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class="user-bio-container"><div class="profile-bio fake-truncate js-profile-about" style="margin: 0px;">Married to Giordana.Convenor of the first and only Ararat Memory meeting.Invited speaker at the first Kim Il Sung University International Conference onthe Development of Science and Human Welfare, Pyongyang.Staunch admirer of the Palestinian Neuroscience Initiative.Educated at Liceo Classico Michelangiolo, Yale College, Univ of Florence, Univ of Rome La Sapienza, Hebrew University of Jerusalem, University of Oxford.In excellent terms with the younger version of myself who, for unfathomable reasons, sports an independent page on Academia.edu<br /><span class="u-fw700">Supervisors: </span>Advised by Guido Martinelli, Daniel Amit, Edmund Rolls., Tried to advise Stefano Panzeri, Francesco Battaglia, Yasser Roudi, Emilio Kropff, Athena Akrami, and In excellent terms with the younger version of myself who, for unfathomable reasons, sports an independent page on Academia.edu<br /><span class="u-fw700">Phone: </span>ask by email<br /><b>Address: </b>via Bonomea 265, 34136 Trieste<br /><div class="js-profile-less-about u-linkUnstyled u-tcGrayDarker u-textDecorationUnderline u-displayNone">less</div></div></div><div class="suggested-academics-container"><div class="suggested-academics--header"><h3 class="ds2-5-heading-sans-serif-xs">Related Authors</h3></div><ul class="suggested-user-card-list" data-nosnippet="true"><div class="suggested-user-card"><div class="suggested-user-card__avatar social-profile-avatar-container"><a data-nosnippet="" href="https://sissa.academia.edu/AlessandroTreves"><img class="profile-avatar u-positionAbsolute" alt="Alessandro Treves related author profile picture" border="0" onerror="if (this.src != '//a.academia-assets.com/images/s200_no_pic.png') this.src = '//a.academia-assets.com/images/s200_no_pic.png';" width="200" height="200" src="https://0.academia-photos.com/2162013/698120/866766/s200_alessandro.treves.jpg" /></a></div><div 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data-dom-id="Pill-react-component-3392c7da-5ee4-443b-951b-d4e92e70071c"></div> <div id="Pill-react-component-3392c7da-5ee4-443b-951b-d4e92e70071c"></div> </a></div></div><div class="external-links-container"><ul class="profile-links new-profile js-UserInfo-social"><li class="profile-profiles js-social-profiles-container"><i class="fa fa-spin fa-spinner"></i></li></ul></div></div></div><div class="right-panel-container"><div class="user-content-wrapper"><div class="uploads-container" id="social-redesign-work-container"><div class="upload-header"><h2 class="ds2-5-heading-sans-serif-xs">Uploads</h2></div><div class="documents-container backbone-social-profile-documents" style="width: 100%;"><div class="u-taCenter"></div><div class="profile--tab_content_container js-tab-pane tab-pane active" id="all"><div class="profile--tab_heading_container js-section-heading" data-section="Papers" id="Papers"><h3 class="profile--tab_heading_container">Papers by Alessandro Treves</h3></div><div class="js-work-strip profile--work_container" data-work-id="104822617"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/104822617/Latching_dynamics_as_a_basis_for_short_term_recall"><img alt="Research paper thumbnail of Latching dynamics as a basis for short-term recall" class="work-thumbnail" src="https://attachments.academia-assets.com/104448905/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/104822617/Latching_dynamics_as_a_basis_for_short_term_recall">Latching dynamics as a basis for short-term recall</a></div><div class="wp-workCard_item"><span>PLOS Computational Biology</span><span>, 2021</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">We discuss simple models for the transient storage in short-term memory of cortical patterns of a...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">We discuss simple models for the transient storage in short-term memory of cortical patterns of activity, all based on the notion that their recall exploits the natural tendency of the cortex to hop from state to state—latching dynamics. We show that in one such model, and in simple spatial memory tasks we have given to human subjects, short-term memory can be limited to similar low capacity by interference effects, in tasks terminated by errors, and can exhibit similar sublinear scaling, when errors are overlooked. The same mechanism can drive serial recall if combined with weak order-encoding plasticity. Finally, even when storing randomly correlated patterns of activity the network demonstrates correlation-driven latching waves, which are reflected at the outer extremes of pattern space.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="66482b4faacf5cee73d08c3df10765d4" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":104448905,"asset_id":104822617,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/104448905/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="104822617"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="104822617"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 104822617; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=104822617]").text(description); $(".js-view-count[data-work-id=104822617]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 104822617; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='104822617']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "66482b4faacf5cee73d08c3df10765d4" } } $('.js-work-strip[data-work-id=104822617]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":104822617,"title":"Latching dynamics as a basis for short-term recall","translated_title":"","metadata":{"abstract":"We discuss simple models for the transient storage in short-term memory of cortical patterns of activity, all based on the notion that their recall exploits the natural tendency of the cortex to hop from state to state—latching dynamics. 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Finally, even when storing randomly correlated patterns of activity the network demonstrates correlation-driven latching waves, which are reflected at the outer extremes of pattern space.","internal_url":"https://www.academia.edu/104822617/Latching_dynamics_as_a_basis_for_short_term_recall","translated_internal_url":"","created_at":"2023-07-22T09:22:55.601-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":32288545,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[{"id":104448905,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/104448905/thumbnails/1.jpg","file_name":"pcbi.1008809.pdf","download_url":"https://www.academia.edu/attachments/104448905/download_file","bulk_download_file_name":"Latching_dynamics_as_a_basis_for_short_t.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/104448905/pcbi.1008809-libre.pdf?1690046223=\u0026response-content-disposition=attachment%3B+filename%3DLatching_dynamics_as_a_basis_for_short_t.pdf\u0026Expires=1743730652\u0026Signature=cj1wjuo2vSTcwoOIkyh9eM-JytlwR86Rw0PufvZtw0sw~IJOS3MlhgjK76kEJMPke7yj7a8BQL1RnDgbO6LM~XBL15fs5MPMoe66u~MOxpHCwtDcOuTnqfAwu24CUDmeYfoATWZmD4eam4gakqunMh3AeEKvFvCDNNnzlwWOHsv-RvtzvE8TNV5tFvWvP7kTw5g8ASi8bEdNyFm654EnJEpBdUDiaJNvlZx-53Nt2V8ClgV~IwJyxP4xzhc2qP1WItefe8OMzVEa8Aeai489g-WCkDh3D9PZ9txxX~T6bQFqJEuvnz9Pycnx1jzaKP~epKlOcWdNqwfCYFmdbURJrQ__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Latching_dynamics_as_a_basis_for_short_term_recall","translated_slug":"","page_count":28,"language":"en","content_type":"Work","summary":"We discuss simple models for the transient storage in short-term memory of cortical patterns of activity, all based on the notion that their recall exploits the natural tendency of the cortex to hop from state to state—latching dynamics. We show that in one such model, and in simple spatial memory tasks we have given to human subjects, short-term memory can be limited to similar low capacity by interference effects, in tasks terminated by errors, and can exhibit similar sublinear scaling, when errors are overlooked. The same mechanism can drive serial recall if combined with weak order-encoding plasticity. Finally, even when storing randomly correlated patterns of activity the network demonstrates correlation-driven latching waves, which are reflected at the outer extremes of pattern space.","owner":{"id":32288545,"first_name":"Alessandro","middle_initials":null,"last_name":"Treves","page_name":"ATreves","domain_name":"sissa","created_at":"2015-06-17T08:51:16.709-07:00","display_name":"Alessandro Treves","url":"https://sissa.academia.edu/ATreves"},"attachments":[{"id":104448905,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/104448905/thumbnails/1.jpg","file_name":"pcbi.1008809.pdf","download_url":"https://www.academia.edu/attachments/104448905/download_file","bulk_download_file_name":"Latching_dynamics_as_a_basis_for_short_t.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/104448905/pcbi.1008809-libre.pdf?1690046223=\u0026response-content-disposition=attachment%3B+filename%3DLatching_dynamics_as_a_basis_for_short_t.pdf\u0026Expires=1743730652\u0026Signature=cj1wjuo2vSTcwoOIkyh9eM-JytlwR86Rw0PufvZtw0sw~IJOS3MlhgjK76kEJMPke7yj7a8BQL1RnDgbO6LM~XBL15fs5MPMoe66u~MOxpHCwtDcOuTnqfAwu24CUDmeYfoATWZmD4eam4gakqunMh3AeEKvFvCDNNnzlwWOHsv-RvtzvE8TNV5tFvWvP7kTw5g8ASi8bEdNyFm654EnJEpBdUDiaJNvlZx-53Nt2V8ClgV~IwJyxP4xzhc2qP1WItefe8OMzVEa8Aeai489g-WCkDh3D9PZ9txxX~T6bQFqJEuvnz9Pycnx1jzaKP~epKlOcWdNqwfCYFmdbURJrQ__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science"},{"id":7710,"name":"Biology","url":"https://www.academia.edu/Documents/in/Biology"},{"id":26327,"name":"Medicine","url":"https://www.academia.edu/Documents/in/Medicine"},{"id":47884,"name":"Biological Sciences","url":"https://www.academia.edu/Documents/in/Biological_Sciences"},{"id":80414,"name":"Mathematical Sciences","url":"https://www.academia.edu/Documents/in/Mathematical_Sciences"},{"id":440689,"name":"Recall","url":"https://www.academia.edu/Documents/in/Recall"},{"id":522464,"name":"Short Term Memory","url":"https://www.academia.edu/Documents/in/Short_Term_Memory"}],"urls":[{"id":33020598,"url":"https://dx.plos.org/10.1371/journal.pcbi.1008809"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") if (false) { Aedu.setUpFigureCarousel('profile-work-104822617-figures'); } }); </script> <div class="js-work-strip profile--work_container" data-work-id="104822616"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/104822616/Continuous_attractors_for_dynamic_memories"><img alt="Research paper thumbnail of Continuous attractors for dynamic memories" class="work-thumbnail" src="https://attachments.academia-assets.com/104448890/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/104822616/Continuous_attractors_for_dynamic_memories">Continuous attractors for dynamic memories</a></div><div class="wp-workCard_item"><span>eLife</span><span>, 2021</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Episodic memory has a dynamic nature: when we recall past episodes, we retrieve not only their co...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Episodic memory has a dynamic nature: when we recall past episodes, we retrieve not only their content, but also their temporal structure. The phenomenon of replay, in the hippocampus of mammals, offers a remarkable example of this temporal dynamics. However, most quantitative models of memory treat memories as static configurations, neglecting the temporal unfolding of the retrieval process. Here, we introduce a continuous attractor network model with a memory-dependent asymmetric component in the synaptic connectivity, which spontaneously breaks the equilibrium of the memory configurations and produces dynamic retrieval. The detailed analysis of the model with analytical calculations and numerical simulations shows that it can robustly retrieve multiple dynamical memories, and that this feature is largely independent of the details of its implementation. By calculating the storage capacity, we show that the dynamic component does not impair memory capacity, and can even enhance it...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="0050eef2b9d1c481cafecbef88686aa8" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":104448890,"asset_id":104822616,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/104448890/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="104822616"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="104822616"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 104822616; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=104822616]").text(description); $(".js-view-count[data-work-id=104822616]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 104822616; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='104822616']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "0050eef2b9d1c481cafecbef88686aa8" } } $('.js-work-strip[data-work-id=104822616]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":104822616,"title":"Continuous attractors for dynamic memories","translated_title":"","metadata":{"abstract":"Episodic memory has a dynamic nature: when we recall past episodes, we retrieve not only their content, but also their temporal structure. The phenomenon of replay, in the hippocampus of mammals, offers a remarkable example of this temporal dynamics. However, most quantitative models of memory treat memories as static configurations, neglecting the temporal unfolding of the retrieval process. Here, we introduce a continuous attractor network model with a memory-dependent asymmetric component in the synaptic connectivity, which spontaneously breaks the equilibrium of the memory configurations and produces dynamic retrieval. The detailed analysis of the model with analytical calculations and numerical simulations shows that it can robustly retrieve multiple dynamical memories, and that this feature is largely independent of the details of its implementation. By calculating the storage capacity, we show that the dynamic component does not impair memory capacity, and can even enhance it...","publisher":"eLife Sciences Publications, Ltd","ai_title_tag":"Dynamic Memory Retrieval via Continuous Attractor Networks","publication_date":{"day":null,"month":null,"year":2021,"errors":{}},"publication_name":"eLife"},"translated_abstract":"Episodic memory has a dynamic nature: when we recall past episodes, we retrieve not only their content, but also their temporal structure. The phenomenon of replay, in the hippocampus of mammals, offers a remarkable example of this temporal dynamics. However, most quantitative models of memory treat memories as static configurations, neglecting the temporal unfolding of the retrieval process. Here, we introduce a continuous attractor network model with a memory-dependent asymmetric component in the synaptic connectivity, which spontaneously breaks the equilibrium of the memory configurations and produces dynamic retrieval. The detailed analysis of the model with analytical calculations and numerical simulations shows that it can robustly retrieve multiple dynamical memories, and that this feature is largely independent of the details of its implementation. By calculating the storage capacity, we show that the dynamic component does not impair memory capacity, and can even enhance it...","internal_url":"https://www.academia.edu/104822616/Continuous_attractors_for_dynamic_memories","translated_internal_url":"","created_at":"2023-07-22T09:22:55.359-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":32288545,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[{"id":104448890,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/104448890/thumbnails/1.jpg","file_name":"elife-69499-v1.pdf","download_url":"https://www.academia.edu/attachments/104448890/download_file","bulk_download_file_name":"Continuous_attractors_for_dynamic_memori.