupling, and vice versa. By combining transcranial magnetic stimulation (TMS) and electroencephalography, we were directly able to test this hypothesis. In a reaction time task, single TMS pulses were delivered randomly during ERD/ERS to the motor cortex. The motor-evoked potential (MEP) was then related to the β and α frequencies and corticomuscular coherence. Results indicate that MEPs are smaller when preceded by high pre-TMS βband power and low pre-TMS α-band corticomuscular coherence (and vice versa) in a network of motor-relevant areas comprising frontal, parietal, and motor cortices. This confirms that an increase in rhythms that putatively reflect functionally inhibited states goes along with weaker coupling of the respective brain regions.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/3338511" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="80466876125f4358dc3c93e9bce1b4cb" rel="nofollow" data-download="{"attachment_id":50328235,"asset_id":3338511,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/50328235/download_file?st=MTczNzI5NjIxNCw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="50445" href="https://uni-kiel.academia.edu/JulianKeil">Julian Keil</a><script data-card-contents-for-user="50445" type="text/json">{"id":50445,"first_name":"Julian","last_name":"Keil","domain_name":"uni-kiel","page_name":"JulianKeil","display_name":"Julian Keil","profile_url":"https://uni-kiel.academia.edu/JulianKeil?f_ri=12950","photo":"https://0.academia-photos.com/50445/15429/1260170/s65_julian.keil.jpg"}</script></span></span></li><li class="js-paper-rank-work_3338511 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="3338511"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 3338511, container: ".js-paper-rank-work_3338511", }); 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However, its relation to functional connectivity has remained largely obscure. In the sensorimotor system, movement-related changes emerge in the α (8-14 Hz) and β (15-30 Hz) range (event-related desynchronization, ERD, before and during movement; event-related synchronization, ERS, after movement offset). Some studies suggest that β-ERS may functionally inhibit new movements. According to the gating-by-inhibition framework (Jensen and Mazaheri 2010), we expected that the ERD would go along with increased corticomuscular coupling, and vice versa. By combining transcranial magnetic stimulation (TMS) and electroencephalography, we were directly able to test this hypothesis. In a reaction time task, single TMS pulses were delivered randomly during ERD/ERS to the motor cortex. The motor-evoked potential (MEP) was then related to the β and α frequencies and corticomuscular coherence. Results indicate that MEPs are smaller when preceded by high pre-TMS βband power and low pre-TMS α-band corticomuscular coherence (and vice versa) in a network of motor-relevant areas comprising frontal, parietal, and motor cortices. This confirms that an increase in rhythms that putatively reflect functionally inhibited states goes along with weaker coupling of the respective brain regions.","downloadable_attachments":[{"id":50328235,"asset_id":3338511,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":50445,"first_name":"Julian","last_name":"Keil","domain_name":"uni-kiel","page_name":"JulianKeil","display_name":"Julian Keil","profile_url":"https://uni-kiel.academia.edu/JulianKeil?f_ri=12950","photo":"https://0.academia-photos.com/50445/15429/1260170/s65_julian.keil.jpg"}],"research_interests":[{"id":12950,"name":"Neural Oscillations","url":"https://www.academia.edu/Documents/in/Neural_Oscillations?f_ri=12950","nofollow":true},{"id":21548,"name":"Cognitive Neuroscience","url":"https://www.academia.edu/Documents/in/Cognitive_Neuroscience?f_ri=12950","nofollow":true},{"id":42361,"name":"TMS","url":"https://www.academia.edu/Documents/in/TMS?f_ri=12950","nofollow":true},{"id":100293,"name":"TMS\\EEG","url":"https://www.academia.edu/Documents/in/TMS_EEG?f_ri=12950","nofollow":false}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_29812055" data-work_id="29812055" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/29812055/On_brain_modeling_in_resting_state_as_a_network_of_coupled_oscillators">On brain modeling in resting-state as a network of coupled oscillators</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">— The problem of emergent synchronization patterns in a complex network of coupled oscillators has caught scientists' interest in a lot of different disciplines. In particular, from a biological point of view, considerable attention has... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_29812055" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">— The problem of emergent synchronization patterns in a complex network of coupled oscillators has caught scientists' interest in a lot of different disciplines. In particular, from a biological point of view, considerable attention has been recently devoted to the study of the human brain as a network of different cortical regions that show coherent activity during resting-state. In literature, there can be found different large-scale models of resting-state dynamics in health and disease. In this context, the Kuramoto model, a classical model apt to describe oscillators' dynamics, has been extended to capture the spatial displacement and the communication conditions in such brain network. Starting from a previous work in this field [1], we analyze this modified model and compare it with other existing large-scale models. In doing so, our aim is to promote a set of mathematical tools useful to better understand real experimental data in neuroscience and estimate brain dynamics.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/29812055" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="41733480a84b5998f6413f5425a5dda8" rel="nofollow" data-download="{"attachment_id":50276071,"asset_id":29812055,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/50276071/download_file?st=MTczNzI5NjIxNCw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="44557249" href="https://talentgate.academia.edu/ChiaraFavaretto">Chiara Favaretto</a><script data-card-contents-for-user="44557249" type="text/json">{"id":44557249,"first_name":"Chiara","last_name":"Favaretto","domain_name":"talentgate","page_name":"ChiaraFavaretto","display_name":"Chiara Favaretto","profile_url":"https://talentgate.academia.edu/ChiaraFavaretto?f_ri=12950","photo":"https://0.academia-photos.com/44557249/12009741/13380054/s65_chiara.favaretto.jpg"}</script></span></span></li><li class="js-paper-rank-work_29812055 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="29812055"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 29812055, container: ".js-paper-rank-work_29812055", }); 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$(".js-view-count[data-work-id=29812055]").text(description); $(".js-view-count-work_29812055").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_29812055").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="29812055"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">3</a>  </div><span class="InlineList-item-text u-textTruncate u-pl9x"><a class="InlineList-item-text" data-has-card-for-ri="12950" rel="nofollow" href="https://www.academia.edu/Documents/in/Neural_Oscillations">Neural Oscillations</a>, <script data-card-contents-for-ri="12950" type="text/json">{"id":12950,"name":"Neural Oscillations","url":"https://www.academia.edu/Documents/in/Neural_Oscillations?f_ri=12950","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="112292" rel="nofollow" href="https://www.academia.edu/Documents/in/Brain_Networks">Brain Networks</a>, <script data-card-contents-for-ri="112292" type="text/json">{"id":112292,"name":"Brain Networks","url":"https://www.academia.edu/Documents/in/Brain_Networks?f_ri=12950","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="398929" rel="nofollow" href="https://www.academia.edu/Documents/in/Kuramoto_Model">Kuramoto Model</a><script data-card-contents-for-ri="398929" type="text/json">{"id":398929,"name":"Kuramoto Model","url":"https://www.academia.edu/Documents/in/Kuramoto_Model?f_ri=12950","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=29812055]'), work: {"id":29812055,"title":"On brain modeling in resting-state as a network of coupled oscillators","created_at":"2016-11-12T15:16:22.815-08:00","url":"https://www.academia.edu/29812055/On_brain_modeling_in_resting_state_as_a_network_of_coupled_oscillators?f_ri=12950","dom_id":"work_29812055","summary":"— The problem of emergent synchronization patterns in a complex network of coupled oscillators has caught scientists' interest in a lot of different disciplines. In particular, from a biological point of view, considerable attention has been recently devoted to the study of the human brain as a network of different cortical regions that show coherent activity during resting-state. In literature, there can be found different large-scale models of resting-state dynamics in health and disease. In this context, the Kuramoto model, a classical model apt to describe oscillators' dynamics, has been extended to capture the spatial displacement and the communication conditions in such brain network. Starting from a previous work in this field [1], we analyze this modified model and compare it with other existing large-scale models. In doing so, our aim is to promote a set of mathematical tools useful to better understand real experimental data in neuroscience and estimate brain dynamics.","downloadable_attachments":[{"id":50276071,"asset_id":29812055,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":44557249,"first_name":"Chiara","last_name":"Favaretto","domain_name":"talentgate","page_name":"ChiaraFavaretto","display_name":"Chiara Favaretto","profile_url":"https://talentgate.academia.edu/ChiaraFavaretto?f_ri=12950","photo":"https://0.academia-photos.com/44557249/12009741/13380054/s65_chiara.favaretto.jpg"}],"research_interests":[{"id":12950,"name":"Neural Oscillations","url":"https://www.academia.edu/Documents/in/Neural_Oscillations?f_ri=12950","nofollow":true},{"id":112292,"name":"Brain Networks","url":"https://www.academia.edu/Documents/in/Brain_Networks?f_ri=12950","nofollow":true},{"id":398929,"name":"Kuramoto Model","url":"https://www.academia.edu/Documents/in/Kuramoto_Model?f_ri=12950","nofollow":true}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_13612374" data-work_id="13612374" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/13612374/Brain_oscillatory_correlates_of_working_memory_constraints">Brain oscillatory correlates of working memory constraints</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest">It has been claimed that the coordination of neuronal oscillations differing in frequency is relevant for cognition. However, the validity of this claim has scarcely been investigated.