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/104448890/elife-69499-v1-libre.pdf?1690046137=\u0026response-content-disposition=attachment%3B+filename%3DContinuous_attractors_for_dynamic_memori.pdf\u0026Expires=1743730652\u0026Signature=hHk650qgiNEM0u1gRnYEipJeuM1EUSNoH6ujUlfSC7RjWnvgkflG9R1CLBNLz5urSBps4ulsWzIhtJPlWSj6zQRoviJ-YZ~gn~LLTBoYcWVenY5bOz-wq7bh01CSJQzV8LYwrVSYEi~CevmklL1yV4m2WeoMEJFYUsk6nVGRy0XzM38ColBB8QneIdLVVTtnh~JmZdXmqxlRpio-eTXnOaZX9acnzVZHeq1tT6aE-JYPob2oU6H58KnjDgnGAKNr0vhiEwHKP~O5Yyq234S7-2IwZs7rB~UVREK01tD9sAGo~9NnSpvS-eco5ULmihUV0yMhX9ivi3W1yaknLBU8VQ__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Continuous_attractors_for_dynamic_memories","translated_slug":"","page_count":28,"language":"en","content_type":"Work","summary":"Episodic memory has a dynamic nature: when we recall past episodes, we retrieve not only their content, but also their temporal structure. 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") if (false) { Aedu.setUpFigureCarousel('profile-work-104822616-figures'); } }); </script> <div class="js-work-strip profile--work_container" data-work-id="104822615"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/104822615/Efficiency_of_local_learning_rules_in_threshold_linear_associative_networks"><img alt="Research paper thumbnail of Efficiency of local learning rules in threshold-linear associative networks" class="work-thumbnail" src="https://attachments.academia-assets.com/104448903/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/104822615/Efficiency_of_local_learning_rules_in_threshold_linear_associative_networks">Efficiency of local learning rules in threshold-linear associative networks</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">We show that associative networks of threshold linear units endowed with Hebbian learning can ope...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">We show that associative networks of threshold linear units endowed with Hebbian learning can operate closer to the Gardner optimal storage capacity than their binary counterparts and even surpass this bound. This is largely achieved through a sparsification of the retrieved patterns, which we analyze for theoretical and empirical distributions of activity. As reaching the optimal capacity via non-local learning rules like back-propagation requires slow and neurally implausible training procedures, our results indicate that one-shot self-organized Hebbian learning can be just as efficient.</span></div><div class="wp-workCard_item"><div class="carousel-container carousel-container--sm" id="profile-work-104822615-figures"><div class="prev-slide-container js-prev-button-container"><button aria-label="Previous" class="carousel-navigation-button js-profile-work-104822615-figures-prev"><span class="material-symbols-outlined" style="font-size: 24px" translate="no">arrow_back_ios</span></button></div><div class="slides-container js-slides-container"><figure class="figure-slide-container"><a href="https://www.academia.edu/figures/25310706/figure-1-dependence-of-the-gardner-capacity-on-different"><img alt="FIG. 1. Dependence of the Gardner capacity a. on different parameters. a. plotted in (a) as a function of g and f (di = 1.1, dz = 2), in (b) as a function of a = dj/de for different values of f (g = 10,d1 = 1.1) in (c) and (d) as a function of di and dz for g = 0.2 and g = 10, respectively (f = 0.5). Note that fixing f, restricts the available range of a, as a cannot be larger than f; the inaccessible ranges of f values are shadowed in (b), (c) and (d). " class="figure-slide-image" src="https://figures.academia-assets.com/104448903/figure_001.jpg" /></a></figure><figure class="figure-slide-container"><a href="https://www.academia.edu/figures/25310707/figure-2-hebbian-capacity-vs-gardner-bound-as-function-of"><img alt="FIG. 2. Hebbian capacity vs Gardner bound. (a) a¥ as a function of f for different sample distribution of stored patterns compared to the universal a bound for errorless retrieval, i.e. the g— co limit of Eq (3); the red diamonds are reached with explicit TL perceptron training. (b) the sparsification of the stored patterns at retrieval, for Hebbian networks loaded at their capacity. " class="figure-slide-image" src="https://figures.academia-assets.com/104448903/figure_002.jpg" /></a></figure><figure class="figure-slide-container"><a href="https://www.academia.edu/figures/25310708/figure-3-hebbian-learning-vs-the-gardner-errorless-bound-for"><img alt="FIG. 3. Hebbian learning vs. the Gardner errorless bound for experimental data. (a,b) Examples of the histograms of two experimentally recorded spike counts (blue) and the retrieved distribution, if the patterns were stored using Hebbian learning (orange). Note that the retrieved distributions a la Gardner would be the same as the stored patterns. (c) Analytically calculated universal Gardner capacity a¢(f) (blue), ie. the g—> oo limit of Eq (3), compared to ae"? for the Hebbian learning of an exponential distribution (orange). The diamonds are the values al!naive achieved with the 9 original discrete distributions, and the circles the values at “*P for those 4 that can be fit to an exponential distribution. The asterisk marks the two cells whose distribution is plotted in a) and b). d) Sparsification of the retrieved patterns, for Hebbian learning. empirical distributions achieve a lower capacity than that of their exponential fit, which leads to further sparsificatior at retrieval. This is illustrated in Fig. 3d, which shows the ratio of the sparsity of patterns retrieved after Hebbiar storage to that of the originally stored pattern, vs. f. " class="figure-slide-image" src="https://figures.academia-assets.com/104448903/figure_003.jpg" /></a></figure><figure class="figure-slide-container"><a href="https://www.academia.edu/figures/25310709/figure-4-in-order-to-compute-the-average-of-the-delta"><img alt="In order to compute the average of the delta functions in Eq.(7), we use the approximation " class="figure-slide-image" src="https://figures.academia-assets.com/104448903/figure_004.jpg" /></a></figure><figure class="figure-slide-container"><a href="https://www.academia.edu/figures/25310714/figure-5-where-in-the-last-passage-we-made-simple-change-of"><img alt="where in the last passage we made a simple change of variables. Therefore we can rewrite Eq. (30) as where G = G(q, G,m, m, E) given by Eq. (21), and set them to zero to find the maximum of Eq. (21), with W(, g, E) given by Eq. (26) and M(q,m) given by Eq. (36). With the first three derivatives equalized to zero, which are applied only to the second and third term of Eq. (21), and assuming Cq >> m? and |C(1 — 2q)| >> m? as C > ov, we obtain the relations " class="figure-slide-image" src="https://figures.academia-assets.com/104448903/figure_005.jpg" /></a></figure><figure class="figure-slide-container"><a href="https://www.academia.edu/figures/25310717/figure-6-substituting-in-to-the-leading-order-leads-to-eq"><img alt="Substituting x in a, to the leading order leads to Eq.(5) presented in the main text. We now proceed to evaluating a,, we apply the same Taylor expansion as before For the purpose of assessing whether the Gardner capacity for errorless retrieval can be reached with explicit training, we can decompose a network of, say, N + 1 = 10001 units into N + 1 independent threshold linear perceptrons. A threshold linear perceptron is just a 1-layer feedforward neural network with N inputs and one output, the activity of which is given by a threshold-linear activation function. " class="figure-slide-image" src="https://figures.academia-assets.com/104448903/figure_006.jpg" /></a></figure><figure class="figure-slide-container"><a href="https://www.academia.edu/figures/25310743/figure-1-suplementary-to-comparison-between-the-hebbian-and"><img alt="FIG. 1. Suplementary to Fig. (2). Comparison between the Hebbian and Gardner storage capacity for 3 discrete distributions The upper row considers as sparsity parameter the one of the input pattern, the lower row the one of the retrieved pattern The Garner capacity is that given by Eq. (3) of the main text As a supplement to Fig. 2 of the main text, reproduced here in the 3 separate panels in the upper row in Fig. 1, we show a comparison between the Hebbian capacity and the Gardner one when plotted as a function of the output sparsity (in the bottom row of Fig. 1). The Gardner storage capacity is now in each of these 3 cases above the Hebbian capacity, taken as a function of the output sparsity instead of the input one. One can see that all distributions are such that (7) = Ee dnP(n)n = a and ( =f, dnP(n)n? = a, so that a coin- cides with the sparsity (7)?/(n?) of the network. The fraction of active unite i is Sn related to a as f = a,9a/5,9a/4, 2a respectively. " class="figure-slide-image" src="https://figures.academia-assets.com/104448903/figure_007.jpg" /></a></figure><figure class="figure-slide-container"><a href="https://www.academia.edu/figures/25310746/figure-3-comparison-between-the-values-of-the-storage"><img alt="FIG. 3. Comparison between the values of the storage capacity a la Gardner and Hebbian, for the 9 empirical distributions extracted from [3]. " class="figure-slide-image" src="https://figures.academia-assets.com/104448903/figure_008.jpg" /></a></figure><figure class="figure-slide-container"><a href="https://www.academia.edu/figures/25310748/table-1-therefore-integrating-over-in-eq-leads-to"><img alt="Therefore, integrating over J in Eq. (24), leads to: " class="figure-slide-image" src="https://figures.academia-assets.com/104448903/table_001.jpg" /></a></figure><figure class="figure-slide-container"><a href="https://www.academia.edu/figures/25310754/figure-2-in-the-real-activity-distributions-we-use-each"><img alt="In the real activity distributions we use, each neuron emits, in time bins of fixed duration (we use 100msec), 0,...,7,---,;Nmax Spikes, with relative frequency c,, such that mee Cn = 1. These values are taken from Fig. 2 of [3] and correspond to the histograms in blue in Fig.2 below (and in Fig.3 of the main text); they are assumed to be the distributions of the patterns to be stored. If the weights are those described by the Gardner calculation, these patterns can be retrieved as they are, and their distribution remains the same. If they are stored with Hebbian weights close to the maximal Hebbian capacity, however, the retrieved distributions look different, and they can be derived as follows. 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This is largely achieved through a sparsification of the retrieved patterns, which we analyze for theoretical and empirical distributions of activity. As reaching the optimal capacity via non-local learning rules like back-propagation requires slow and neurally implausible training procedures, our results indicate that one-shot self-organized Hebbian learning can be just as efficient.","publisher":"Cold Spring Harbor Laboratory","ai_title_tag":"Local Hebbian Learning in Efficient Associative Networks","publication_date":{"day":null,"month":null,"year":2020,"errors":{}}},"translated_abstract":"We show that associative networks of threshold linear units endowed with Hebbian learning can operate closer to the Gardner optimal storage capacity than their binary counterparts and even surpass this bound. This is largely achieved through a sparsification of the retrieved patterns, which we analyze for theoretical and empirical distributions of activity. As reaching the optimal capacity via non-local learning rules like back-propagation requires slow and neurally implausible training procedures, our results indicate that one-shot self-organized Hebbian learning can be just as efficient.","internal_url":"https://www.academia.edu/104822615/Efficiency_of_local_learning_rules_in_threshold_linear_associative_networks","translated_internal_url":"","created_at":"2023-07-22T09:22:55.070-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":32288545,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[{"id":104448903,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/104448903/thumbnails/1.jpg","file_name":"2007.12584v1.pdf","download_url":"https://www.academia.edu/attachments/104448903/download_file","bulk_download_file_name":"Efficiency_of_local_learning_rules_in_th.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/104448903/2007.12584v1-libre.pdf?1690046122=\u0026response-content-disposition=attachment%3B+filename%3DEfficiency_of_local_learning_rules_in_th.pdf\u0026Expires=1743730652\u0026Signature=XSibSvdZhs97MmLhy~CYYzmr~Dr-fpXLWhALiPx7D4mRlEluT1NOcHDjwQ8BLzcgnjSO7qbOUuZsqErKQuWbOZjImk0u3SmjYpqj-uCSG7sCYPfPoHffktOsWmy2VH0i6jM6Ab9bExiGja-5EXKqxCdLCDuN~rd1uEvIPvspRrFEY1-UPhXLaRQ13lRafCQz0sexifOWSq1uPm0mza4x350PUAolXm~xfnAlzjRHifyO6p7Gvc4eakMtIOGWpeDdbt4jw-afiQ5yG1cy-tLmWOcsVZU63CsDKvQOQm2U4w~VWOK9hVq5CPucrxWhPtzUIHw0oNtPi34IfQfvyeKj6Q__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Efficiency_of_local_learning_rules_in_threshold_linear_associative_networks","translated_slug":"","page_count":19,"language":"en","content_type":"Work","summary":"We show that associative networks of threshold linear units endowed with Hebbian learning can operate closer to the Gardner optimal storage capacity than their binary counterparts and even surpass this bound. This is largely achieved through a sparsification of the retrieved patterns, which we analyze for theoretical and empirical distributions of activity. As reaching the optimal capacity via non-local learning rules like back-propagation requires slow and neurally implausible training procedures, our results indicate that one-shot self-organized Hebbian learning can be just as efficient.","owner":{"id":32288545,"first_name":"Alessandro","middle_initials":null,"last_name":"Treves","page_name":"ATreves","domain_name":"sissa","created_at":"2015-06-17T08:51:16.709-07:00","display_name":"Alessandro Treves","url":"https://sissa.academia.edu/ATreves"},"attachments":[{"id":104448903,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/104448903/thumbnails/1.jpg","file_name":"2007.12584v1.pdf","download_url":"https://www.academia.edu/attachments/104448903/download_file","bulk_download_file_name":"Efficiency_of_local_learning_rules_in_th.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/104448903/2007.12584v1-libre.pdf?1690046122=\u0026response-content-disposition=attachment%3B+filename%3DEfficiency_of_local_learning_rules_in_th.pdf\u0026Expires=1743730652\u0026Signature=XSibSvdZhs97MmLhy~CYYzmr~Dr-fpXLWhALiPx7D4mRlEluT1NOcHDjwQ8BLzcgnjSO7qbOUuZsqErKQuWbOZjImk0u3SmjYpqj-uCSG7sCYPfPoHffktOsWmy2VH0i6jM6Ab9bExiGja-5EXKqxCdLCDuN~rd1uEvIPvspRrFEY1-UPhXLaRQ13lRafCQz0sexifOWSq1uPm0mza4x350PUAolXm~xfnAlzjRHifyO6p7Gvc4eakMtIOGWpeDdbt4jw-afiQ5yG1cy-tLmWOcsVZU63CsDKvQOQm2U4w~VWOK9hVq5CPucrxWhPtzUIHw0oNtPi34IfQfvyeKj6Q__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science"},{"id":498,"name":"Physics","url":"https://www.academia.edu/Documents/in/Physics"},{"id":7710,"name":"Biology","url":"https://www.academia.edu/Documents/in/Biology"},{"id":26327,"name":"Medicine","url":"https://www.academia.edu/Documents/in/Medicine"},{"id":51073,"name":"Recurrent Neural Network","url":"https://www.academia.edu/Documents/in/Recurrent_Neural_Network"}],"urls":[{"id":33020596,"url":"https://syndication.highwire.org/content/doi/10.1101/2020.07.28.225318"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") if (true) { Aedu.setUpFigureCarousel('profile-work-104822615-figures'); } }); </script> <div class="js-work-strip profile--work_container" data-work-id="104822614"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/104822614/Selforganization_of_modular_activity_of_grid_cells"><img alt="Research paper thumbnail of Selforganization of modular activity of grid cells" class="work-thumbnail" src="https://attachments.academia-assets.com/104448913/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/104822614/Selforganization_of_modular_activity_of_grid_cells">Selforganization of modular activity of grid cells</a></div><div class="wp-workCard_item"><span>Hippocampus</span><span>, 2017</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">A unique topographical representation of space is found in the concerted activity of grid cells i...