</div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/13612374" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="d23309de15750c8167fe3818f6684437" rel="nofollow" data-download="{"attachment_id":45155232,"asset_id":13612374,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/45155232/download_file?st=MTczNzI5NjIxNCw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="32783839" href="https://sbg.academia.edu/RomanFreunberger">Roman Freunberger</a><script data-card-contents-for-user="32783839" type="text/json">{"id":32783839,"first_name":"Roman","last_name":"Freunberger","domain_name":"sbg","page_name":"RomanFreunberger","display_name":"Roman Freunberger","profile_url":"https://sbg.academia.edu/RomanFreunberger?f_ri=12950","photo":"/images/s65_no_pic.png"}</script></span></span></li><li class="js-paper-rank-work_13612374 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="13612374"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 13612374, container: ".js-paper-rank-work_13612374", }); 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$(".js-view-count[data-work-id=13612374]").text(description); $(".js-view-count-work_13612374").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_13612374").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="13612374"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">25</a>  </div><span class="InlineList-item-text u-textTruncate u-pl10x"><a class="InlineList-item-text" data-has-card-for-ri="161" rel="nofollow" href="https://www.academia.edu/Documents/in/Neuroscience">Neuroscience</a>, <script data-card-contents-for-ri="161" type="text/json">{"id":161,"name":"Neuroscience","url":"https://www.academia.edu/Documents/in/Neuroscience?f_ri=12950","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="237" rel="nofollow" href="https://www.academia.edu/Documents/in/Cognitive_Science">Cognitive Science</a>, <script data-card-contents-for-ri="237" type="text/json">{"id":237,"name":"Cognitive Science","url":"https://www.academia.edu/Documents/in/Cognitive_Science?f_ri=12950","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="863" rel="nofollow" href="https://www.academia.edu/Documents/in/Visual_Studies">Visual Studies</a>, <script data-card-contents-for-ri="863" type="text/json">{"id":863,"name":"Visual Studies","url":"https://www.academia.edu/Documents/in/Visual_Studies?f_ri=12950","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="1680" rel="nofollow" href="https://www.academia.edu/Documents/in/Selective_Attention">Selective Attention</a><script data-card-contents-for-ri="1680" type="text/json">{"id":1680,"name":"Selective Attention","url":"https://www.academia.edu/Documents/in/Selective_Attention?f_ri=12950","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=13612374]'), work: {"id":13612374,"title":"Brain oscillatory correlates of working memory constraints","created_at":"2015-07-04T03:48:36.609-07:00","url":"https://www.academia.edu/13612374/Brain_oscillatory_correlates_of_working_memory_constraints?f_ri=12950","dom_id":"work_13612374","summary":"It has been claimed that the coordination of neuronal oscillations differing in frequency is relevant for cognition. However, the validity of this claim has scarcely been investigated.","downloadable_attachments":[{"id":45155232,"asset_id":13612374,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":32783839,"first_name":"Roman","last_name":"Freunberger","domain_name":"sbg","page_name":"RomanFreunberger","display_name":"Roman Freunberger","profile_url":"https://sbg.academia.edu/RomanFreunberger?f_ri=12950","photo":"/images/s65_no_pic.png"}],"research_interests":[{"id":161,"name":"Neuroscience","url":"https://www.academia.edu/Documents/in/Neuroscience?f_ri=12950","nofollow":true},{"id":237,"name":"Cognitive Science","url":"https://www.academia.edu/Documents/in/Cognitive_Science?f_ri=12950","nofollow":true},{"id":863,"name":"Visual Studies","url":"https://www.academia.edu/Documents/in/Visual_Studies?f_ri=12950","nofollow":true},{"id":1680,"name":"Selective Attention","url":"https://www.academia.edu/Documents/in/Selective_Attention?f_ri=12950","nofollow":true},{"id":2639,"name":"Neuroimaging","url":"https://www.academia.edu/Documents/in/Neuroimaging?f_ri=12950"},{"id":4008,"name":"Visual attention","url":"https://www.academia.edu/Documents/in/Visual_attention?f_ri=12950"},{"id":7736,"name":"Attention","url":"https://www.academia.edu/Documents/in/Attention?f_ri=12950"},{"id":8538,"name":"Working Memory","url":"https://www.academia.edu/Documents/in/Working_Memory?f_ri=12950"},{"id":10904,"name":"Electroencephalography","url":"https://www.academia.edu/Documents/in/Electroencephalography?f_ri=12950"},{"id":12950,"name":"Neural Oscillations","url":"https://www.academia.edu/Documents/in/Neural_Oscillations?f_ri=12950"},{"id":20855,"name":"Visual Working Memory","url":"https://www.academia.edu/Documents/in/Visual_Working_Memory?f_ri=12950"},{"id":21548,"name":"Cognitive Neuroscience","url":"https://www.academia.edu/Documents/in/Cognitive_Neuroscience?f_ri=12950"},{"id":42362,"name":"Alpha Oscillations","url":"https://www.academia.edu/Documents/in/Alpha_Oscillations?f_ri=12950"},{"id":52176,"name":"Brain Mapping","url":"https://www.academia.edu/Documents/in/Brain_Mapping?f_ri=12950"},{"id":61474,"name":"Brain","url":"https://www.academia.edu/Documents/in/Brain?f_ri=12950"},{"id":81761,"name":"Gamma Band Oscillations","url":"https://www.academia.edu/Documents/in/Gamma_Band_Oscillations?f_ri=12950"},{"id":119665,"name":"Reaction Time","url":"https://www.academia.edu/Documents/in/Reaction_Time?f_ri=12950"},{"id":142889,"name":"Mental processes","url":"https://www.academia.edu/Documents/in/Mental_processes?f_ri=12950"},{"id":174781,"name":"Oscillations","url":"https://www.academia.edu/Documents/in/Oscillations?f_ri=12950"},{"id":449068,"name":"Working Memory Capacity","url":"https://www.academia.edu/Documents/in/Working_Memory_Capacity?f_ri=12950"},{"id":522464,"name":"Short Term Memory","url":"https://www.academia.edu/Documents/in/Short_Term_Memory?f_ri=12950"},{"id":1239755,"name":"Neurosciences","url":"https://www.academia.edu/Documents/in/Neurosciences?f_ri=12950"},{"id":1275961,"name":"Alpha Rhythm","url":"https://www.academia.edu/Documents/in/Alpha_Rhythm?f_ri=12950"},{"id":1394091,"name":"Theta Rhythm","url":"https://www.academia.edu/Documents/in/Theta_Rhythm?f_ri=12950"},{"id":1394092,"name":"Beta Rhythm","url":"https://www.academia.edu/Documents/in/Beta_Rhythm?f_ri=12950"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_24517293" data-work_id="24517293" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/24517293/Rhythmic_influence_of_top_down_perceptual_priors_in_the_phase_of_pre_stimulus_occipital_alpha_oscillations">Rhythmic influence of top-down perceptual priors in the phase of pre-stimulus occipital alpha oscillations</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">Prior expectations have a powerful influence on perception, biasing both decision and confidence. However, how this occurs at the neural level remains unclear. It has been suggested that spontaneous alpha-band neural oscillations... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_24517293" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">Prior expectations have a powerful influence on perception, biasing both decision and confidence. However, how this occurs at the neural level remains unclear. It has been suggested that spontaneous alpha-band neural oscillations represent rhythms of the perceptual system that periodically modulate perceptual judgements. We hypothesised that these oscillations instantiate the effects of expectations. While collecting scalp EEG, participants performed a detection task that orthogonally manipulated perceptual expectations and attention. Trial-by-trial retrospective confidence judgements were also collected. Results showed that independently of attention, pre-stimulus occipital alpha phase predicted the weighting of expectations on yes/no decisions. Moreover, phase predicted the influence of expectations on confidence. Thus, expectations periodically bias objective and subjective perceptual decision-making together, prior to stimulus onset. Our results suggest that alpha-band neural oscillations periodically transmit prior evidence to visual cortex, changing the baseline from which evidence accumulation begins. In turn, our results inform accounts of how expectations shape early visual processing.