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">A unique topographical representation of space is found in the concerted activity of grid cells in the rodent medial entorhinal cortex. Many among the principal cells in this region exhibit a hexagonal firing pattern, in which each cell expresses its own set of place fields (spatial phases) at the vertices of a triangular grid, the spacing and orientation of which are typically shared with neighboring cells. Grid spacing, in particular, has been found to increase along the dorso-ventral axis of the entorhinal cortex but in discrete steps, that is, with a modular structure. In this study, we show that such a modular activity may result from the self-organization of interacting units, which individually would not show discrete but rather continuously varying grid spacing. Within our "adaptation" network model, the effect of a continuously varying time constant, which determines grid spacing in the isolated cell model, is modulated by recurrent collateral connections, which tend to produce a few subnetworks, akin to magnetic domains, each with its own grid spacing. In agreement with experimental evidence, the modular structure is tightly defined by grid spacing, but also involves grid orientation and distortion, due to interactions across modules. Thus, our study sheds light onto a possible mechanism, other than simply assuming separate networks a priori, underlying the formation of modular grid representations.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="596e868f448dfea3c7827c93cc12294f" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":104448913,"asset_id":104822614,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/104448913/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="104822614"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="104822614"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 104822614; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=104822614]").text(description); $(".js-view-count[data-work-id=104822614]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 104822614; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='104822614']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "596e868f448dfea3c7827c93cc12294f" } } $('.js-work-strip[data-work-id=104822614]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":104822614,"title":"Selforganization of modular activity of grid cells","translated_title":"","metadata":{"publisher":"Wiley","ai_title_tag":"Self-Organization of Modular Grid Cell Activity","grobid_abstract":"A unique topographical representation of space is found in the concerted activity of grid cells in the rodent medial entorhinal cortex. Many among the principal cells in this region exhibit a hexagonal firing pattern, in which each cell expresses its own set of place fields (spatial phases) at the vertices of a triangular grid, the spacing and orientation of which are typically shared with neighboring cells. Grid spacing, in particular, has been found to increase along the dorso-ventral axis of the entorhinal cortex but in discrete steps, that is, with a modular structure. In this study, we show that such a modular activity may result from the self-organization of interacting units, which individually would not show discrete but rather continuously varying grid spacing. Within our \"adaptation\" network model, the effect of a continuously varying time constant, which determines grid spacing in the isolated cell model, is modulated by recurrent collateral connections, which tend to produce a few subnetworks, akin to magnetic domains, each with its own grid spacing. In agreement with experimental evidence, the modular structure is tightly defined by grid spacing, but also involves grid orientation and distortion, due to interactions across modules. Thus, our study sheds light onto a possible mechanism, other than simply assuming separate networks a priori, underlying the formation of modular grid representations.","publication_date":{"day":null,"month":null,"year":2017,"errors":{}},"publication_name":"Hippocampus","grobid_abstract_attachment_id":104448913},"translated_abstract":null,"internal_url":"https://www.academia.edu/104822614/Selforganization_of_modular_activity_of_grid_cells","translated_internal_url":"","created_at":"2023-07-22T09:22:54.799-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":32288545,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[{"id":104448913,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/104448913/thumbnails/1.jpg","file_name":"pmc5697658.pdf","download_url":"https://www.academia.edu/attachments/104448913/download_file","bulk_download_file_name":"Selforganization_of_modular_activity_of.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/104448913/pmc5697658-libre.pdf?1690046098=\u0026response-content-disposition=attachment%3B+filename%3DSelforganization_of_modular_activity_of.pdf\u0026Expires=1743730653\u0026Signature=I8VqKm7b0p5HOZnhsDnkz7BN0zV4i1F0sbCeCaI5SBoCJ3uKWiZ~uBJFycd~~5PAmqzzd29VQhhEPe3QqpNbhsJXwnqbHbnyJrCKHDHcOI7Grf8vdQi6iXLg~W7dX96Yz-NQZ5CVe-Lx0gK93LaWl2vkn95-xDi6F9DZuxQNiJ-Y20buE7wSmYBUk-fsjCfndkDOKlOmz9WAqlEpuLeHAB6pgYDh9t~5iI5VrAl6eZYExRXdwIrNmJTvgN7UB2AROl~aaJqzDnmYONEEvAhbfi6Mx3MSiQKoGVnzsbgxbcxjeXUBu4zU1sItDZXWn107g2o5rbVBRRLxZNCMXDIJbQ__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Selforganization_of_modular_activity_of_grid_cells","translated_slug":"","page_count":10,"language":"en","content_type":"Work","summary":"A unique topographical representation of space is found in the concerted activity of grid cells in the rodent medial entorhinal cortex. Many among the principal cells in this region exhibit a hexagonal firing pattern, in which each cell expresses its own set of place fields (spatial phases) at the vertices of a triangular grid, the spacing and orientation of which are typically shared with neighboring cells. Grid spacing, in particular, has been found to increase along the dorso-ventral axis of the entorhinal cortex but in discrete steps, that is, with a modular structure. In this study, we show that such a modular activity may result from the self-organization of interacting units, which individually would not show discrete but rather continuously varying grid spacing. Within our \"adaptation\" network model, the effect of a continuously varying time constant, which determines grid spacing in the isolated cell model, is modulated by recurrent collateral connections, which tend to produce a few subnetworks, akin to magnetic domains, each with its own grid spacing. In agreement with experimental evidence, the modular structure is tightly defined by grid spacing, but also involves grid orientation and distortion, due to interactions across modules. Thus, our study sheds light onto a possible mechanism, other than simply assuming separate networks a priori, underlying the formation of modular grid representations.","owner":{"id":32288545,"first_name":"Alessandro","middle_initials":null,"last_name":"Treves","page_name":"ATreves","domain_name":"sissa","created_at":"2015-06-17T08:51:16.709-07:00","display_name":"Alessandro Treves","url":"https://sissa.academia.edu/ATreves"},"attachments":[{"id":104448913,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/104448913/thumbnails/1.jpg","file_name":"pmc5697658.pdf","download_url":"https://www.academia.edu/attachments/104448913/download_file","bulk_download_file_name":"Selforganization_of_modular_activity_of.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/104448913/pmc5697658-libre.pdf?1690046098=\u0026response-content-disposition=attachment%3B+filename%3DSelforganization_of_modular_activity_of.pdf\u0026Expires=1743730653\u0026Signature=I8VqKm7b0p5HOZnhsDnkz7BN0zV4i1F0sbCeCaI5SBoCJ3uKWiZ~uBJFycd~~5PAmqzzd29VQhhEPe3QqpNbhsJXwnqbHbnyJrCKHDHcOI7Grf8vdQi6iXLg~W7dX96Yz-NQZ5CVe-Lx0gK93LaWl2vkn95-xDi6F9DZuxQNiJ-Y20buE7wSmYBUk-fsjCfndkDOKlOmz9WAqlEpuLeHAB6pgYDh9t~5iI5VrAl6eZYExRXdwIrNmJTvgN7UB2AROl~aaJqzDnmYONEEvAhbfi6Mx3MSiQKoGVnzsbgxbcxjeXUBu4zU1sItDZXWn107g2o5rbVBRRLxZNCMXDIJbQ__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":161,"name":"Neuroscience","url":"https://www.academia.edu/Documents/in/Neuroscience"},{"id":237,"name":"Cognitive Science","url":"https://www.academia.edu/Documents/in/Cognitive_Science"},{"id":498,"name":"Physics","url":"https://www.academia.edu/Documents/in/Physics"},{"id":26327,"name":"Medicine","url":"https://www.academia.edu/Documents/in/Medicine"},{"id":39035,"name":"Modules","url":"https://www.academia.edu/Documents/in/Modules"},{"id":50118,"name":"Representation","url":"https://www.academia.edu/Documents/in/Representation"},{"id":57556,"name":"Hippocampus","url":"https://www.academia.edu/Documents/in/Hippocampus"},{"id":61684,"name":"Model","url":"https://www.academia.edu/Documents/in/Model"},{"id":153623,"name":"Direction","url":"https://www.academia.edu/Documents/in/Direction"},{"id":165671,"name":"Astronomía","url":"https://www.academia.edu/Documents/in/Astronom%C3%ADa"},{"id":231348,"name":"Dorsal","url":"https://www.academia.edu/Documents/in/Dorsal"},{"id":289680,"name":"Place cells","url":"https://www.academia.edu/Documents/in/Place_cells"},{"id":416536,"name":"Science Technology","url":"https://www.academia.edu/Documents/in/Science_Technology"},{"id":955727,"name":"Action Potentials","url":"https://www.academia.edu/Documents/in/Action_Potentials"},{"id":1107332,"name":"Modular Design","url":"https://www.academia.edu/Documents/in/Modular_Design"},{"id":1189367,"name":"Fields","url":"https://www.academia.edu/Documents/in/Fields"},{"id":1239755,"name":"Neurosciences","url":"https://www.academia.edu/Documents/in/Neurosciences"},{"id":1393611,"name":"Grid Cells","url":"https://www.academia.edu/Documents/in/Grid_Cells"},{"id":2246318,"name":"Motor activity","url":"https://www.academia.edu/Documents/in/Motor_activity"}],"urls":[{"id":33020595,"url":"https://api.wiley.com/onlinelibrary/tdm/v1/articles/10.1002%2Fhipo.22765"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") if (false) { Aedu.setUpFigureCarousel('profile-work-104822614-figures'); } }); </script> <div class="js-work-strip profile--work_container" data-work-id="104822613"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/104822613/Autoassociation_memory"><img alt="Research paper thumbnail of Autoassociation memory" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title">Autoassociation memory</div><div class="wp-workCard_item"><span>Neural Networks and Brain Function</span><span>, 1997</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="104822613"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="104822613"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 104822613; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=104822613]").text(description); $(".js-view-count[data-work-id=104822613]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 104822613; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='104822613']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") if (false) { Aedu.setUpFigureCarousel('profile-work-104822613-figures'); } }); </script> <div class="js-work-strip profile--work_container" data-work-id="104822612"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/104822612/Disappearance_of_spurious_states_in_analog_associative_memories"><img alt="Research paper thumbnail of Disappearance of spurious states in analog associative memories" class="work-thumbnail" src="https://attachments.academia-assets.com/104448916/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/104822612/Disappearance_of_spurious_states_in_analog_associative_memories">Disappearance of spurious states in analog associative memories</a></div><div class="wp-workCard_item"><span>Physical review. E, Statistical, nonlinear, and soft matter physics</span><span>, 2003</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">We show that symmetric n-mixture states, when they exist, are almost never stable in autoassociat...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">We show that symmetric n-mixture states, when they exist, are almost never stable in autoassociative networks with threshold-linear units. Only with a binary coding scheme, we could find a limited region of the parameter space in which either 2-mixture or 3-mixture states are stable attractors of the dynamics.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="08cc8225833ab53fb46b67a34e2cf2e9" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":104448916,"asset_id":104822612,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/104448916/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="104822612"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="104822612"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 104822612; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=104822612]").text(description); $(".js-view-count[data-work-id=104822612]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 104822612; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='104822612']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "08cc8225833ab53fb46b67a34e2cf2e9" } } $('.js-work-strip[data-work-id=104822612]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":104822612,"title":"Disappearance of spurious states in analog associative memories","translated_title":"","metadata":{"abstract":"We show that symmetric n-mixture states, when they exist, are almost never stable in autoassociative networks with threshold-linear units. 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") if (false) { Aedu.setUpFigureCarousel('profile-work-104822612-figures'); } }); </script> <div class="js-work-strip profile--work_container" data-work-id="104822611"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/104822611/After_effects_in_the_Perception_of_Emotion_Following_Brief_Masked_Adaptor_Faces"><img alt="Research paper thumbnail of After effects in the Perception of Emotion Following Brief, Masked Adaptor Faces" class="work-thumbnail" src="https://attachments.academia-assets.com/104448915/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/104822611/After_effects_in_the_Perception_of_Emotion_Following_Brief_Masked_Adaptor_Faces">After effects in the Perception of Emotion Following Brief, Masked Adaptor Faces</a></div><div class="wp-workCard_item"><span>The Open Behavioral Science Journal</span><span>, 2008</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Adaptation aftereffects are the tendency to perceive an ambiguous target stimulus, which follows ...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Adaptation aftereffects are the tendency to perceive an ambiguous target stimulus, which follows an adaptor stimulus, as different from the adaptor. A duration dependence of face adaptation aftereffects has been demonstrated for durations of at least 500ms, for identity related judgments. Here we describe the duration dependence of the adaptation aftereffects of very brief (11.7ms-500ms) backwardly masked faces, on both expression and identity category judgments of ambiguous target faces. We find significant aftereffects at minimum duration 23.5ms for emotional expression, and 47ms for identity, but these are abolished by backward masking with an inverted face, although these same adaptors can be correctly categorized above chance. The presence of a short duration adaptation effect in expression might be mediated by rapid transfer of low spatial frequency (LSF) information. We tested this possibility by comparing aftereffects in low pass and high pass filtered ambiguous targets, and found no evidence of independent adaptation of a LSF specific channel.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="22d298caca432b3b9838d8ee2e38e9da" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":104448915,"asset_id":104822611,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/104448915/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="104822611"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="104822611"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 104822611; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=104822611]").text(description); $(".js-view-count[data-work-id=104822611]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 104822611; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='104822611']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "22d298caca432b3b9838d8ee2e38e9da" } } $('.js-work-strip[data-work-id=104822611]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":104822611,"title":"After effects in the Perception of Emotion Following Brief, Masked Adaptor Faces","translated_title":"","metadata":{"publisher":"Bentham Science Publishers Ltd.","ai_title_tag":"Duration-Dependent Adaptation Aftereffects of Masked Faces","grobid_abstract":"Adaptation aftereffects are the tendency to perceive an ambiguous target stimulus, which follows an adaptor stimulus, as different from the adaptor. 