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/24517293" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="87ad0abfe08b273fc51fe753939711ef" rel="nofollow" data-download="{"attachment_id":44848862,"asset_id":24517293,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/44848862/download_file?st=MTczNzI5NjIxNCw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="3028088" href="https://sussex.academia.edu/MaxineSherman">Maxine Sherman</a><script data-card-contents-for-user="3028088" type="text/json">{"id":3028088,"first_name":"Maxine","last_name":"Sherman","domain_name":"sussex","page_name":"MaxineSherman","display_name":"Maxine Sherman","profile_url":"https://sussex.academia.edu/MaxineSherman?f_ri=12950","photo":"https://0.academia-photos.com/3028088/995828/1245781/s65_maxine.sherman.jpg"}</script></span></span></li><li class="js-paper-rank-work_24517293 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="24517293"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 24517293, container: ".js-paper-rank-work_24517293", }); 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$(".js-view-count[data-work-id=24517293]").text(description); $(".js-view-count-work_24517293").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_24517293").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="24517293"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">9</a>  </div><span class="InlineList-item-text u-textTruncate u-pl9x"><a class="InlineList-item-text" data-has-card-for-ri="867" rel="nofollow" href="https://www.academia.edu/Documents/in/Perception">Perception</a>, <script data-card-contents-for-ri="867" type="text/json">{"id":867,"name":"Perception","url":"https://www.academia.edu/Documents/in/Perception?f_ri=12950","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="5359" rel="nofollow" href="https://www.academia.edu/Documents/in/Visual_perception">Visual perception</a>, <script data-card-contents-for-ri="5359" type="text/json">{"id":5359,"name":"Visual perception","url":"https://www.academia.edu/Documents/in/Visual_perception?f_ri=12950","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="9040" rel="nofollow" href="https://www.academia.edu/Documents/in/Consciousness">Consciousness</a>, <script data-card-contents-for-ri="9040" type="text/json">{"id":9040,"name":"Consciousness","url":"https://www.academia.edu/Documents/in/Consciousness?f_ri=12950","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="10402" rel="nofollow" href="https://www.academia.edu/Documents/in/EEG">EEG</a><script data-card-contents-for-ri="10402" type="text/json">{"id":10402,"name":"EEG","url":"https://www.academia.edu/Documents/in/EEG?f_ri=12950","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=24517293]'), work: {"id":24517293,"title":"Rhythmic influence of top-down perceptual priors in the phase of pre-stimulus occipital alpha oscillations","created_at":"2016-04-18T03:32:40.863-07:00","url":"https://www.academia.edu/24517293/Rhythmic_influence_of_top_down_perceptual_priors_in_the_phase_of_pre_stimulus_occipital_alpha_oscillations?f_ri=12950","dom_id":"work_24517293","summary":"Prior expectations have a powerful influence on perception, biasing both decision and confidence. However, how this occurs at the neural level remains unclear. It has been suggested that spontaneous alpha-band neural oscillations represent rhythms of the perceptual system that periodically modulate perceptual judgements. We hypothesised that these oscillations instantiate the effects of expectations. While collecting scalp EEG, participants performed a detection task that orthogonally manipulated perceptual expectations and attention. Trial-by-trial retrospective confidence judgements were also collected. Results showed that independently of attention, pre-stimulus occipital alpha phase predicted the weighting of expectations on yes/no decisions. Moreover, phase predicted the influence of expectations on confidence. Thus, expectations periodically bias objective and subjective perceptual decision-making together, prior to stimulus onset. Our results suggest that alpha-band neural oscillations periodically transmit prior evidence to visual cortex, changing the baseline from which evidence accumulation begins. In turn, our results inform accounts of how expectations shape early visual processing.","downloadable_attachments":[{"id":44848862,"asset_id":24517293,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":3028088,"first_name":"Maxine","last_name":"Sherman","domain_name":"sussex","page_name":"MaxineSherman","display_name":"Maxine Sherman","profile_url":"https://sussex.academia.edu/MaxineSherman?f_ri=12950","photo":"https://0.academia-photos.com/3028088/995828/1245781/s65_maxine.sherman.jpg"}],"research_interests":[{"id":867,"name":"Perception","url":"https://www.academia.edu/Documents/in/Perception?f_ri=12950","nofollow":true},{"id":5359,"name":"Visual perception","url":"https://www.academia.edu/Documents/in/Visual_perception?f_ri=12950","nofollow":true},{"id":9040,"name":"Consciousness","url":"https://www.academia.edu/Documents/in/Consciousness?f_ri=12950","nofollow":true},{"id":10402,"name":"EEG","url":"https://www.academia.edu/Documents/in/EEG?f_ri=12950","nofollow":true},{"id":10868,"name":"Confidence","url":"https://www.academia.edu/Documents/in/Confidence?f_ri=12950"},{"id":12950,"name":"Neural Oscillations","url":"https://www.academia.edu/Documents/in/Neural_Oscillations?f_ri=12950"},{"id":16097,"name":"Decision Making Under Uncertainty","url":"https://www.academia.edu/Documents/in/Decision_Making_Under_Uncertainty?f_ri=12950"},{"id":42362,"name":"Alpha Oscillations","url":"https://www.academia.edu/Documents/in/Alpha_Oscillations?f_ri=12950"},{"id":51529,"name":"Bayesian Inference","url":"https://www.academia.edu/Documents/in/Bayesian_Inference?f_ri=12950"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_16036843 coauthored" data-work_id="16036843" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/16036843/Analytical_insights_on_theta_gamma_coupled_neural_oscillators">Analytical insights on theta-gamma coupled neural oscillators</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">In this paper, we study the dynamics of a quadratic integrate-and-fire neuron, spiking in the gamma (30-100 Hz) range, coupled to a delta/theta frequency (1-8 Hz) neural oscillator. Using analytical and semianalytical methods, we were... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_16036843" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">In this paper, we study the dynamics of a quadratic integrate-and-fire neuron, spiking in the gamma (30-100 Hz) range, coupled to a delta/theta frequency (1-8 Hz) neural oscillator. Using analytical and semianalytical methods, we were able to derive characteristic spiking times for the system in two distinct regimes (depending on parameter values): one regime where the gamma neuron is intrinsically oscillating in the absence of theta input, and a second one in which gamma spiking is directly gated by theta input, i.e., windows of gamma activity alternate with silence periods depending on the underlying theta phase. In the former case, we transform the equations such that the system becomes analogous to the Mathieu differential equation. By solving this equation, we can compute numerically the time to the first gamma spike, and then use singular perturbation theory to find successive spike times. On the other hand, in the excitable condition, we make direct use of singular perturbation theory to obtain an approximation of the time to first gamma spike, and then extend the result to calculate ensuing gamma spikes in a recursive fashion. We thereby give explicit L. Fontolan ( ) formulas for the onset and offset of gamma spike burst during a theta cycle, and provide an estimation of the total number of spikes per theta cycle both for excitable and oscillator regimes.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/16036843" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="cede28618a849e630ca924530521ca1f" rel="nofollow" data-download="{"attachment_id":38855472,"asset_id":16036843,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/38855472/download_file?st=MTczNzI5NjIxNCw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="5307982" href="https://upf.academia.edu/AlexandreHyafil">Alexandre Hyafil</a><script data-card-contents-for-user="5307982" type="text/json">{"id":5307982,"first_name":"Alexandre","last_name":"Hyafil","domain_name":"upf","page_name":"AlexandreHyafil","display_name":"Alexandre Hyafil","profile_url":"https://upf.academia.edu/AlexandreHyafil?f_ri=12950","photo":"https://0.academia-photos.com/5307982/2332972/2720945/s65_alexandre.hyafil.jpg"}</script></span></span><span class="u-displayInlineBlock InlineList-item-text"> and <span class="u-textDecorationUnderline u-clickable InlineList-item-text js-work-more-authors-16036843">+1</span><div class="hidden js-additional-users-16036843"><div><span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a href="https://independent.academia.edu/MaciejKrupa">Maciej Krupa</a></span></div></div></span><script>(function(){ var popoverSettings = { el: $('.js-work-more-authors-16036843'), placement: 'bottom', hide_delay: 200, html: true, content: function(){ return $('.js-additional-users-16036843').html(); 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Using analytical and semianalytical methods, we were able to derive characteristic spiking times for the system in two distinct regimes (depending on parameter values): one regime where the gamma neuron is intrinsically oscillating in the absence of theta input, and a second one in which gamma spiking is directly gated by theta input, i.e., windows of gamma activity alternate with silence periods depending on the underlying theta phase. In the former case, we transform the equations such that the system becomes analogous to the Mathieu differential equation. By solving this equation, we can compute numerically the time to the first gamma spike, and then use singular perturbation theory to find successive spike times. On the other hand, in the excitable condition, we make direct use of singular perturbation theory to obtain an approximation of the time to first gamma spike, and then extend the result to calculate ensuing gamma spikes in a recursive fashion. We thereby give explicit L. Fontolan ( ) formulas for the onset and offset of gamma spike burst during a theta cycle, and provide an estimation of the total number of spikes per theta cycle both for excitable and oscillator regimes.","downloadable_attachments":[{"id":38855472,"asset_id":16036843,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":5307982,"first_name":"Alexandre","last_name":"Hyafil","domain_name":"upf","page_name":"AlexandreHyafil","display_name":"Alexandre Hyafil","profile_url":"https://upf.academia.edu/AlexandreHyafil?f_ri=12950","photo":"https://0.academia-photos.com/5307982/2332972/2720945/s65_alexandre.hyafil.jpg"},{"id":35591503,"first_name":"Maciej","last_name":"Krupa","domain_name":"independent","page_name":"MaciejKrupa","display_name":"Maciej Krupa","profile_url":"https://independent.academia.edu/MaciejKrupa?f_ri=12950","photo":"/images/s65_no_pic.png"}],"research_interests":[{"id":1080,"name":"Dynamical Systems","url":"https://www.academia.edu/Documents/in/Dynamical_Systems?f_ri=12950","nofollow":true},{"id":5451,"name":"Computational Neuroscience","url":"https://www.academia.edu/Documents/in/Computational_Neuroscience?f_ri=12950","nofollow":true},{"id":12950,"name":"Neural Oscillations","url":"https://www.academia.edu/Documents/in/Neural_Oscillations?f_ri=12950","nofollow":true},{"id":49731,"name":"Mathematical Neuroscience","url":"https://www.academia.edu/Documents/in/Mathematical_Neuroscience?f_ri=12950","nofollow":true}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_36678628" data-work_id="36678628" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" rel="nofollow" href="https://www.academia.edu/36678628/AN_EFFICIENT_FEATURE_EXTRACTION_AND_CLASSIFICATION_OF_HANDWRITTEN_DIGITS_USING_NEURAL_NETWORKS">AN EFFICIENT FEATURE EXTRACTION AND CLASSIFICATION OF HANDWRITTEN DIGITS USING NEURAL NETWORKS</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">The wide range of shape variations for handwritten digits requires an adequate representation of thediscriminating features for classification. For the recognition of characters or numerals requires pixel valuesof a normalized raster... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_36678628" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">The wide range of shape variations for handwritten digits requires an adequate representation of thediscriminating features for classification. For the recognition of characters or numerals requires pixel valuesof a normalized raster image and proper features to reach very good classification rate. This paper primarily concerns the problem of isolated handwritten numeral recognition of English scripts.Multilayer Perceptron(MLP) classifier is used for classification. The principalcontributions presented here are preprocessing, feature extraction and multilayer perceptron (MLP) classifiers.The strength of our approach is efficient feature extraction and the comprehensive classification scheme due to which, we have been able to achieve a recognition rate of 95.6, better than the previous approaches.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/36678628" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="68e16cb38e5fac285c1f2781805b3b26" rel="nofollow" data-download="{"attachment_id":56615785,"asset_id":36678628,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/56615785/download_file?st=MTczNzI5NjIxNCw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="21168859" rel="nofollow" href="https://independent.academia.edu/IjcseaJournal">International Journal of Computer Science, Engineering and Applications (IJCSEA)</a><script data-card-contents-for-user="21168859" type="text/json">{"id":21168859,"first_name":"International Journal of Computer Science, Engineering and Applications","last_name":"(IJCSEA)","domain_name":"independent","page_name":"IjcseaJournal","display_name":"International Journal of Computer Science, Engineering and Applications (IJCSEA)","profile_url":"https://independent.academia.edu/IjcseaJournal?f_ri=12950","photo":"https://0.academia-photos.com/21168859/6040200/101887818/s65_international_journal_of_computer_science_engineering_and_applications._ijcsea_.jpg"}</script></span></span></li><li class="js-paper-rank-work_36678628 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="36678628"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 36678628, container: ".js-paper-rank-work_36678628", }); 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$(".js-view-count[data-work-id=36678628]").text(description); $(".js-view-count-work_36678628").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_36678628").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="36678628"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">20</a>  </div><span class="InlineList-item-text u-textTruncate u-pl10x"><a class="InlineList-item-text" data-has-card-for-ri="2446" rel="nofollow" href="https://www.academia.edu/Documents/in/Estimation_and_Filtering_Theory">Estimation and Filtering Theory</a>, <script data-card-contents-for-ri="2446" type="text/json">{"id":2446,"name":"Estimation and Filtering Theory","url":"https://www.academia.edu/Documents/in/Estimation_and_Filtering_Theory?f_ri=12950","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="9174" rel="nofollow" href="https://www.academia.edu/Documents/in/Image_Features_Extraction">Image Features Extraction</a>, <script data-card-contents-for-ri="9174" type="text/json">{"id":9174,"name":"Image Features Extraction","url":"https://www.academia.edu/Documents/in/Image_Features_Extraction?f_ri=12950","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="11315" rel="nofollow" href="https://www.academia.edu/Documents/in/Neural_Control_of_Movement">Neural Control of Movement</a>, <script data-card-contents-for-ri="11315" type="text/json">{"id":11315,"name":"Neural Control of Movement","url":"https://www.academia.edu/Documents/in/Neural_Control_of_Movement?f_ri=12950","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="11598" rel="nofollow" href="https://www.academia.edu/Documents/in/Neural_Networks">Neural Networks</a><script data-card-contents-for-ri="11598" type="text/json">{"id":11598,"name":"Neural Networks","url":"https://www.academia.edu/Documents/in/Neural_Networks?f_ri=12950","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=36678628]'), work: {"id":36678628,"title":"AN EFFICIENT FEATURE EXTRACTION AND CLASSIFICATION OF HANDWRITTEN DIGITS USING NEURAL NETWORKS","created_at":"2018-05-21T00:23:35.001-07:00","url":"https://www.academia.edu/36678628/AN_EFFICIENT_FEATURE_EXTRACTION_AND_CLASSIFICATION_OF_HANDWRITTEN_DIGITS_USING_NEURAL_NETWORKS?f_ri=12950","dom_id":"work_36678628","summary":"The wide range of shape variations for handwritten digits requires an adequate representation of thediscriminating features for classification. For the recognition of characters or numerals requires pixel valuesof a normalized raster image and proper features to reach very good classification rate. This paper primarily concerns the problem of isolated handwritten numeral recognition of English scripts.Multilayer Perceptron(MLP) classifier is used for classification. The principalcontributions presented here are preprocessing, feature extraction and multilayer perceptron (MLP) classifiers.The strength of our approach is efficient feature extraction and the comprehensive classification scheme due to which, we have been able to achieve a recognition rate of 95.6, better than the previous approaches.","downloadable_attachments":[{"id":56615785,"asset_id":36678628,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":21168859,"first_name":"International Journal of Computer Science, Engineering and Applications","last_name":"(IJCSEA)","domain_name":"independent","page_name":"IjcseaJournal","display_name":"International Journal of Computer Science, Engineering and Applications (IJCSEA)","profile_url":"https://independent.academia.edu/IjcseaJournal?f_ri=12950","photo":"https://0.academia-photos.com/21168859/6040200/101887818/s65_international_journal_of_computer_science_engineering_and_applications._ijcsea_.jpg"}],"research_interests":[{"id":2446,"name":"Estimation and Filtering Theory","url":"https://www.academia.edu/Documents/in/Estimation_and_Filtering_Theory?f_ri=12950","nofollow":true},{"id":9174,"name":"Image Features Extraction","url":"https://www.academia.edu/Documents/in/Image_Features_Extraction?f_ri=12950","nofollow":true},{"id":11315,"name":"Neural Control of Movement","url":"https://www.academia.edu/Documents/in/Neural_Control_of_Movement?f_ri=12950","nofollow":true},{"id":11598,"name":"Neural Networks","url":"https://www.academia.edu/Documents/in/Neural_Networks?f_ri=12950","nofollow":true},{"id":12950,"name":"Neural Oscillations","url":"https://www.academia.edu/Documents/in/Neural_Oscillations?f_ri=12950"},{"id":21066,"name":"Artficial