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At the level of single cells, no di!erences were found between the areas in the information conveyed about each correlate. We constructed pseudosimultaneous response vectors and applied a decoding algorithm to quantify di!erences at a population level. We found that, on average, samples of 20 MI units carried less information about both movement type and direction than SMA units in a time window of 500 ms across the movement onset; a more detailed temporal analysis has revealed that SMA precedes M1 in motor planning and execution and that along the trial M1 cells carry as much information about direction as SMA cells.</span></div><div class="wp-workCard_item"><div class="carousel-container carousel-container--sm" id="profile-work-104822610-figures"><div class="prev-slide-container js-prev-button-container"><button aria-label="Previous" class="carousel-navigation-button js-profile-work-104822610-figures-prev"><span class="material-symbols-outlined" style="font-size: 24px" translate="no">arrow_back_ios</span></button></div><div class="slides-container js-slides-container"><figure class="figure-slide-container"><a href="https://www.academia.edu/figures/16587902/figure-1-information-about-dir-vs-type-in-and-sma-single"><img alt="Fig. 1. Information about dir vs type in M1 and SMA single cells. V. Del Prete et al. / Neurocomputing 38-40 (2001) 1181-1184 32 different correlates of the neural activity. Eighty-seven and 103 cells were recorded respectively, in the right M1 and in the right SMA areas. The activity was quantified by the number of spikes emitted in a given time window along the trial, and expressed as the firing rate rf of unit i in trial k. Trials (10-20) were typically recorded for each cell and each correlate. Experimental procedures are described in detail elsewhere [1]. First, we evaluated the mutual information J(s,r) at a single neuron level, to extract the amount of information each cell carried, separately, about either movement type or direction. " class="figure-slide-image" src="https://figures.academia-assets.com/104448904/figure_001.jpg" /></a></figure><figure class="figure-slide-container"><a href="https://www.academia.edu/figures/16587918/figure-2-info-in-and-sma-right-cells-for-time-window-of-ms"><img alt="Fig. 2. Info in M1 and SMA right cells for a time window of 500 ms (on the left) and for different time windows (on the right, curve for 20 cells). " class="figure-slide-image" src="https://figures.academia-assets.com/104448904/figure_002.jpg" /></a></figure></div><div class="next-slide-container js-next-button-container"><button aria-label="Next" class="carousel-navigation-button js-profile-work-104822610-figures-next"><span class="material-symbols-outlined" style="font-size: 24px" translate="no">arrow_forward_ios</span></button></div></div></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="475012ce591b56081fd31e20cc50ac95" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":104448904,"asset_id":104822610,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/104448904/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="104822610"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="104822610"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 104822610; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=104822610]").text(description); $(".js-view-count[data-work-id=104822610]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 104822610; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='104822610']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "475012ce591b56081fd31e20cc50ac95" } } $('.js-work-strip[data-work-id=104822610]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":104822610,"title":"How much do they tell us to move?","translated_title":"","metadata":{"publisher":"Elsevier BV","grobid_abstract":"A previous study revealed that neuronal activity in primary motor cortex (MI) and supplementary motor area (SMA) of the monkey depends both on which arm(s) moved and on the direction of movement. At the level of single cells, no di!erences were found between the areas in the information conveyed about each correlate. We constructed pseudosimultaneous response vectors and applied a decoding algorithm to quantify di!erences at a population level. We found that, on average, samples of 20 MI units carried less information about both movement type and direction than SMA units in a time window of 500 ms across the movement onset; a more detailed temporal analysis has revealed that SMA precedes M1 in motor planning and execution and that along the trial M1 cells carry as much information about direction as SMA cells.","publication_date":{"day":null,"month":null,"year":2001,"errors":{}},"publication_name":"Neurocomputing","grobid_abstract_attachment_id":104448904},"translated_abstract":null,"internal_url":"https://www.academia.edu/104822610/How_much_do_they_tell_us_to_move","translated_internal_url":"","created_at":"2023-07-22T09:22:53.918-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":32288545,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[{"id":104448904,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/104448904/thumbnails/1.jpg","file_name":"s0925-231228012900558-620230722-1-ckpdmw.pdf","download_url":"https://www.academia.edu/attachments/104448904/download_file","bulk_download_file_name":"How_much_do_they_tell_us_to_move.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/104448904/s0925-231228012900558-620230722-1-ckpdmw-libre.pdf?1690046094=\u0026response-content-disposition=attachment%3B+filename%3DHow_much_do_they_tell_us_to_move.pdf\u0026Expires=1743730653\u0026Signature=NwaDzOQc3NApZtC8upcp1f4JZyoYXHDEFlnOhAsWDzYYDTUzs5NMxZRVaSLrR0eq5yKB~FNZ15A6CREK0ZZT7w6BzLDeUsUzs~hBmF2dbC2ilpC04ZDYpfQXknAmpJP3FQClIprflLrpqCg3yIBa79jDwWjRSW3mp3~ihfXDBkXGmyHQolgrtZq4lYaSlUAVl0nmLzcpq1A5SYP3mnBq2FsObA31~iqTr~5U3KsNy4rdRVX-5V-~hBf9PDcZbDnjAjAExeOCsbrtW8-wxeoYaRfw9Kxh8msQoTJ4f2NQ-xxcFsoOTTXU5tpkvIsNEAMGkTcBnV2jsAuQFF3XXtDKBA__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"How_much_do_they_tell_us_to_move","translated_slug":"","page_count":4,"language":"en","content_type":"Work","summary":"A previous study revealed that neuronal activity in primary motor cortex (MI) and supplementary motor area (SMA) of the monkey depends both on which arm(s) moved and on the direction of movement. At the level of single cells, no di!erences were found between the areas in the information conveyed about each correlate. We constructed pseudosimultaneous response vectors and applied a decoding algorithm to quantify di!erences at a population level. We found that, on average, samples of 20 MI units carried less information about both movement type and direction than SMA units in a time window of 500 ms across the movement onset; a more detailed temporal analysis has revealed that SMA precedes M1 in motor planning and execution and that along the trial M1 cells carry as much information about direction as SMA cells.","owner":{"id":32288545,"first_name":"Alessandro","middle_initials":null,"last_name":"Treves","page_name":"ATreves","domain_name":"sissa","created_at":"2015-06-17T08:51:16.709-07:00","display_name":"Alessandro Treves","url":"https://sissa.academia.edu/ATreves"},"attachments":[{"id":104448904,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/104448904/thumbnails/1.jpg","file_name":"s0925-231228012900558-620230722-1-ckpdmw.pdf","download_url":"https://www.academia.edu/attachments/104448904/download_file","bulk_download_file_name":"How_much_do_they_tell_us_to_move.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/104448904/s0925-231228012900558-620230722-1-ckpdmw-libre.pdf?1690046094=\u0026response-content-disposition=attachment%3B+filename%3DHow_much_do_they_tell_us_to_move.pdf\u0026Expires=1743730653\u0026Signature=NwaDzOQc3NApZtC8upcp1f4JZyoYXHDEFlnOhAsWDzYYDTUzs5NMxZRVaSLrR0eq5yKB~FNZ15A6CREK0ZZT7w6BzLDeUsUzs~hBmF2dbC2ilpC04ZDYpfQXknAmpJP3FQClIprflLrpqCg3yIBa79jDwWjRSW3mp3~ihfXDBkXGmyHQolgrtZq4lYaSlUAVl0nmLzcpq1A5SYP3mnBq2FsObA31~iqTr~5U3KsNy4rdRVX-5V-~hBf9PDcZbDnjAjAExeOCsbrtW8-wxeoYaRfw9Kxh8msQoTJ4f2NQ-xxcFsoOTTXU5tpkvIsNEAMGkTcBnV2jsAuQFF3XXtDKBA__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":48,"name":"Engineering","url":"https://www.academia.edu/Documents/in/Engineering"},{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science"},{"id":465,"name":"Artificial Intelligence","url":"https://www.academia.edu/Documents/in/Artificial_Intelligence"},{"id":1410,"name":"Information Theory","url":"https://www.academia.edu/Documents/in/Information_Theory"},{"id":44433,"name":"Motor planning","url":"https://www.academia.edu/Documents/in/Motor_planning"},{"id":49482,"name":"Neural coding","url":"https://www.academia.edu/Documents/in/Neural_coding"},{"id":64336,"name":"Population","url":"https://www.academia.edu/Documents/in/Population"},{"id":153836,"name":"Motor Cortex","url":"https://www.academia.edu/Documents/in/Motor_Cortex"},{"id":306573,"name":"Neurocomputing","url":"https://www.academia.edu/Documents/in/Neurocomputing"},{"id":337457,"name":"Supplementary Motor Area","url":"https://www.academia.edu/Documents/in/Supplementary_Motor_Area"},{"id":473567,"name":"Neuronal Activity","url":"https://www.academia.edu/Documents/in/Neuronal_Activity"},{"id":594811,"name":"Cellular and Molecular Neuroscience","url":"https://www.academia.edu/Documents/in/Cellular_and_Molecular_Neuroscience"},{"id":668846,"name":"Sma","url":"https://www.academia.edu/Documents/in/Sma"},{"id":766410,"name":"Primary Motor Cortex","url":"https://www.academia.edu/Documents/in/Primary_Motor_Cortex"},{"id":980062,"name":"Temporal Analysis","url":"https://www.academia.edu/Documents/in/Temporal_Analysis"},{"id":2540355,"name":"neural code","url":"https://www.academia.edu/Documents/in/neural_code"},{"id":2922956,"name":"Psychology and Cognitive Sciences","url":"https://www.academia.edu/Documents/in/Psychology_and_Cognitive_Sciences"},{"id":3370271,"name":"Time window","url":"https://www.academia.edu/Documents/in/Time_window"}],"urls":[]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") if (true) { Aedu.setUpFigureCarousel('profile-work-104822610-figures'); } }); </script> <div class="js-work-strip profile--work_container" data-work-id="104822609"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/104822609/Disorders_of_Brain_Behavior_and_Cognition_The_Neurocomputational_Perspective_edited_by_James_A_Reggia_Eytan_Ruppin_and_Dennis_L_Glanzman"><img alt="Research paper thumbnail of Disorders of Brain, Behavior and Cognition: The Neurocomputational Perspective edited by James A. Reggia, Eytan Ruppin and Dennis L. Glanzman" class="work-thumbnail" src="https://attachments.academia-assets.com/104448902/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/104822609/Disorders_of_Brain_Behavior_and_Cognition_The_Neurocomputational_Perspective_edited_by_James_A_Reggia_Eytan_Ruppin_and_Dennis_L_Glanzman">Disorders of Brain, Behavior and Cognition: The Neurocomputational Perspective edited by James A. Reggia, Eytan Ruppin and Dennis L. Glanzman</a></div><div class="wp-workCard_item"><span>Trends in Neurosciences</span><span>, 2000</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="6e16bcb26c6fd505d0a0b4c7633b09dc" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":104448902,"asset_id":104822609,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/104448902/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="104822609"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="104822609"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 104822609; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=104822609]").text(description); $(".js-view-count[data-work-id=104822609]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 104822609; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='104822609']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "6e16bcb26c6fd505d0a0b4c7633b09dc" } } $('.js-work-strip[data-work-id=104822609]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":104822609,"title":"Disorders of Brain, Behavior and Cognition: The Neurocomputational Perspective edited by James A. 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While the book includes notable chapters such as the one on semantic dementia, it raises questions about the ability of these models to offer new predictions and rigorous testing of theoretical constructs in neuroscience.","publication_date":{"day":null,"month":null,"year":2000,"errors":{}},"publication_name":"Trends in Neurosciences"},"translated_abstract":null,"internal_url":"https://www.academia.edu/104822609/Disorders_of_Brain_Behavior_and_Cognition_The_Neurocomputational_Perspective_edited_by_James_A_Reggia_Eytan_Ruppin_and_Dennis_L_Glanzman","translated_internal_url":"","created_at":"2023-07-22T09:22:53.706-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":32288545,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[{"id":104448902,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/104448902/thumbnails/1.jpg","file_name":"s0166-223628002901565-420230722-1-pwada9.pdf","download_url":"https://www.academia.edu/attachments/104448902/download_file","bulk_download_file_name":"Disorders_of_Brain_Behavior_and_Cognitio.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/104448902/s0166-223628002901565-420230722-1-pwada9-libre.pdf?1690046096=\u0026response-content-disposition=attachment%3B+filename%3DDisorders_of_Brain_Behavior_and_Cognitio.pdf\u0026Expires=1743730653\u0026Signature=BpGYVJeeUV8qf7vO7WEdwhnTX6JWr8pDRYviLNmFWfgnAQTqCDbXaujoHYBzw7wDqqACPQ3RVf98uXyDP2BEd8tYB-cCGJbA8HUi0LoImM6HF68Z4NRxOxMkhMKmzDGQugaB6zRVKScEzLMuS-HGt80vo8DT3FJDcwKO28KsYcGSCCz-h4E9OtBCHKiAIWE9kM0wPQXZXhvT-wLmfyjs6aTPOh1B2uuXwhOsYxU2HN1FOOV5~giH4jB4Ke4yw8JR88TTiIOdMMyXO719Zq2dxGO~6idHN4vXpxy4Vpb1wUm4Nsj8ThFGPgHK3Bua7W7URmmRE14j95VHH9ZipD5S-w__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Disorders_of_Brain_Behavior_and_Cognition_The_Neurocomputational_Perspective_edited_by_James_A_Reggia_Eytan_Ruppin_and_Dennis_L_Glanzman","translated_slug":"","page_count":2,"language":"en","content_type":"Work","summary":null,"owner":{"id":32288545,"first_name":"Alessandro","middle_initials":null,"last_name":"Treves","page_name":"ATreves","domain_name":"sissa","created_at":"2015-06-17T08:51:16.709-07:00","display_name":"Alessandro Treves","url":"https://sissa.academia.edu/ATreves"},"attachments":[{"id":104448902,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/104448902/thumbnails/1.jpg","file_name":"s0166-223628002901565-420230722-1-pwada9.pdf","download_url":"https://www.academia.edu/attachments/104448902/download_file","bulk_download_file_name":"Disorders_of_Brain_Behavior_and_Cognitio.