Neural Networks","url":"https://www.academia.edu/Documents/in/Artficial_Neural_Networks?f_ri=12950"},{"id":26066,"name":"Neural Network","url":"https://www.academia.edu/Documents/in/Neural_Network?f_ri=12950"},{"id":33251,"name":"Adaptive Filtering","url":"https://www.academia.edu/Documents/in/Adaptive_Filtering?f_ri=12950"},{"id":44389,"name":"Artificial Neural Networks for modeling purposes","url":"https://www.academia.edu/Documents/in/Artificial_Neural_Networks_for_modeling_purposes?f_ri=12950"},{"id":54123,"name":"Artificial Neural Networks","url":"https://www.academia.edu/Documents/in/Artificial_Neural_Networks?f_ri=12950"},{"id":61052,"name":"Feature extraction, PCA, Datamining","url":"https://www.academia.edu/Documents/in/Feature_extraction_PCA_Datamining?f_ri=12950"},{"id":77193,"name":"Collaborative Filtering","url":"https://www.academia.edu/Documents/in/Collaborative_Filtering?f_ri=12950"},{"id":95532,"name":"DIGITAL IMAGE PROCESSING: BINARIZATION","url":"https://www.academia.edu/Documents/in/DIGITAL_IMAGE_PROCESSING_BINARIZATION?f_ri=12950"},{"id":96525,"name":"Thinning","url":"https://www.academia.edu/Documents/in/Thinning?f_ri=12950"},{"id":160144,"name":"Feature Extraction","url":"https://www.academia.edu/Documents/in/Feature_Extraction?f_ri=12950"},{"id":342230,"name":"Artifical Neural Networks","url":"https://www.academia.edu/Documents/in/Artifical_Neural_Networks?f_ri=12950"},{"id":460181,"name":"Signal and Image Processing, Pattern Recognition, Machine learning, Feature Extraction and Classification of Biomedical signals, Brain Machine Interface (BMI), and Computational Neuroscience","url":"https://www.academia.edu/Documents/in/Signal_and_Image_Processing_Pattern_Recognition_Machine_learning_Feature_Extraction_and_Classific?f_ri=12950"},{"id":534046,"name":"Binarization Algorithms","url":"https://www.academia.edu/Documents/in/Binarization_Algorithms?f_ri=12950"},{"id":1211304,"name":"Artificial Neural Network","url":"https://www.academia.edu/Documents/in/Artificial_Neural_Network?f_ri=12950"},{"id":1770370,"name":"Binarization","url":"https://www.academia.edu/Documents/in/Binarization?f_ri=12950"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_31330094 coauthored" data-work_id="31330094" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/31330094/Oscillatory_Brain_Dynamics_during_Sentence_Reading_A_Fixation_Related_Spectral_Perturbation_Analysis">Oscillatory Brain Dynamics during Sentence Reading: A Fixation-Related Spectral Perturbation Analysis</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">The present study investigated oscillatory brain dynamics during self-paced sentence-level processing. Participants read fully correct sentences, sentences containing a semantic violation and " sentences " in which the order of the words... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_31330094" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">The present study investigated oscillatory brain dynamics during self-paced sentence-level processing. Participants read fully correct sentences, sentences containing a semantic violation and " sentences " in which the order of the words was randomized. At the target word level, fixations on semantically unrelated words elicited a lower-beta band (13–18 Hz) desynchronization. At the sentence level, gamma power (31–55 Hz) increased linearly for syntactically correct sentences, but not when the order of the words was randomized. In the 300–900 ms time window after sentence onsets, theta power (4–7 Hz) was greater for syntactically correct sentences as compared to sentences where no syntactic structure was preserved (random words condition). We interpret our results as conforming with a recently formulated predictive-coding framework for oscillatory neural dynamics during sentence-level language comprehension. Additionally, we discuss how our results relate to previous findings with serial visual presentation vs. self-paced reading.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/31330094" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="156f48408c63768fbd6efeff8f0be16b" rel="nofollow" data-download="{"attachment_id":51717429,"asset_id":31330094,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/51717429/download_file?st=MTczNzI5NjIxNCw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="59871526" href="https://sbg.academia.edu/LorenzoVignali">Lorenzo Vignali</a><script data-card-contents-for-user="59871526" type="text/json">{"id":59871526,"first_name":"Lorenzo","last_name":"Vignali","domain_name":"sbg","page_name":"LorenzoVignali","display_name":"Lorenzo Vignali","profile_url":"https://sbg.academia.edu/LorenzoVignali?f_ri=12950","photo":"https://0.academia-photos.com/59871526/15615612/16191749/s65_lorenzo.vignali.jpg"}</script></span></span><span class="u-displayInlineBlock InlineList-item-text"> and <span class="u-textDecorationUnderline u-clickable InlineList-item-text js-work-more-authors-31330094">+1</span><div class="hidden js-additional-users-31330094"><div><span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a href="https://sbg.academia.edu/FabioRichlan">Fabio Richlan</a></span></div></div></span><script>(function(){ var popoverSettings = { el: $('.js-work-more-authors-31330094'), placement: 'bottom', hide_delay: 200, html: true, content: function(){ return $('.js-additional-users-31330094').html(); } } new HoverPopover(popoverSettings); })();</script></li><li class="js-paper-rank-work_31330094 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="31330094"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 31330094, container: ".js-paper-rank-work_31330094", }); });</script></li><li class="js-percentile-work_31330094 InlineList-item InlineList-item--bordered hidden u-tcGrayDark"><span class="percentile-widget hidden"><span class="u-mr2x percentile-widget" style="display: none">•</span><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 31330094; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-percentile-work_31330094"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></li><li class="js-view-count-work_31330094 InlineList-item InlineList-item--bordered hidden"><div><span><span class="js-view-count view-count u-mr2x" data-work-id="31330094"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 31330094; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=31330094]").text(description); $(".js-view-count-work_31330094").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_31330094").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="31330094"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">8</a>  </div><span class="InlineList-item-text u-textTruncate u-pl9x"><a class="InlineList-item-text" data-has-card-for-ri="1200" rel="nofollow" href="https://www.academia.edu/Documents/in/Languages_and_Linguistics">Languages and Linguistics</a>, <script data-card-contents-for-ri="1200" type="text/json">{"id":1200,"name":"Languages and Linguistics","url":"https://www.academia.edu/Documents/in/Languages_and_Linguistics?f_ri=12950","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="9471" rel="nofollow" href="https://www.academia.edu/Documents/in/Reading">Reading</a>, <script data-card-contents-for-ri="9471" type="text/json">{"id":9471,"name":"Reading","url":"https://www.academia.edu/Documents/in/Reading?f_ri=12950","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="10402" rel="nofollow" href="https://www.academia.edu/Documents/in/EEG">EEG</a>, <script data-card-contents-for-ri="10402" type="text/json">{"id":10402,"name":"EEG","url":"https://www.academia.edu/Documents/in/EEG?f_ri=12950","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="12950" rel="nofollow" href="https://www.academia.edu/Documents/in/Neural_Oscillations">Neural Oscillations</a><script data-card-contents-for-ri="12950" type="text/json">{"id":12950,"name":"Neural Oscillations","url":"https://www.academia.edu/Documents/in/Neural_Oscillations?f_ri=12950","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=31330094]'), work: {"id":31330094,"title":"Oscillatory Brain Dynamics during Sentence Reading: A Fixation-Related Spectral Perturbation Analysis","created_at":"2017-02-09T07:10:33.666-08:00","url":"https://www.academia.edu/31330094/Oscillatory_Brain_Dynamics_during_Sentence_Reading_A_Fixation_Related_Spectral_Perturbation_Analysis?f_ri=12950","dom_id":"work_31330094","summary":"The present study investigated oscillatory brain dynamics during self-paced sentence-level processing. Participants read fully correct sentences, sentences containing a semantic violation and \" sentences \" in which the order of the words was randomized. At the target word level, fixations on semantically unrelated words elicited a lower-beta band (13–18 Hz) desynchronization. At the sentence level, gamma power (31–55 Hz) increased linearly for syntactically correct sentences, but not when the order of the words was randomized. In the 300–900 ms time window after sentence onsets, theta power (4–7 Hz) was greater for syntactically correct sentences as compared to sentences where no syntactic structure was preserved (random words condition). We interpret our results as conforming with a recently formulated predictive-coding framework for oscillatory neural dynamics during sentence-level language comprehension. Additionally, we discuss how our results relate to previous findings with serial visual presentation vs. self-paced reading.","downloadable_attachments":[{"id":51717429,"asset_id":31330094,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":59871526,"first_name":"Lorenzo","last_name":"Vignali","domain_name":"sbg","page_name":"LorenzoVignali","display_name":"Lorenzo Vignali","profile_url":"https://sbg.academia.edu/LorenzoVignali?f_ri=12950","photo":"https://0.academia-photos.com/59871526/15615612/16191749/s65_lorenzo.vignali.jpg"},{"id":2967349,"first_name":"Fabio","last_name":"Richlan","domain_name":"sbg","page_name":"FabioRichlan","display_name":"Fabio Richlan","profile_url":"https://sbg.academia.edu/FabioRichlan?f_ri=12950","photo":"https://0.academia-photos.com/2967349/1295839/43769353/s65_fabio.richlan.jpg"}],"research_interests":[{"id":1200,"name":"Languages and Linguistics","url":"https://www.academia.edu/Documents/in/Languages_and_Linguistics?f_ri=12950","nofollow":true},{"id":9471,"name":"Reading","url":"https://www.academia.edu/Documents/in/Reading?f_ri=12950","nofollow":true},{"id":10402,"name":"EEG","url":"https://www.academia.edu/Documents/in/EEG?f_ri=12950","nofollow":true},{"id":12950,"name":"Neural Oscillations","url":"https://www.academia.edu/Documents/in/Neural_Oscillations?f_ri=12950","nofollow":true},{"id":14180,"name":"Reading Comprehension","url":"https://www.academia.edu/Documents/in/Reading_Comprehension?f_ri=12950"},{"id":21548,"name":"Cognitive Neuroscience","url":"https://www.academia.edu/Documents/in/Cognitive_Neuroscience?f_ri=12950"},{"id":149891,"name":"Brain oscillations","url":"https://www.academia.edu/Documents/in/Brain_oscillations?f_ri=12950"},{"id":442015,"name":"Fixation-Related Potentials","url":"https://www.academia.edu/Documents/in/Fixation-Related_Potentials?f_ri=12950"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_15222006" data-work_id="15222006" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/15222006/Population_coding_and_neural_rhythmicity_in_the_orbitofrontal_cortex">Population coding and neural rhythmicity in the orbitofrontal cortex</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">The orbitofrontal cortex has been implicated in the prediction of valuable outcomes based on environmental stimuli. However, it remains unknown how it represents outcome-predictive information at the population level, and how it provides... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_15222006" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">The orbitofrontal cortex has been implicated in the prediction of valuable outcomes based on environmental stimuli. However, it remains unknown how it represents outcome-predictive information at the population level, and how it provides temporal structure to such representations. Here, we pay attention especially to the population coding of probabilistic reward, and to the importance of orbitofrontal theta-and gamma-band rhythmicity in relation to target areas. When rats learned to associate odors to food outcome with variable likelihood, we found singlecell and population coding of reward probability, but not uncertainty. In related experiments, reward anticipation correlated to firing activity locking to theta-band oscillations. In contrast, gamma-band activity was associated with a firing-rate suppression of neurons that was most active during goal-directed movement. Orbitofrontal coding of outcome-relevant parameters appears bound to all relevant temporal phases of behavioral tasks, has a distributed nature, and is temporally structured according to multiple modes of rhythmicity.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/15222006" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="978d028fcf02a8cb6a1ea032bd753138" rel="nofollow" data-download="{"attachment_id":43421166,"asset_id":15222006,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/43421166/download_file?st=MTczNzI5NjIxNCw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="34285049" href="https://uva.academia.edu/CyrielPennartz">Cyriel Pennartz</a><script data-card-contents-for-user="34285049" type="text/json">{"id":34285049,"first_name":"Cyriel","last_name":"Pennartz","domain_name":"uva","page_name":"CyrielPennartz","display_name":"Cyriel Pennartz","profile_url":"https://uva.academia.edu/CyrielPennartz?f_ri=12950","photo":"https://0.academia-photos.com/34285049/51033231/39087832/s65_cyriel.pennartz.jpeg"}</script></span></span></li><li class="js-paper-rank-work_15222006 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="15222006"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 15222006, container: ".js-paper-rank-work_15222006", }); });</script></li><li class="js-percentile-work_15222006 InlineList-item InlineList-item--bordered hidden u-tcGrayDark"><span class="percentile-widget hidden"><span class="u-mr2x percentile-widget" style="display: none">•</span><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 15222006; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-percentile-work_15222006"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></li><li class="js-view-count-work_15222006 InlineList-item InlineList-item--bordered hidden"><div><span><span class="js-view-count view-count u-mr2x" data-work-id="15222006"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 15222006; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=15222006]").text(description); $(".js-view-count-work_15222006").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_15222006").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="15222006"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">18</a>  </div><span class="InlineList-item-text u-textTruncate u-pl10x"><a class="InlineList-item-text" data-has-card-for-ri="161" rel="nofollow" href="https://www.academia.edu/Documents/in/Neuroscience">Neuroscience</a>, <script data-card-contents-for-ri="161" type="text/json">{"id":161,"name":"Neuroscience","url":"https://www.academia.edu/Documents/in/Neuroscience?f_ri=12950","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="2007" rel="nofollow" href="https://www.academia.edu/Documents/in/Electrophysiology">Electrophysiology</a>, <script data-card-contents-for-ri="2007" type="text/json">{"id":2007,"name":"Electrophysiology","url":"https://www.academia.edu/Documents/in/Electrophysiology?f_ri=12950","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="2749" rel="nofollow" href="https://www.academia.edu/Documents/in/Animal_Behavior">Animal Behavior</a>, <script data-card-contents-for-ri="2749" type="text/json">{"id":2749,"name":"Animal Behavior","url":"https://www.academia.edu/Documents/in/Animal_Behavior?f_ri=12950","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="4307" rel="nofollow" href="https://www.academia.edu/Documents/in/Behavior">Behavior</a><script data-card-contents-for-ri="4307" type="text/json">{"id":4307,"name":"Behavior","url":"https://www.academia.edu/Documents/in/Behavior?f_ri=12950","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=15222006]'), work: {"id":15222006,"title":"Population coding and neural rhythmicity in the orbitofrontal cortex","created_at":"2015-08-27T04:11:13.224-07:00","url":"https://www.academia.edu/15222006/Population_coding_and_neural_rhythmicity_in_the_orbitofrontal_cortex?f_ri=12950","dom_id":"work_15222006","summary":"The orbitofrontal cortex has been implicated in the prediction of valuable outcomes based on environmental stimuli. However, it remains unknown how it represents outcome-predictive information at the population level, and how it provides temporal structure to such representations. Here, we pay attention especially to the population coding of probabilistic reward, and to the importance of orbitofrontal theta-and gamma-band rhythmicity in relation to target areas. When rats learned to associate odors to food outcome with variable likelihood, we found singlecell and population coding of reward probability, but not uncertainty. In related experiments, reward anticipation correlated to firing activity locking to theta-band oscillations. In contrast, gamma-band activity was associated with a firing-rate suppression of neurons that was most active during goal-directed movement. Orbitofrontal coding of outcome-relevant parameters appears bound to all relevant temporal phases of behavioral tasks, has a distributed nature, and is temporally structured according to multiple modes of rhythmicity.","downloadable_attachments":[{"id":43421166,"asset_id":15222006,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":34285049,"first_name":"Cyriel","last_name":"Pennartz","domain_name":"uva","page_name":"CyrielPennartz","display_name":"Cyriel Pennartz","profile_url":"https://uva.academia.edu/CyrielPennartz?f_ri=12950","photo":"https://0.academia-photos.com/34285049/51033231/39087832/s65_cyriel.pennartz.jpeg"}],"research_interests":[{"id":161,"name":"Neuroscience","url":"https://www.academia.edu/Documents/in/Neuroscience?f_ri=12950","nofollow":true},{"id":2007,"name":"Electrophysiology","url":"https://www.academia.edu/Documents/in/Electrophysiology?f_ri=12950","nofollow":true},{"id":2749,"name":"Animal Behavior","url":"https://www.academia.edu/Documents/in/Animal_Behavior?f_ri=12950","nofollow":true},{"id":4307,"name":"Behavior","url":"https://www.academia.edu/Documents/in/Behavior?f_ri=12950","nofollow":true},{"id":12950,"name":"Neural Oscillations","url":"https://www.academia.edu/Documents/in/Neural_Oscillations?f_ri=12950"},{"id":28235,"name":"Multidisciplinary","url":"https://www.academia.edu/Documents/in/Multidisciplinary?f_ri=12950"},{"id":29204,"name":"Comparative Physiology","url":"https://www.academia.edu/Documents/in/Comparative_Physiology?f_ri=12950"},{"id":49021,"name":"Reward","url":"https://www.academia.edu/Documents/in/Reward?f_ri=12950"},{"id":174781,"name":"Oscillations","url":"https://www.academia.edu/Documents/in/Oscillations?f_ri=12950"},{"id":174800,"name":"Gamma