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/104448902/s0166-223628002901565-420230722-1-pwada9-libre.pdf?1690046096=\u0026response-content-disposition=attachment%3B+filename%3DDisorders_of_Brain_Behavior_and_Cognitio.pdf\u0026Expires=1743730653\u0026Signature=BpGYVJeeUV8qf7vO7WEdwhnTX6JWr8pDRYviLNmFWfgnAQTqCDbXaujoHYBzw7wDqqACPQ3RVf98uXyDP2BEd8tYB-cCGJbA8HUi0LoImM6HF68Z4NRxOxMkhMKmzDGQugaB6zRVKScEzLMuS-HGt80vo8DT3FJDcwKO28KsYcGSCCz-h4E9OtBCHKiAIWE9kM0wPQXZXhvT-wLmfyjs6aTPOh1B2uuXwhOsYxU2HN1FOOV5~giH4jB4Ke4yw8JR88TTiIOdMMyXO719Zq2dxGO~6idHN4vXpxy4Vpb1wUm4Nsj8ThFGPgHK3Bua7W7URmmRE14j95VHH9ZipD5S-w__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":161,"name":"Neuroscience","url":"https://www.academia.edu/Documents/in/Neuroscience"},{"id":221,"name":"Psychology","url":"https://www.academia.edu/Documents/in/Psychology"},{"id":237,"name":"Cognitive Science","url":"https://www.academia.edu/Documents/in/Cognitive_Science"},{"id":4212,"name":"Cognition","url":"https://www.academia.edu/Documents/in/Cognition"},{"id":59487,"name":"Computation","url":"https://www.academia.edu/Documents/in/Computation"},{"id":72667,"name":"Behaviour","url":"https://www.academia.edu/Documents/in/Behaviour"},{"id":1239755,"name":"Neurosciences","url":"https://www.academia.edu/Documents/in/Neurosciences"}],"urls":[]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") if (false) { Aedu.setUpFigureCarousel('profile-work-104822609-figures'); } }); </script> <div class="js-work-strip profile--work_container" data-work-id="104822608"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/104822608/Differential_impact_of_brain_damage_on_the_access_mode_to_memory_representations_an_information_theoretic_approach"><img alt="Research paper thumbnail of Differential impact of brain damage on the access mode to memory representations: an information theoretic approach" class="work-thumbnail" src="https://attachments.academia-assets.com/104448910/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/104822608/Differential_impact_of_brain_damage_on_the_access_mode_to_memory_representations_an_information_theoretic_approach">Differential impact of brain damage on the access mode to memory representations: an information theoretic approach</a></div><div class="wp-workCard_item"><span>European Journal of Neuroscience</span><span>, 2007</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Different access modes to information stored in long-term memory can lead to different distributi...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Different access modes to information stored in long-term memory can lead to different distributions of errors in classification tasks. We have designed a famous faces memory classification task that allows for the extraction of a measure of metric content, an index of the relevance of semantic cues for classification performance. High levels of metric content are indicative of a relatively preferred semantic access mode, while low levels, and similar correct performance, suggest a preferential episodic access mode. Compared with normal controls, the metric content index was increased in patients with Alzheimer's disease (AD), decreased in patients with herpes simplex encephalitis, and unvaried in patients with insult in the prefrontal cortex. Moreover, the metric content index was found to correlate with a measure of the severity of dementia in patients with AD, and to track the progression of the disease. These results underline the role of the medial-temporal lobes and of the temporal cortex, respectively, for the episodic and semantic routes to memory retrieval. Moreover, they confirm the reliability of information theoretic measures for characterizing the structure of the surviving memory representations in memory-impaired patient populations.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="9e0611c229575c15f50d0d2d86517035" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":104448910,"asset_id":104822608,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/104448910/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="104822608"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="104822608"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 104822608; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=104822608]").text(description); $(".js-view-count[data-work-id=104822608]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 104822608; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='104822608']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "9e0611c229575c15f50d0d2d86517035" } } $('.js-work-strip[data-work-id=104822608]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":104822608,"title":"Differential impact of brain damage on the access mode to memory representations: an information theoretic approach","translated_title":"","metadata":{"publisher":"Wiley","ai_title_tag":"Brain Damage Effects on Memory Access Modes","grobid_abstract":"Different access modes to information stored in long-term memory can lead to different distributions of errors in classification tasks. We have designed a famous faces memory classification task that allows for the extraction of a measure of metric content, an index of the relevance of semantic cues for classification performance. High levels of metric content are indicative of a relatively preferred semantic access mode, while low levels, and similar correct performance, suggest a preferential episodic access mode. Compared with normal controls, the metric content index was increased in patients with Alzheimer's disease (AD), decreased in patients with herpes simplex encephalitis, and unvaried in patients with insult in the prefrontal cortex. Moreover, the metric content index was found to correlate with a measure of the severity of dementia in patients with AD, and to track the progression of the disease. These results underline the role of the medial-temporal lobes and of the temporal cortex, respectively, for the episodic and semantic routes to memory retrieval. Moreover, they confirm the reliability of information theoretic measures for characterizing the structure of the surviving memory representations in memory-impaired patient populations.","publication_date":{"day":null,"month":null,"year":2007,"errors":{}},"publication_name":"European Journal of Neuroscience","grobid_abstract_attachment_id":104448910},"translated_abstract":null,"internal_url":"https://www.academia.edu/104822608/Differential_impact_of_brain_damage_on_the_access_mode_to_memory_representations_an_information_theoretic_approach","translated_internal_url":"","created_at":"2023-07-22T09:22:53.406-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":32288545,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[{"id":104448910,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/104448910/thumbnails/1.jpg","file_name":"Lau_07.pdf","download_url":"https://www.academia.edu/attachments/104448910/download_file","bulk_download_file_name":"Differential_impact_of_brain_damage_on_t.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/104448910/Lau_07-libre.pdf?1690046098=\u0026response-content-disposition=attachment%3B+filename%3DDifferential_impact_of_brain_damage_on_t.pdf\u0026Expires=1743730653\u0026Signature=I5v2~Gyqa8Y9VctxyJhIAL50ficX2H2Iyc0WKbar2FR0ZAParPFeltNIWXTovOienjQ3xfTbIz8Gh~d4mVO3LtvZ6gDnyem3ddtZYkYuoOhzcDIaLZUsKIjw4d3DpMCX82ZWFnriIWeaqtwHVY72uP4-xE76gq~F5b4zBcLUY2mL~L4om0qlg4~QcM3j5f7u8yO4E3g2Qhccmn6AxcBhNX-3WgmXpTmycU4Hum0ExapvssgnNnULIWkskZggxzlnzD0U~aZuC2PhXob0s32dGF5QznRQZnfNYWUBfFX8510Wj5mS7RvXBHNVcq5Zj8AkWVnfUQUpQkVvXyZ3Fri4Hg__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Differential_impact_of_brain_damage_on_the_access_mode_to_memory_representations_an_information_theoretic_approach","translated_slug":"","page_count":11,"language":"en","content_type":"Work","summary":"Different access modes to information stored in long-term memory can lead to different distributions of errors in classification tasks. We have designed a famous faces memory classification task that allows for the extraction of a measure of metric content, an index of the relevance of semantic cues for classification performance. High levels of metric content are indicative of a relatively preferred semantic access mode, while low levels, and similar correct performance, suggest a preferential episodic access mode. Compared with normal controls, the metric content index was increased in patients with Alzheimer's disease (AD), decreased in patients with herpes simplex encephalitis, and unvaried in patients with insult in the prefrontal cortex. Moreover, the metric content index was found to correlate with a measure of the severity of dementia in patients with AD, and to track the progression of the disease. These results underline the role of the medial-temporal lobes and of the temporal cortex, respectively, for the episodic and semantic routes to memory retrieval. Moreover, they confirm the reliability of information theoretic measures for characterizing the structure of the surviving memory representations in memory-impaired patient populations.","owner":{"id":32288545,"first_name":"Alessandro","middle_initials":null,"last_name":"Treves","page_name":"ATreves","domain_name":"sissa","created_at":"2015-06-17T08:51:16.709-07:00","display_name":"Alessandro Treves","url":"https://sissa.academia.edu/ATreves"},"attachments":[{"id":104448910,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/104448910/thumbnails/1.jpg","file_name":"Lau_07.pdf","download_url":"https://www.academia.edu/attachments/104448910/download_file","bulk_download_file_name":"Differential_impact_of_brain_damage_on_t.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/104448910/Lau_07-libre.pdf?1690046098=\u0026response-content-disposition=attachment%3B+filename%3DDifferential_impact_of_brain_damage_on_t.pdf\u0026Expires=1743730653\u0026Signature=I5v2~Gyqa8Y9VctxyJhIAL50ficX2H2Iyc0WKbar2FR0ZAParPFeltNIWXTovOienjQ3xfTbIz8Gh~d4mVO3LtvZ6gDnyem3ddtZYkYuoOhzcDIaLZUsKIjw4d3DpMCX82ZWFnriIWeaqtwHVY72uP4-xE76gq~F5b4zBcLUY2mL~L4om0qlg4~QcM3j5f7u8yO4E3g2Qhccmn6AxcBhNX-3WgmXpTmycU4Hum0ExapvssgnNnULIWkskZggxzlnzD0U~aZuC2PhXob0s32dGF5QznRQZnfNYWUBfFX8510Wj5mS7RvXBHNVcq5Zj8AkWVnfUQUpQkVvXyZ3Fri4Hg__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":237,"name":"Cognitive Science","url":"https://www.academia.edu/Documents/in/Cognitive_Science"},{"id":3303,"name":"Human Memory","url":"https://www.academia.edu/Documents/in/Human_Memory"},{"id":26327,"name":"Medicine","url":"https://www.academia.edu/Documents/in/Medicine"},{"id":32361,"name":"Episodic Memory","url":"https://www.academia.edu/Documents/in/Episodic_Memory"},{"id":46858,"name":"Memory","url":"https://www.academia.edu/Documents/in/Memory"},{"id":57556,"name":"Hippocampus","url":"https://www.academia.edu/Documents/in/Hippocampus"},{"id":178419,"name":"Memory Retrieval","url":"https://www.academia.edu/Documents/in/Memory_Retrieval"},{"id":279027,"name":"European","url":"https://www.academia.edu/Documents/in/European"},{"id":289271,"name":"Aged","url":"https://www.academia.edu/Documents/in/Aged"},{"id":522465,"name":"Long Term Memory","url":"https://www.academia.edu/Documents/in/Long_Term_Memory"},{"id":609835,"name":"Medial Temporal Lobe","url":"https://www.academia.edu/Documents/in/Medial_Temporal_Lobe"},{"id":749302,"name":"Indexation","url":"https://www.academia.edu/Documents/in/Indexation"},{"id":1120234,"name":"Alzheimer Disease","url":"https://www.academia.edu/Documents/in/Alzheimer_Disease"},{"id":1431361,"name":"Brain Damage","url":"https://www.academia.edu/Documents/in/Brain_Damage"},{"id":2450733,"name":"Brain injuries","url":"https://www.academia.edu/Documents/in/Brain_injuries"}],"urls":[{"id":33020594,"url":"http://onlinelibrary.wiley.com/wol1/doi/10.1111/j.1460-9568.2007.05881.x/fullpdf"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") if (false) { Aedu.setUpFigureCarousel('profile-work-104822608-figures'); } }); </script> <div class="js-work-strip profile--work_container" data-work-id="104822593"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/104822593/Grid_Cells_Lose_Coherence_in_Realistic_Environments"><img alt="Research paper thumbnail of Grid Cells Lose Coherence in Realistic Environments" class="work-thumbnail" src="https://attachments.academia-assets.com/104448882/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/104822593/Grid_Cells_Lose_Coherence_in_Realistic_Environments">Grid Cells Lose Coherence in Realistic Environments</a></div><div class="wp-workCard_item"><span>Hippocampus - New Advances [Working Title]</span><span>, 2021</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Spatial cognition in naturalistic environments, for freely moving animals, may pose quite differe...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Spatial cognition in naturalistic environments, for freely moving animals, may pose quite different constraints from that studied in artificial laboratory settings. Hippocampal place cells indeed look quite different, but almost nothing is known about entorhinal cortex grid cells, in the wild. Simulating our self-organizing adaptation model of grid cell pattern formation, we consider a virtual rat randomly exploring a virtual burrow, with feedforward connectivity from place to grid units and recurrent connectivity between grid units. The virtual burrow was based on those observed by John B. Calhoun, including several chambers and tunnels. Our results indicate that lateral connectivity between grid units may enhance their “gridness” within a limited strength range, but the overall effect of the irregular geometry is to disable long-range and obstruct short-range order. What appears as a smooth continuous attractor in a flat box, kept rigid by recurrent connections, turns into an inco...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="e8b24aa56a3b5dfef561b2aa4ff66389" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":104448882,"asset_id":104822593,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/104448882/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="104822593"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="104822593"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 104822593; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=104822593]").text(description); $(".js-view-count[data-work-id=104822593]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 104822593; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='104822593']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "e8b24aa56a3b5dfef561b2aa4ff66389" } } $('.js-work-strip[data-work-id=104822593]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":104822593,"title":"Grid Cells Lose Coherence in Realistic Environments","translated_title":"","metadata":{"abstract":"Spatial cognition in naturalistic environments, for freely moving animals, may pose quite different constraints from that studied in artificial laboratory settings. Hippocampal place cells indeed look quite different, but almost nothing is known about entorhinal cortex grid cells, in the wild. Simulating our self-organizing adaptation model of grid cell pattern formation, we consider a virtual rat randomly exploring a virtual burrow, with feedforward connectivity from place to grid units and recurrent connectivity between grid units. The virtual burrow was based on those observed by John B. Calhoun, including several chambers and tunnels. Our results indicate that lateral connectivity between grid units may enhance their “gridness” within a limited strength range, but the overall effect of the irregular geometry is to disable long-range and obstruct short-range order. What appears as a smooth continuous attractor in a flat box, kept rigid by recurrent connections, turns into an inco...","publisher":"IntechOpen","ai_title_tag":"Grid Cell Coherence in Naturalistic Settings","publication_date":{"day":null,"month":null,"year":2021,"errors":{}},"publication_name":"Hippocampus - New Advances [Working Title]"},"translated_abstract":"Spatial cognition in naturalistic environments, for freely moving animals, may pose quite different constraints from that studied in artificial laboratory settings. Hippocampal place cells indeed look quite different, but almost nothing is known about entorhinal cortex grid cells, in the wild. Simulating our self-organizing adaptation model of grid cell pattern formation, we consider a virtual rat randomly exploring a virtual burrow, with feedforward connectivity from place to grid units and recurrent connectivity between grid units. The virtual burrow was based on those observed by John B. Calhoun, including several chambers and tunnels. Our results indicate that lateral connectivity between grid units may enhance their “gridness” within a limited strength range, but the overall effect of the irregular geometry is to disable long-range and obstruct short-range order. What appears as a smooth continuous attractor in a flat box, kept rigid by recurrent connections, turns into an inco...","internal_url":"https://www.academia.edu/104822593/Grid_Cells_Lose_Coherence_in_Realistic_Environments","translated_internal_url":"","created_at":"2023-07-22T09:21:13.991-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":32288545,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[{"id":104448882,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/104448882/thumbnails/1.jpg","file_name":"78836.pdf","download_url":"https://www.academia.edu/attachments/104448882/download_file","bulk_download_file_name":"Grid_Cells_Lose_Coherence_in_Realistic_E.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/104448882/78836-libre.pdf?1690046107=\u0026response-content-disposition=attachment%3B+filename%3DGrid_Cells_Lose_Coherence_in_Realistic_E.pdf\u0026Expires=1743730653\u0026Signature=FW3ASaQySAi2B56v3bu7LOCOTUY9mHuQ9qBGaMU35mFPAPYGab2PGkV1ViuWFmFH4acYAStihFs7p812TlX4BQuqkzbQdM8MYIWGox3G-x85qnbeAv9zrE1lAe6IFR2UdD0Ut18d4xcI6~lTMfwXquPolP3cXZg8nRE4EOER2~ElOi8yi9G~GUtAXCbrjIpdXgu4i5zcVqzX81jK9OM6vSm6pSCv0XDx3PhREUlz82OxZPiI6vtxkQV3e34jIFNIEAVN5oMM14W-uBj45ZjHwlHioT1iIHKaHdp1qORYxsbqtYqHl8RNcUMxKkLSraPef5bIedaKsdT39qn3B2El4A__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Grid_Cells_Lose_Coherence_in_Realistic_Environments","translated_slug":"","page_count":18,"language":"en","content_type":"Work","summary":"Spatial cognition in naturalistic environments, for freely moving animals, may pose quite different constraints from that studied in artificial laboratory settings. Hippocampal place cells indeed look quite different, but almost nothing is known about entorhinal cortex grid cells, in the wild. Simulating our self-organizing adaptation model of grid cell pattern formation, we consider a virtual rat randomly exploring a virtual burrow, with feedforward connectivity from place to grid units and recurrent connectivity between grid units. The virtual burrow was based on those observed by John B. Calhoun, including several chambers and tunnels. Our results indicate that lateral connectivity between grid units may enhance their “gridness” within a limited strength range, but the overall effect of the irregular geometry is to disable long-range and obstruct short-range order. What appears as a smooth continuous attractor in a flat box, kept rigid by recurrent connections, turns into an inco...","owner":{"id":32288545,"first_name":"Alessandro","middle_initials":null,"last_name":"Treves","page_name":"ATreves","domain_name":"sissa","created_at":"2015-06-17T08:51:16.709-07:00","display_name":"Alessandro Treves","url":"https://sissa.academia.edu/ATreves"},"attachments":[{"id":104448882,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/104448882/thumbnails/1.jpg","file_name":"78836.pdf","download_url":"https://www.academia.edu/attachments/104448882/download_file","bulk_download_file_name":"Grid_Cells_Lose_Coherence_in_Realistic_E.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/104448882/78836-libre.pdf?1690046107=\u0026response-content-disposition=attachment%3B+filename%3DGrid_Cells_Lose_Coherence_in_Realistic_E.pdf\u0026Expires=1743730653\u0026Signature=FW3ASaQySAi2B56v3bu7LOCOTUY9mHuQ9qBGaMU35mFPAPYGab2PGkV1ViuWFmFH4acYAStihFs7p812TlX4BQuqkzbQdM8MYIWGox3G-x85qnbeAv9zrE1lAe6IFR2UdD0Ut18d4xcI6~lTMfwXquPolP3cXZg8nRE4EOER2~ElOi8yi9G~GUtAXCbrjIpdXgu4i5zcVqzX81jK9OM6vSm6pSCv0XDx3PhREUlz82OxZPiI6vtxkQV3e34jIFNIEAVN5oMM14W-uBj45ZjHwlHioT1iIHKaHdp1qORYxsbqtYqHl8RNcUMxKkLSraPef5bIedaKsdT39qn3B2El4A__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science"},{"id":303727,"name":"Grid","url":"https://www.academia.edu/Documents/in/Grid"},{"id":307228,"name":"Regular Grid","url":"https://www.academia.edu/Documents/in/Regular_Grid"},{"id":589751,"name":"Burrow","url":"https://www.academia.edu/Documents/in/Burrow"}],"urls":[{"id":33020586,"url":"http://www.intechopen.com/download/pdf/78836"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") if (false) { Aedu.setUpFigureCarousel('profile-work-104822593-figures'); } }); </script> <div class="js-work-strip profile--work_container" data-work-id="67945547"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/67945547/Integration_of_grid_maps_in_merged_environments"><img alt="Research paper thumbnail of Integration of grid maps in merged environments" class="work-thumbnail" src="https://attachments.academia-assets.com/78602721/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/67945547/Integration_of_grid_maps_in_merged_environments">Integration of grid maps in merged environments</a></div><div class="wp-workCard_item"><span>Nature neuroscience</span><span>, 2018</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Natural environments are represented by local maps of grid cells and place cells that are stitche...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Natural environments are represented by local maps of grid cells and place cells that are stitched together. The manner by which transitions between map fragments are generated is unknown. We recorded grid cells while rats were trained in two rectangular compartments, A and B (each 1 m × 2 m), separated by a wall. Once distinct grid maps were established in each environment, we removed the partition and allowed the rat to explore the merged environment (2 m × 2 m). The grid patterns were largely retained along the distal walls of the box. Nearer the former partition line, individual grid fields changed location, resulting almost immediately in local spatial periodicity and continuity between the two original maps. Grid cells belonging to the same grid module retained phase relationships during the transformation. Thus, when environments are merged, grid fields reorganize rapidly to establish spatial periodicity in the area where the environments meet.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="f0c34fc24b5c9a70dc76933f9afcaae7" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":78602721,"asset_id":67945547,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/78602721/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="67945547"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="67945547"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 67945547; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=67945547]").text(description); $(".js-view-count[data-work-id=67945547]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 67945547; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='67945547']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "f0c34fc24b5c9a70dc76933f9afcaae7" } } $('.js-work-strip[data-work-id=67945547]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":67945547,"title":"Integration of grid maps in merged environments","translated_title":"","metadata":{"abstract":"Natural environments are represented by local maps of grid cells and place cells that are stitched together. 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") if (false) { Aedu.setUpFigureCarousel('profile-work-67945541-figures'); } }); </script> <div class="js-work-strip profile--work_container" data-work-id="67945538"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/67945538/Chapter_19_Information_coding_in_higher_sensory_and_memory_areas"><img alt="Research paper thumbnail of Chapter 19 Information coding in higher sensory and memory areas" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title">Chapter 19 Information coding in higher sensory and memory areas</div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Publisher Summary This chapter discusses information coding in higher sensory and memory areas. N...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Publisher Summary This chapter discusses information coding in higher sensory and memory areas. Neurons are vastly simpler than human beings are, but the metaphor is not completely silly because it illustrates the volatility of the notion of neural codes. Information theory has been developed precisely to quantify communication and is quintessential to an appraisal of neural codes. Applying information theory to neural activity (rather than to the synthetic communication systems for which it was developed) is however riddled with practical problems and subtleties, which must be clarified before reporting experimental results. The chapter considers other means of neuronal communication than the emission of action potentials or spikes and regards them as self-similar all-or-none events whose only distinctive features are the time of emission and the identity of the emitting neuron. The extent to which the firing rates of a population of neurons may or may not carry most of the information represented in the complete list of spike emission times is a question to be addressed experimentally in any given situation.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="67945538"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="67945538"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 67945538; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=67945538]").text(description); $(".js-view-count[data-work-id=67945538]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 67945538; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='67945538']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=67945538]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":67945538,"title":"Chapter 19 Information coding in higher sensory and memory areas","translated_title":"","metadata":{"abstract":"Publisher Summary This chapter discusses information coding in higher sensory and memory areas. 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The chapter considers other means of neuronal communication than the emission of action potentials or spikes and regards them as self-similar all-or-none events whose only distinctive features are the time of emission and the identity of the emitting neuron. 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Neurons are vastly simpler than human beings are, but the metaphor is not completely silly because it illustrates the volatility of the notion of neural codes. Information theory has been developed precisely to quantify communication and is quintessential to an appraisal of neural codes. Applying information theory to neural activity (rather than to the synthetic communication systems for which it was developed) is however riddled with practical problems and subtleties, which must be clarified before reporting experimental results. The chapter considers other means of neuronal communication than the emission of action potentials or spikes and regards them as self-similar all-or-none events whose only distinctive features are the time of emission and the identity of the emitting neuron. The extent to which the firing rates of a population of neurons may or may not carry most of the information represented in the complete list of spike emission times is a question to be addressed experimentally in any given situation.","owner":{"id":32288545,"first_name":"Alessandro","middle_initials":null,"last_name":"Treves","page_name":"ATreves","domain_name":"sissa","created_at":"2015-06-17T08:51:16.709-07:00","display_name":"Alessandro Treves","url":"https://sissa.academia.edu/ATreves"},"attachments":[],"research_interests":[],"urls":[{"id":16348381,"url":"http://sciencedirect.com/science/article/pii/s1383812101800221"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") if (false) { Aedu.setUpFigureCarousel('profile-work-67945538-figures'); } }); </script> <div class="js-work-strip profile--work_container" data-work-id="67945536"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/67945536/Tov%C3%A9e_M_J_Rolls_E_T_and_Treves_A_Information_encoding_and_the_responses_of_single_neurons_in_the_primate_temporal_visual_cortex_J_Neurophysiol_70_640_654"><img alt="Research paper thumbnail of Tovée, M. J., Rolls, E. T. & Treves, A. Information encoding and the responses of single neurons in the primate temporal visual cortex. J. Neurophysiol. 70, 640-654" class="work-thumbnail" src="https://attachments.academia-assets.com/78602724/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/67945536/Tov%C3%A9e_M_J_Rolls_E_T_and_Treves_A_Information_encoding_and_the_responses_of_single_neurons_in_the_primate_temporal_visual_cortex_J_Neurophysiol_70_640_654">Tovée, M. J., Rolls, E. T. & Treves, A. Information encoding and the responses of single neurons in the primate temporal visual cortex. J. Neurophysiol. 70, 640-654</a></div><div class="wp-workCard_item"><span>Journal of Neurophysiology</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="66de1c9a98ba0d99f2643f13b2a72ac8" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":78602724,"asset_id":67945536,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/78602724/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="67945536"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="67945536"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 67945536; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=67945536]").text(description); $(".js-view-count[data-work-id=67945536]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 67945536; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='67945536']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "66de1c9a98ba0d99f2643f13b2a72ac8" } } $('.js-work-strip[data-work-id=67945536]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":67945536,"title":"Tovée, M. J., Rolls, E. T. \u0026 Treves, A. Information encoding and the responses of single neurons in the primate temporal visual cortex. J. Neurophysiol. 70, 640-654","translated_title":"","metadata":{"ai_abstract":"Temporal encoding in neuronal spike trains was investigated in the primate temporal visual cortex, focusing on neuron responses to face stimuli. The analysis revealed that the first principal component in firing rates carried the majority of the information, while higher components contributed significantly less. 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") if (false) { Aedu.setUpFigureCarousel('profile-work-67945536-figures'); } }); </script> <div class="js-work-strip profile--work_container" data-work-id="67945534"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/67945534/Analytical_estimates_of_limited_sampling_in_different_information_measures"><img alt="Research paper thumbnail of Analytical estimates of limited sampling in different information measures" class="work-thumbnail" src="https://attachments.academia-assets.com/78602723/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/67945534/Analytical_estimates_of_limited_sampling_in_different_information_measures">Analytical estimates of limited sampling in different information measures</a></div><div class="wp-workCard_item"><span>Network Computation in Neural Systems</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Measuring the information carried by neuronal activity is made difficult, particularly when recor...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Measuring the information carried by neuronal activity is made difficult, particularly when recording from mammalian cells, by the limited amount of data usually available, which results in a systematic error. While empirical ad hoc procedures have been used to correct for such error, we have recently proposed a direct procedure consisting of the analytical calculation of the average error, its estimation (up to subleading terms) from the data, and its subtraction from raw information measures to yield unbiased measures. We calculate here the leading correction terms for both the average transmitted information and the conditional information and, since usually one must first regularize the data, we specify the expressions appropriate to different regularizations. Computer simulations indicate a broad range of validity of the analytical results, suggest the effectiveness of regularizing by simple binning and illustrate the advantage of this over the previously used 'bootstrap' procedure.