Band","url":"https://www.academia.edu/Documents/in/Gamma_Band?f_ri=12950"},{"id":190905,"name":"Periodicity","url":"https://www.academia.edu/Documents/in/Periodicity?f_ri=12950"},{"id":193974,"name":"Neurons","url":"https://www.academia.edu/Documents/in/Neurons?f_ri=12950"},{"id":201456,"name":"Orbitofrontal cortex","url":"https://www.academia.edu/Documents/in/Orbitofrontal_cortex?f_ri=12950"},{"id":235046,"name":"Single Cell","url":"https://www.academia.edu/Documents/in/Single_Cell?f_ri=12950"},{"id":384671,"name":"Population coding","url":"https://www.academia.edu/Documents/in/Population_coding?f_ri=12950"},{"id":477865,"name":"Operant Conditioning","url":"https://www.academia.edu/Documents/in/Operant_Conditioning?f_ri=12950"},{"id":522475,"name":"Reversal Learning","url":"https://www.academia.edu/Documents/in/Reversal_Learning?f_ri=12950"},{"id":1257483,"name":"Frontal Lobe","url":"https://www.academia.edu/Documents/in/Frontal_Lobe?f_ri=12950"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_15790098" data-work_id="15790098" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/15790098/Prestimulus_theta_activity_predicts_correct_source_memory_retrieval">Prestimulus theta activity predicts correct source memory retrieval</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">Recent evidence indicates that the processing of a stimulus can be influenced by preceding patterns of brain activity. Here we examine whether prestimulus oscillatory brain activity can influence the ability to retrieve episodic memories.... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_15790098" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">Recent evidence indicates that the processing of a stimulus can be influenced by preceding patterns of brain activity. Here we examine whether prestimulus oscillatory brain activity can influence the ability to retrieve episodic memories. Neural activity in the theta-frequency band (4-8 Hz) was enhanced before presentation of test items which elicited accurate recollection of contextual details of the prior study episode ("source retrieval"), relative to trials for which item recognition was successful but source retrieval failed. Poststimulus theta activity was also related to source retrieval, and the magnitude of poststimulus theta was predicted by the magnitude of the prestimulus theta effects. The results suggest that ongoing neural processes occurring before stimulus onset might play a critical role in readying the brain for successful memory retrieval.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/15790098" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="b6a06573c72e2d4698d870f46dc2bffc" rel="nofollow" data-download="{"attachment_id":38796404,"asset_id":15790098,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/38796404/download_file?st=MTczNzI5NjIxNCw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="34982927" href="https://fit.academia.edu/RichardAddante">Richard J Addante</a><script data-card-contents-for-user="34982927" type="text/json">{"id":34982927,"first_name":"Richard","last_name":"Addante","domain_name":"fit","page_name":"RichardAddante","display_name":"Richard J Addante","profile_url":"https://fit.academia.edu/RichardAddante?f_ri=12950","photo":"https://0.academia-photos.com/34982927/10186803/36063235/s65_richard.addante.jpg"}</script></span></span></li><li class="js-paper-rank-work_15790098 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="15790098"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 15790098, container: ".js-paper-rank-work_15790098", }); });</script></li><li class="js-percentile-work_15790098 InlineList-item InlineList-item--bordered hidden u-tcGrayDark"><span class="percentile-widget hidden"><span class="u-mr2x percentile-widget" style="display: none">•</span><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 15790098; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-percentile-work_15790098"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></li><li class="js-view-count-work_15790098 InlineList-item InlineList-item--bordered hidden"><div><span><span class="js-view-count view-count u-mr2x" data-work-id="15790098"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 15790098; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=15790098]").text(description); $(".js-view-count-work_15790098").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_15790098").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="15790098"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">10</a>  </div><span class="InlineList-item-text u-textTruncate u-pl10x"><a class="InlineList-item-text" data-has-card-for-ri="11452" rel="nofollow" href="https://www.academia.edu/Documents/in/Memory_Studies">Memory Studies</a>, <script data-card-contents-for-ri="11452" type="text/json">{"id":11452,"name":"Memory Studies","url":"https://www.academia.edu/Documents/in/Memory_Studies?f_ri=12950","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="12950" rel="nofollow" href="https://www.academia.edu/Documents/in/Neural_Oscillations">Neural Oscillations</a>, <script data-card-contents-for-ri="12950" type="text/json">{"id":12950,"name":"Neural Oscillations","url":"https://www.academia.edu/Documents/in/Neural_Oscillations?f_ri=12950","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="28576" rel="nofollow" href="https://www.academia.edu/Documents/in/Prefrontal_Cortex">Prefrontal Cortex</a>, <script data-card-contents-for-ri="28576" type="text/json">{"id":28576,"name":"Prefrontal Cortex","url":"https://www.academia.edu/Documents/in/Prefrontal_Cortex?f_ri=12950","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="32361" rel="nofollow" href="https://www.academia.edu/Documents/in/Episodic_Memory">Episodic Memory</a><script data-card-contents-for-ri="32361" type="text/json">{"id":32361,"name":"Episodic Memory","url":"https://www.academia.edu/Documents/in/Episodic_Memory?f_ri=12950","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=15790098]'), work: {"id":15790098,"title":"Prestimulus theta activity predicts correct source memory retrieval","created_at":"2015-09-16T23:39:14.767-07:00","url":"https://www.academia.edu/15790098/Prestimulus_theta_activity_predicts_correct_source_memory_retrieval?f_ri=12950","dom_id":"work_15790098","summary":"Recent evidence indicates that the processing of a stimulus can be influenced by preceding patterns of brain activity. Here we examine whether prestimulus oscillatory brain activity can influence the ability to retrieve episodic memories. Neural activity in the theta-frequency band (4-8 Hz) was enhanced before presentation of test items which elicited accurate recollection of contextual details of the prior study episode (\"source retrieval\"), relative to trials for which item recognition was successful but source retrieval failed. Poststimulus theta activity was also related to source retrieval, and the magnitude of poststimulus theta was predicted by the magnitude of the prestimulus theta effects. The results suggest that ongoing neural processes occurring before stimulus onset might play a critical role in readying the brain for successful memory retrieval.","downloadable_attachments":[{"id":38796404,"asset_id":15790098,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":34982927,"first_name":"Richard","last_name":"Addante","domain_name":"fit","page_name":"RichardAddante","display_name":"Richard J Addante","profile_url":"https://fit.academia.edu/RichardAddante?f_ri=12950","photo":"https://0.academia-photos.com/34982927/10186803/36063235/s65_richard.addante.jpg"}],"research_interests":[{"id":11452,"name":"Memory Studies","url":"https://www.academia.edu/Documents/in/Memory_Studies?f_ri=12950","nofollow":true},{"id":12950,"name":"Neural Oscillations","url":"https://www.academia.edu/Documents/in/Neural_Oscillations?f_ri=12950","nofollow":true},{"id":28576,"name":"Prefrontal Cortex","url":"https://www.academia.edu/Documents/in/Prefrontal_Cortex?f_ri=12950","nofollow":true},{"id":32361,"name":"Episodic Memory","url":"https://www.academia.edu/Documents/in/Episodic_Memory?f_ri=12950","nofollow":true},{"id":57556,"name":"Hippocampus","url":"https://www.academia.edu/Documents/in/Hippocampus?f_ri=12950"},{"id":76071,"name":"EEG Signal Processing","url":"https://www.academia.edu/Documents/in/EEG_Signal_Processing?f_ri=12950"},{"id":522465,"name":"Long Term Memory","url":"https://www.academia.edu/Documents/in/Long_Term_Memory?f_ri=12950"},{"id":693316,"name":"EEG Signal Analysis","url":"https://www.academia.edu/Documents/in/EEG_Signal_Analysis?f_ri=12950"},{"id":976865,"name":"Theta","url":"https://www.academia.edu/Documents/in/Theta?f_ri=12950"},{"id":1011639,"name":"Theta criterion","url":"https://www.academia.edu/Documents/in/Theta_criterion?f_ri=12950"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_13961001" data-work_id="13961001" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/13961001/Gomez_Molina_JF_Pineda_DA_Molina_R_L_2009_Models_of_wavelets_for_estimating_MRI_oscillations_from_evoked_and_spontaneous_EEG_MEG_activity_Society_for_Neuroscience_Meeting_Abstract_On_line_sfn_org">Gomez-Molina JF, Pineda DA, Molina-R L (2009) Models of wavelets for estimating MRI-oscillations from evoked and spontaneous EEG/MEG activity. -Society for Neuroscience Meeting Abstract On line sfn.org</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">"Models of wavelets for estimating MRI-oscillations from evoked and spontaneous EEG/MEG activity" -Society for Neuroscience Meeting Abstract. On line sfn.org Juan Fernando Gomez-Molina, David A Pineda, Molina-R Lylliam ABSTRACT:... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_13961001" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">"Models of wavelets for estimating MRI-oscillations from evoked and spontaneous EEG/MEG activity"<br /><br /> -Society for Neuroscience Meeting Abstract. On line sfn.org<br /><br />Juan Fernando Gomez-Molina, David A Pineda, Molina-R Lylliam<br /><br />ABSTRACT:<br />Location: South Hall A - Presentation Time: Tuesday, Oct 20, 2009, 4:00 PM - 5:00 PM - <br /><br />Authors:<br /> *J. F. GOMEZ-MOLINA 1, <br />D. A. PINEDA 2,<br />L. MOLINA-R 3;<br /><br /> - 1 Int Group of Neuro Cra 64c #48-94 Suramericana 3 (603) Medellin Colombia, Medellin, Colombia; <br />- 2 Grupo de Neuropsicologia y Conducta, Fac. of Psychology, Univ. of San Buenaventura, Medellin, Colombia;<br /> - 3 Int Group Neuro Cra 64c # 48 - 94 (603), Medellin, Colombia. <br /><br />      - Introduction. <br /><br />    Wavelets are small waves that decay quickly (their envelope decreases fast). They generate signals with self-similar characteristics like the EEG and the fMRI. On the other hand, it was suggested that a visual stimulus desynchronize -or spatially fragment- alpha rhythms (Gomez, SfN 2008). Slow (<0.1Hz), resting fMRI-BOLD signals might display similar fragmentation (alpha power and BOLD seem to be related). <br /><br />    - Methods.<br /><br />      We convert synthetic EEG/MEG signals into fMRI (see table):  <br />    (i) Using gamma and hemodynamic response functions. <br /><br />    (ii) Using wavelets and Gabor functions. <br />  <br />    The model can be used with real-EEG evoked potentials (i.e. normals vs. children with ADHD). <br /><br />    - Conclusions.<br /><br />      Wavelets can express more explicitly the intrinsic, spontaneous oscillations(*) of the EEG/fMRI and the stimulus-induced changes. They imply the existence of peaks in fMRI responses (resonances) at certain stimulus frequencies. <br /><br />    Wavelets can model evoked signals as waves in sensory pathways which might form BOLD-related functional connections. <br /><br />    Diffusion-fMRI-response (ahead from BOLD onset and offset in visual cortex) can be estimated from it and a fast component -icMRI (**), caused by the effects of the ionic currents on water protons and MRI-times (TE, T1)- can be extracted from slower cell-swelling signals. <br /> <br />    - (*) Llinas R. I of the vortex. MIT Press. <br /><br />    - (**) Gomez-M Juan F (2008) Combination of Visual Stimulation and EEG with fMRI/"Ionic-current-MRI Dyn Neuro XVI, NIMH.<br /><br />    Society for Neuroscience Meeting - Chicago IL, USA -, South Hall A Chicago IL USA; 10/2009  On line sfn.org<br /><br />    OASIS Technical Support. 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On line sfn.org\n\nJuan Fernando Gomez-Molina, David A Pineda, Molina-R Lylliam\n\nABSTRACT:\nLocation: South Hall A - Presentation Time: Tuesday, Oct 20, 2009, 4:00 PM - 5:00 PM - \n\nAuthors:\n *J. F. GOMEZ-MOLINA 1, \nD. A. PINEDA 2,\nL. MOLINA-R 3;\n\n - 1 Int Group of Neuro Cra 64c #48-94 Suramericana 3 (603) Medellin Colombia, Medellin, Colombia; \n- 2 Grupo de Neuropsicologia y Conducta, Fac. of Psychology, Univ. of San Buenaventura, Medellin, Colombia;\n - 3 Int Group Neuro Cra 64c # 48 - 94 (603), Medellin, Colombia. \n\n - Introduction. \n\n Wavelets are small waves that decay quickly (their envelope decreases fast). They generate signals with self-similar characteristics like the EEG and the fMRI. On the other hand, it was suggested that a visual stimulus desynchronize -or spatially fragment- alpha rhythms (Gomez, SfN 2008). Slow (\u003c0.1Hz), resting fMRI-BOLD signals might display similar fragmentation (alpha power and BOLD seem to be related). \n\n - Methods.\n\n We convert synthetic EEG/MEG signals into fMRI (see table): \n (i) Using gamma and hemodynamic response functions. \n\n (ii) Using wavelets and Gabor functions. \n \n The model can be used with real-EEG evoked potentials (i.e. normals vs. children with ADHD). \n\n - Conclusions.\n\n Wavelets can express more explicitly the intrinsic, spontaneous oscillations(*) of the EEG/fMRI and the stimulus-induced changes. They imply the existence of peaks in fMRI responses (resonances) at certain stimulus frequencies. \n\n Wavelets can model evoked signals as waves in sensory pathways which might form BOLD-related functional connections. \n\n Diffusion-fMRI-response (ahead from BOLD onset and offset in visual cortex) can be estimated from it and a fast component -icMRI (**), caused by the effects of the ionic currents on water protons and MRI-times (TE, T1)- can be extracted from slower cell-swelling signals. \n \n - (*) Llinas R. I of the vortex. MIT Press. \n\n - (**) Gomez-M Juan F (2008) Combination of Visual Stimulation and EEG with fMRI/\"Ionic-current-MRI Dyn Neuro XVI, NIMH.\n\n Society for Neuroscience Meeting - Chicago IL, USA -, South Hall A Chicago IL USA; 10/2009 On line sfn.org\n\n OASIS Technical Support. Monday - Friday, 9 am - 5 pm CT\nPhone: 1-217-398-1792","downloadable_attachments":[{"id":38167940,"asset_id":13961001,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":15867359,"first_name":"Juan-Fernando","last_name":"Gomez-Molina","domain_name":"independent","page_name":"JuanFernandoGomezMolina","display_name":"Juan-Fernando Gomez-Molina","profile_url":"https://independent.academia.edu/JuanFernandoGomezMolina?f_ri=12950","photo":"https://0.academia-photos.com/15867359/4287537/6182814/s65_juan_fernando.gomez-molina.jpg"}],"research_interests":[{"id":2639,"name":"Neuroimaging","url":"https://www.academia.edu/Documents/in/Neuroimaging?f_ri=12950","nofollow":true},{"id":5451,"name":"Computational Neuroscience","url":"https://www.academia.edu/Documents/in/Computational_Neuroscience?f_ri=12950","nofollow":true},{"id":9224,"name":"Functional MRI","url":"https://www.academia.edu/Documents/in/Functional_MRI?f_ri=12950","nofollow":true},{"id":10402,"name":"EEG","url":"https://www.academia.edu/Documents/in/EEG?f_ri=12950","nofollow":true},{"id":12950,"name":"Neural Oscillations","url":"https://www.academia.edu/Documents/in/Neural_Oscillations?f_ri=12950"},{"id":43974,"name":"Wavelets","url":"https://www.academia.edu/Documents/in/Wavelets?f_ri=12950"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_11825397 coauthored" data-work_id="11825397" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/11825397/Oscillatory_reinstatement_enhances_declarative_memory">Oscillatory reinstatement enhances declarative memory</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">The interaction between learning (encoding) and remembering (retrieval) events is critical to declarative memory . Several neuro-computational models argue that the successful recall of memory is dependent on the reinstatement of patterns... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_11825397" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">The interaction between learning (encoding) and remembering (retrieval) events is critical to declarative memory . Several neuro-computational models argue that the successful recall of memory is dependent on the reinstatement of patterns of activity that were present when the memory was encoded .</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/11825397" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="7d02efb2f21d582efa5fe21dcfb9445b" rel="nofollow" data-download="{"attachment_id":37230802,"asset_id":11825397,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/37230802/download_file?st=MTczNzI5NjIxNSw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="13572641" href="https://ucl.academia.edu/CalumGlen">Calum Glen</a><script data-card-contents-for-user="13572641" type="text/json">{"id":13572641,"first_name":"Calum","last_name":"Glen","domain_name":"ucl","page_name":"CalumGlen","display_name":"Calum Glen","profile_url":"https://ucl.academia.edu/CalumGlen?f_ri=12950","photo":"https://0.academia-photos.com/13572641/8308750/9310553/s65_calum.glen.jpg"}</script></span></span><span class="u-displayInlineBlock InlineList-item-text"> and <span class="u-textDecorationUnderline u-clickable InlineList-item-text js-work-more-authors-11825397">+1</span><div class="hidden js-additional-users-11825397"><div><span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a href="https://kent.academia.edu/AmirHomayounJavadi">Amir-Homayoun Javadi</a></span></div></div></span><script>(function(){ var popoverSettings = { el: $('.js-work-more-authors-11825397'), placement: 'bottom', hide_delay: 200, html: true, content: function(){ return $('.js-additional-users-11825397').html(); 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Several neuro-computational models argue that the successful recall of memory is dependent on the reinstatement of patterns of activity that were present when the memory was encoded .","downloadable_attachments":[{"id":37230802,"asset_id":11825397,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":13572641,"first_name":"Calum","last_name":"Glen","domain_name":"ucl","page_name":"CalumGlen","display_name":"Calum Glen","profile_url":"https://ucl.academia.edu/CalumGlen?f_ri=12950","photo":"https://0.academia-photos.com/13572641/8308750/9310553/s65_calum.glen.jpg"},{"id":29296336,"first_name":"Amir-Homayoun","last_name":"Javadi","domain_name":"kent","page_name":"AmirHomayounJavadi","display_name":"Amir-Homayoun Javadi","profile_url":"https://kent.academia.edu/AmirHomayounJavadi?f_ri=12950","photo":"https://0.academia-photos.com/29296336/8377065/9366764/s65_amir-homayoun.javadi.jpg"}],"research_interests":[{"id":11452,"name":"Memory 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