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="62f46cb8ddd36803986db324e68c9428" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":78602723,"asset_id":67945534,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/78602723/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="67945534"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="67945534"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 67945534; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=67945534]").text(description); $(".js-view-count[data-work-id=67945534]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 67945534; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='67945534']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "62f46cb8ddd36803986db324e68c9428" } } $('.js-work-strip[data-work-id=67945534]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":67945534,"title":"Analytical estimates of limited sampling in different information measures","translated_title":"","metadata":{"grobid_abstract":"Measuring the information carried by neuronal activity is made difficult, particularly when recording from mammalian cells, by the limited amount of data usually available, which results in a systematic error. 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These developments are illuminating processes of information storage in the brain, and this is illustrated in the case of one brain structure considered here, the hippocampus.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="67945525"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="67945525"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 67945525; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=67945525]").text(description); $(".js-view-count[data-work-id=67945525]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 67945525; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='67945525']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=67945525]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":67945525,"title":"Neuronal networks in the brain","translated_title":"","metadata":{"abstract":"Recent advances in neuroscience are making it possible to describe the architecture and properties of some of the networks of neurones (brain cells) in brain structures, while neuronal network theory, including approaches related to the statistical mechanics of spin glasses, is becoming helpful in analysing the computational properties of these networks. 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These developments are illuminating processes of information storage in the brain, and this is illustrated in the case of one brain structure considered here, the hippocampus.","owner":{"id":32288545,"first_name":"Alessandro","middle_initials":null,"last_name":"Treves","page_name":"ATreves","domain_name":"sissa","created_at":"2015-06-17T08:51:16.709-07:00","display_name":"Alessandro Treves","url":"https://sissa.academia.edu/ATreves"},"attachments":[],"research_interests":[{"id":80414,"name":"Mathematical Sciences","url":"https://www.academia.edu/Documents/in/Mathematical_Sciences"},{"id":118582,"name":"Physical sciences","url":"https://www.academia.edu/Documents/in/Physical_sciences"}],"urls":[]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") if (false) { Aedu.setUpFigureCarousel('profile-work-67945525-figures'); } }); </script> </div><div class="profile--tab_content_container js-tab-pane tab-pane" data-section-id="3049887" id="papers"><div class="js-work-strip profile--work_container" data-work-id="104822617"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/104822617/Latching_dynamics_as_a_basis_for_short_term_recall"><img alt="Research paper thumbnail of Latching dynamics as a basis for short-term recall" class="work-thumbnail" src="https://attachments.academia-assets.com/104448905/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/104822617/Latching_dynamics_as_a_basis_for_short_term_recall">Latching dynamics as a basis for short-term recall</a></div><div class="wp-workCard_item"><span>PLOS Computational Biology</span><span>, 2021</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">We discuss simple models for the transient storage in short-term memory of cortical patterns of a...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">We discuss simple models for the transient storage in short-term memory of cortical patterns of activity, all based on the notion that their recall exploits the natural tendency of the cortex to hop from state to state—latching dynamics. We show that in one such model, and in simple spatial memory tasks we have given to human subjects, short-term memory can be limited to similar low capacity by interference effects, in tasks terminated by errors, and can exhibit similar sublinear scaling, when errors are overlooked. The same mechanism can drive serial recall if combined with weak order-encoding plasticity. Finally, even when storing randomly correlated patterns of activity the network demonstrates correlation-driven latching waves, which are reflected at the outer extremes of pattern space.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="66482b4faacf5cee73d08c3df10765d4" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":104448905,"asset_id":104822617,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/104448905/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="104822617"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="104822617"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 104822617; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=104822617]").text(description); $(".js-view-count[data-work-id=104822617]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 104822617; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='104822617']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "66482b4faacf5cee73d08c3df10765d4" } } $('.js-work-strip[data-work-id=104822617]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":104822617,"title":"Latching dynamics as a basis for short-term recall","translated_title":"","metadata":{"abstract":"We discuss simple models for the transient storage in short-term memory of cortical patterns of activity, all based on the notion that their recall exploits the natural tendency of the cortex to hop from state to state—latching dynamics. 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The phenomenon of replay, in the hippocampus of mammals, offers a remarkable example of this temporal dynamics. However, most quantitative models of memory treat memories as static configurations, neglecting the temporal unfolding of the retrieval process. Here, we introduce a continuous attractor network model with a memory-dependent asymmetric component in the synaptic connectivity, which spontaneously breaks the equilibrium of the memory configurations and produces dynamic retrieval. The detailed analysis of the model with analytical calculations and numerical simulations shows that it can robustly retrieve multiple dynamical memories, and that this feature is largely independent of the details of its implementation. By calculating the storage capacity, we show that the dynamic component does not impair memory capacity, and can even enhance it...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="0050eef2b9d1c481cafecbef88686aa8" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":104448890,"asset_id":104822616,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/104448890/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="104822616"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="104822616"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 104822616; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=104822616]").text(description); $(".js-view-count[data-work-id=104822616]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 104822616; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='104822616']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "0050eef2b9d1c481cafecbef88686aa8" } } $('.js-work-strip[data-work-id=104822616]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":104822616,"title":"Continuous attractors for dynamic memories","translated_title":"","metadata":{"abstract":"Episodic memory has a dynamic nature: when we recall past episodes, we retrieve not only their content, but also their temporal structure. 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By calculating the storage capacity, we show that the dynamic component does not impair memory capacity, and can even enhance it...","internal_url":"https://www.academia.edu/104822616/Continuous_attractors_for_dynamic_memories","translated_internal_url":"","created_at":"2023-07-22T09:22:55.359-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":32288545,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[{"id":104448890,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/104448890/thumbnails/1.jpg","file_name":"elife-69499-v1.pdf","download_url":"https://www.academia.edu/attachments/104448890/download_file","bulk_download_file_name":"Continuous_attractors_for_dynamic_memori.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/104448890/elife-69499-v1-libre.pdf?1690046137=\u0026response-content-disposition=attachment%3B+filename%3DContinuous_attractors_for_dynamic_memori.pdf\u0026Expires=1743730652\u0026Signature=hHk650qgiNEM0u1gRnYEipJeuM1EUSNoH6ujUlfSC7RjWnvgkflG9R1CLBNLz5urSBps4ulsWzIhtJPlWSj6zQRoviJ-YZ~gn~LLTBoYcWVenY5bOz-wq7bh01CSJQzV8LYwrVSYEi~CevmklL1yV4m2WeoMEJFYUsk6nVGRy0XzM38ColBB8QneIdLVVTtnh~JmZdXmqxlRpio-eTXnOaZX9acnzVZHeq1tT6aE-JYPob2oU6H58KnjDgnGAKNr0vhiEwHKP~O5Yyq234S7-2IwZs7rB~UVREK01tD9sAGo~9NnSpvS-eco5ULmihUV0yMhX9ivi3W1yaknLBU8VQ__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Continuous_attractors_for_dynamic_memories","translated_slug":"","page_count":28,"language":"en","content_type":"Work","summary":"Episodic memory has a dynamic nature: when we recall past episodes, we retrieve not only their content, but also their temporal structure. The phenomenon of replay, in the hippocampus of mammals, offers a remarkable example of this temporal dynamics. However, most quantitative models of memory treat memories as static configurations, neglecting the temporal unfolding of the retrieval process. Here, we introduce a continuous attractor network model with a memory-dependent asymmetric component in the synaptic connectivity, which spontaneously breaks the equilibrium of the memory configurations and produces dynamic retrieval. The detailed analysis of the model with analytical calculations and numerical simulations shows that it can robustly retrieve multiple dynamical memories, and that this feature is largely independent of the details of its implementation. By calculating the storage capacity, we show that the dynamic component does not impair memory capacity, and can even enhance it...","owner":{"id":32288545,"first_name":"Alessandro","middle_initials":null,"last_name":"Treves","page_name":"ATreves","domain_name":"sissa","created_at":"2015-06-17T08:51:16.709-07:00","display_name":"Alessandro Treves","url":"https://sissa.academia.edu/ATreves"},"attachments":[{"id":104448890,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/104448890/thumbnails/1.jpg","file_name":"elife-69499-v1.pdf","download_url":"https://www.academia.edu/attachments/104448890/download_file","bulk_download_file_name":"Continuous_attractors_for_dynamic_memori.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/104448890/elife-69499-v1-libre.pdf?1690046137=\u0026response-content-disposition=attachment%3B+filename%3DContinuous_attractors_for_dynamic_memori.pdf\u0026Expires=1743730652\u0026Signature=hHk650qgiNEM0u1gRnYEipJeuM1EUSNoH6ujUlfSC7RjWnvgkflG9R1CLBNLz5urSBps4ulsWzIhtJPlWSj6zQRoviJ-YZ~gn~LLTBoYcWVenY5bOz-wq7bh01CSJQzV8LYwrVSYEi~CevmklL1yV4m2WeoMEJFYUsk6nVGRy0XzM38ColBB8QneIdLVVTtnh~JmZdXmqxlRpio-eTXnOaZX9acnzVZHeq1tT6aE-JYPob2oU6H58KnjDgnGAKNr0vhiEwHKP~O5Yyq234S7-2IwZs7rB~UVREK01tD9sAGo~9NnSpvS-eco5ULmihUV0yMhX9ivi3W1yaknLBU8VQ__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"},{"id":104448889,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/104448889/thumbnails/1.jpg","file_name":"elife-69499-v1.pdf","download_url":"https://www.academia.edu/attachments/104448889/download_file","bulk_download_file_name":"Continuous_attractors_for_dynamic_memori.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/104448889/elife-69499-v1-libre.pdf?1690046152=\u0026response-content-disposition=attachment%3B+filename%3DContinuous_attractors_for_dynamic_memori.pdf\u0026Expires=1743730652\u0026Signature=YMnN3fl4jvlCa-QcCh4hBAHgqREbvDWZvNVGzHfM~v-AgT-CkRnFkrgb8Z7F4HS7TdyoaV8Fkl3m9mmOsf-EHYzBEQkovrzkh-9GVPvjFKEVnHnn5h~lkvyUG4mZ-SeP~1PBaFbfQO1DPOCHD6dVG~gCjJd4OA3TFOFS2aj1QzdFJL4oaPalnEglFJ6o4QaDcgIOdZGYqIZuCBMkfZdCNcFhm2SSM1QZ~nz2qWIpnkpIVlfm09K-y0Q-6kebncag3Zxn36xwMSF8aYCIT8Ef53K9~DNzi4-zAFTVjdaW9WvU5rec7EAVzDhXWo1zRRrJ6ds8hM4oHY9jVq6be5w7yA__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science"},{"id":7710,"name":"Biology","url":"https://www.academia.edu/Documents/in/Biology"},{"id":26327,"name":"Medicine","url":"https://www.academia.edu/Documents/in/Medicine"},{"id":32361,"name":"Episodic Memory","url":"https://www.academia.edu/Documents/in/Episodic_Memory"},{"id":440689,"name":"Recall","url":"https://www.academia.edu/Documents/in/Recall"},{"id":1325912,"name":"Elife","url":"https://www.academia.edu/Documents/in/Elife"},{"id":1881397,"name":"Dynamic Random Access Memory","url":"https://www.academia.edu/Documents/in/Dynamic_Random_Access_Memory"},{"id":3167069,"name":"attractor","url":"https://www.academia.edu/Documents/in/attractor"}],"urls":[{"id":33020597,"url":"https://cdn.elifesciences.org/articles/69499/elife-69499-v1.pdf"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") if (false) { Aedu.setUpFigureCarousel('profile-work-104822616-figures'); } }); </script> <div class="js-work-strip profile--work_container" data-work-id="104822615"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/104822615/Efficiency_of_local_learning_rules_in_threshold_linear_associative_networks"><img alt="Research paper thumbnail of Efficiency of local learning rules in threshold-linear associative networks" class="work-thumbnail" src="https://attachments.academia-assets.com/104448903/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/104822615/Efficiency_of_local_learning_rules_in_threshold_linear_associative_networks">Efficiency of local learning rules in threshold-linear associative networks</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">We show that associative networks of threshold linear units endowed with Hebbian learning can ope...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">We show that associative networks of threshold linear units endowed with Hebbian learning can operate closer to the Gardner optimal storage capacity than their binary counterparts and even surpass this bound. This is largely achieved through a sparsification of the retrieved patterns, which we analyze for theoretical and empirical distributions of activity. As reaching the optimal capacity via non-local learning rules like back-propagation requires slow and neurally implausible training procedures, our results indicate that one-shot self-organized Hebbian learning can be just as efficient.</span></div><div class="wp-workCard_item"><div class="carousel-container carousel-container--sm" id="profile-work-104822615-figures"><div class="prev-slide-container js-prev-button-container"><button aria-label="Previous" class="carousel-navigation-button js-profile-work-104822615-figures-prev"><span class="material-symbols-outlined" style="font-size: 24px" translate="no">arrow_back_ios</span></button></div><div class="slides-container js-slides-container"><figure class="figure-slide-container"><a href="https://www.academia.edu/figures/25310706/figure-1-dependence-of-the-gardner-capacity-on-different"><img alt="FIG. 1. Dependence of the Gardner capacity a. on different parameters. a. plotted in (a) as a function of g and f (di = 1.1, dz = 2), in (b) as a function of a = dj/de for different values of f (g = 10,d1 = 1.1) in (c) and (d) as a function of di and dz for g = 0.2 and g = 10, respectively (f = 0.5). Note that fixing f, restricts the available range of a, as a cannot be larger than f; the inaccessible ranges of f values are shadowed in (b), (c) and (d). " class="figure-slide-image" src="https://figures.academia-assets.com/104448903/figure_001.jpg" /></a></figure><figure class="figure-slide-container"><a href="https://www.academia.edu/figures/25310707/figure-2-hebbian-capacity-vs-gardner-bound-as-function-of"><img alt="FIG. 2. Hebbian capacity vs Gardner bound. (a) a¥ as a function of f for different sample distribution of stored patterns compared to the universal a bound for errorless retrieval, i.e. the g— co limit of Eq (3); the red diamonds are reached with explicit TL perceptron training. (b) the sparsification of the stored patterns at retrieval, for Hebbian networks loaded at their capacity. " class="figure-slide-image" src="https://figures.academia-assets.com/104448903/figure_002.jpg" /></a></figure><figure class="figure-slide-container"><a href="https://www.academia.edu/figures/25310708/figure-3-hebbian-learning-vs-the-gardner-errorless-bound-for"><img alt="FIG. 3. Hebbian learning vs. the Gardner errorless bound for experimental data. (a,b) Examples of the histograms of two experimentally recorded spike counts (blue) and the retrieved distribution, if the patterns were stored using Hebbian learning (orange). Note that the retrieved distributions a la Gardner would be the same as the stored patterns. (c) Analytically calculated universal Gardner capacity a¢(f) (blue), ie. the g—> oo limit of Eq (3), compared to ae"? for the Hebbian learning of an exponential distribution (orange). The diamonds are the values al!naive achieved with the 9 original discrete distributions, and the circles the values at “*P for those 4 that can be fit to an exponential distribution. The asterisk marks the two cells whose distribution is plotted in a) and b). d) Sparsification of the retrieved patterns, for Hebbian learning. empirical distributions achieve a lower capacity than that of their exponential fit, which leads to further sparsificatior at retrieval. This is illustrated in Fig. 3d, which shows the ratio of the sparsity of patterns retrieved after Hebbiar storage to that of the originally stored pattern, vs. f. " class="figure-slide-image" src="https://figures.academia-assets.com/104448903/figure_003.jpg" /></a></figure><figure class="figure-slide-container"><a href="https://www.academia.edu/figures/25310709/figure-4-in-order-to-compute-the-average-of-the-delta"><img alt="In order to compute the average of the delta functions in Eq.(7), we use the approximation " class="figure-slide-image" src="https://figures.academia-assets.com/104448903/figure_004.jpg" /></a></figure><figure class="figure-slide-container"><a href="https://www.academia.edu/figures/25310714/figure-5-where-in-the-last-passage-we-made-simple-change-of"><img alt="where in the last passage we made a simple change of variables. Therefore we can rewrite Eq. (30) as where G = G(q, G,m, m, E) given by Eq. (21), and set them to zero to find the maximum of Eq. (21), with W(, g, E) given by Eq. (26) and M(q,m) given by Eq. (36). With the first three derivatives equalized to zero, which are applied only to the second and third term of Eq. (21), and assuming Cq >> m? and |C(1 — 2q)| >> m? as C > ov, we obtain the relations " class="figure-slide-image" src="https://figures.academia-assets.com/104448903/figure_005.jpg" /></a></figure><figure class="figure-slide-container"><a href="https://www.academia.edu/figures/25310717/figure-6-substituting-in-to-the-leading-order-leads-to-eq"><img alt="Substituting x in a, to the leading order leads to Eq.(5) presented in the main text. We now proceed to evaluating a,, we apply the same Taylor expansion as before For the purpose of assessing whether the Gardner capacity for errorless retrieval can be reached with explicit training, we can decompose a network of, say, N + 1 = 10001 units into N + 1 independent threshold linear perceptrons. A threshold linear perceptron is just a 1-layer feedforward neural network with N inputs and one output, the activity of which is given by a threshold-linear activation function. " class="figure-slide-image" src="https://figures.academia-assets.com/104448903/figure_006.jpg" /></a></figure><figure class="figure-slide-container"><a href="https://www.academia.edu/figures/25310743/figure-1-suplementary-to-comparison-between-the-hebbian-and"><img alt="FIG. 1. Suplementary to Fig. (2). Comparison between the Hebbian and Gardner storage capacity for 3 discrete distributions The upper row considers as sparsity parameter the one of the input pattern, the lower row the one of the retrieved pattern The Garner capacity is that given by Eq. (3) of the main text As a supplement to Fig. 2 of the main text, reproduced here in the 3 separate panels in the upper row in Fig. 1, we show a comparison between the Hebbian capacity and the Gardner one when plotted as a function of the output sparsity (in the bottom row of Fig. 1). The Gardner storage capacity is now in each of these 3 cases above the Hebbian capacity, taken as a function of the output sparsity instead of the input one. One can see that all distributions are such that (7) = Ee dnP(n)n = a and ( =f, dnP(n)n? = a, so that a coin- cides with the sparsity (7)?/(n?) of the network. The fraction of active unite i is Sn related to a as f = a,9a/5,9a/4, 2a respectively. " class="figure-slide-image" src="https://figures.academia-assets.com/104448903/figure_007.jpg" /></a></figure><figure class="figure-slide-container"><a href="https://www.academia.edu/figures/25310746/figure-3-comparison-between-the-values-of-the-storage"><img alt="FIG. 3. Comparison between the values of the storage capacity a la Gardner and Hebbian, for the 9 empirical distributions extracted from [3]. " class="figure-slide-image" src="https://figures.academia-assets.com/104448903/figure_008.jpg" /></a></figure><figure class="figure-slide-container"><a href="https://www.academia.edu/figures/25310748/table-1-therefore-integrating-over-in-eq-leads-to"><img alt="Therefore, integrating over J in Eq. (24), leads to: " class="figure-slide-image" src="https://figures.academia-assets.com/104448903/table_001.jpg" /></a></figure><figure class="figure-slide-container"><a href="https://www.academia.edu/figures/25310754/figure-2-in-the-real-activity-distributions-we-use-each"><img alt="In the real activity distributions we use, each neuron emits, in time bins of fixed duration (we use 100msec), 0,...,7,---,;Nmax Spikes, with relative frequency c,, such that mee Cn = 1. These values are taken from Fig. 2 of [3] and correspond to the histograms in blue in Fig.2 below (and in Fig.3 of the main text); they are assumed to be the distributions of the patterns to be stored. If the weights are those described by the Gardner calculation, these patterns can be retrieved as they are, and their distribution remains the same. If they are stored with Hebbian weights close to the maximal Hebbian capacity, however, the retrieved distributions look different, and they can be derived as follows. 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This is largely achieved through a sparsification of the retrieved patterns, which we analyze for theoretical and empirical distributions of activity. As reaching the optimal capacity via non-local learning rules like back-propagation requires slow and neurally implausible training procedures, our results indicate that one-shot self-organized Hebbian learning can be just as efficient.","publisher":"Cold Spring Harbor Laboratory","ai_title_tag":"Local Hebbian Learning in Efficient Associative Networks","publication_date":{"day":null,"month":null,"year":2020,"errors":{}}},"translated_abstract":"We show that associative networks of threshold linear units endowed with Hebbian learning can operate closer to the Gardner optimal storage capacity than their binary counterparts and even surpass this bound. This is largely achieved through a sparsification of the retrieved patterns, which we analyze for theoretical and empirical distributions of activity. As reaching the optimal capacity via non-local learning rules like back-propagation requires slow and neurally implausible training procedures, our results indicate that one-shot self-organized Hebbian learning can be just as efficient.","internal_url":"https://www.academia.edu/104822615/Efficiency_of_local_learning_rules_in_threshold_linear_associative_networks","translated_internal_url":"","created_at":"2023-07-22T09:22:55.070-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":32288545,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[{"id":104448903,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/104448903/thumbnails/1.jpg","file_name":"2007.12584v1.pdf","download_url":"https://www.academia.edu/attachments/104448903/download_file","bulk_download_file_name":"Efficiency_of_local_learning_rules_in_th.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/104448903/2007.12584v1-libre.pdf?1690046122=\u0026response-content-disposition=attachment%3B+filename%3DEfficiency_of_local_learning_rules_in_th.pdf\u0026Expires=1743730652\u0026Signature=XSibSvdZhs97MmLhy~CYYzmr~Dr-fpXLWhALiPx7D4mRlEluT1NOcHDjwQ8BLzcgnjSO7qbOUuZsqErKQuWbOZjImk0u3SmjYpqj-uCSG7sCYPfPoHffktOsWmy2VH0i6jM6Ab9bExiGja-5EXKqxCdLCDuN~rd1uEvIPvspRrFEY1-UPhXLaRQ13lRafCQz0sexifOWSq1uPm0mza4x350PUAolXm~xfnAlzjRHifyO6p7Gvc4eakMtIOGWpeDdbt4jw-afiQ5yG1cy-tLmWOcsVZU63CsDKvQOQm2U4w~VWOK9hVq5CPucrxWhPtzUIHw0oNtPi34IfQfvyeKj6Q__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Efficiency_of_local_learning_rules_in_threshold_linear_associative_networks","translated_slug":"","page_count":19,"language":"en","content_type":"Work","summary":"We show that associative networks of threshold linear units endowed with Hebbian learning can operate closer to the Gardner optimal storage capacity than their binary counterparts and even surpass this bound. This is largely achieved through a sparsification of the retrieved patterns, which we analyze for theoretical and empirical distributions of activity. As reaching the optimal capacity via non-local learning rules like back-propagation requires slow and neurally implausible training procedures, our results indicate that one-shot self-organized Hebbian learning can be just as efficient.","owner":{"id":32288545,"first_name":"Alessandro","middle_initials":null,"last_name":"Treves","page_name":"ATreves","domain_name":"sissa","created_at":"2015-06-17T08:51:16.709-07:00","display_name":"Alessandro Treves","url":"https://sissa.academia.edu/ATreves"},"attachments":[{"id":104448903,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/104448903/thumbnails/1.jpg","file_name":"2007.12584v1.pdf","download_url":"https://www.academia.edu/attachments/104448903/download_file","bulk_download_file_name":"Efficiency_of_local_learning_rules_in_th.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/104448903/2007.12584v1-libre.pdf?1690046122=\u0026response-content-disposition=attachment%3B+filename%3DEfficiency_of_local_learning_rules_in_th.pdf\u0026Expires=1743730652\u0026Signature=XSibSvdZhs97MmLhy~CYYzmr~Dr-fpXLWhALiPx7D4mRlEluT1NOcHDjwQ8BLzcgnjSO7qbOUuZsqErKQuWbOZjImk0u3SmjYpqj-uCSG7sCYPfPoHffktOsWmy2VH0i6jM6Ab9bExiGja-5EXKqxCdLCDuN~rd1uEvIPvspRrFEY1-UPhXLaRQ13lRafCQz0sexifOWSq1uPm0mza4x350PUAolXm~xfnAlzjRHifyO6p7Gvc4eakMtIOGWpeDdbt4jw-afiQ5yG1cy-tLmWOcsVZU63CsDKvQOQm2U4w~VWOK9hVq5CPucrxWhPtzUIHw0oNtPi34IfQfvyeKj6Q__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science"},{"id":498,"name":"Physics","url":"https://www.academia.edu/Documents/in/Physics"},{"id":7710,"name":"Biology","url":"https://www.academia.edu/Documents/in/Biology"},{"id":26327,"name":"Medicine","url":"https://www.academia.edu/Documents/in/Medicine"},{"id":51073,"name":"Recurrent Neural Network","url":"https://www.academia.edu/Documents/in/Recurrent_Neural_Network"}],"urls":[{"id":33020596,"url":"https://syndication.highwire.org/content/doi/10.1101/2020.07.28.225318"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") if (true) { Aedu.setUpFigureCarousel('profile-work-104822615-figures'); } }); </script> <div class="js-work-strip profile--work_container" data-work-id="104822614"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/104822614/Selforganization_of_modular_activity_of_grid_cells"><img alt="Research paper thumbnail of Selforganization of modular activity of grid cells" class="work-thumbnail" src="https://attachments.academia-assets.com/104448913/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/104822614/Selforganization_of_modular_activity_of_grid_cells">Selforganization of modular activity of grid cells</a></div><div class="wp-workCard_item"><span>Hippocampus</span><span>, 2017</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">A unique topographical representation of space is found in the concerted activity of grid cells i...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">A unique topographical representation of space is found in the concerted activity of grid cells in the rodent medial entorhinal cortex. Many among the principal cells in this region exhibit a hexagonal firing pattern, in which each cell expresses its own set of place fields (spatial phases) at the vertices of a triangular grid, the spacing and orientation of which are typically shared with neighboring cells. Grid spacing, in particular, has been found to increase along the dorso-ventral axis of the entorhinal cortex but in discrete steps, that is, with a modular structure. In this study, we show that such a modular activity may result from the self-organization of interacting units, which individually would not show discrete but rather continuously varying grid spacing. Within our "adaptation" network model, the effect of a continuously varying time constant, which determines grid spacing in the isolated cell model, is modulated by recurrent collateral connections, which tend to produce a few subnetworks, akin to magnetic domains, each with its own grid spacing. In agreement with experimental evidence, the modular structure is tightly defined by grid spacing, but also involves grid orientation and distortion, due to interactions across modules. Thus, our study sheds light onto a possible mechanism, other than simply assuming separate networks a priori, underlying the formation of modular grid representations.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="596e868f448dfea3c7827c93cc12294f" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":104448913,"asset_id":104822614,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/104448913/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="104822614"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="104822614"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 104822614; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=104822614]").text(description); $(".js-view-count[data-work-id=104822614]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 104822614; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='104822614']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "596e868f448dfea3c7827c93cc12294f" } } $('.js-work-strip[data-work-id=104822614]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":104822614,"title":"Selforganization of modular activity of grid cells","translated_title":"","metadata":{"publisher":"Wiley","ai_title_tag":"Self-Organization of Modular Grid Cell Activity","grobid_abstract":"A unique topographical representation of space is found in the concerted activity of grid cells in the rodent medial entorhinal cortex. 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In agreement with experimental evidence, the modular structure is tightly defined by grid spacing, but also involves grid orientation and distortion, due to interactions across modules. 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Many among the principal cells in this region exhibit a hexagonal firing pattern, in which each cell expresses its own set of place fields (spatial phases) at the vertices of a triangular grid, the spacing and orientation of which are typically shared with neighboring cells. Grid spacing, in particular, has been found to increase along the dorso-ventral axis of the entorhinal cortex but in discrete steps, that is, with a modular structure. In this study, we show that such a modular activity may result from the self-organization of interacting units, which individually would not show discrete but rather continuously varying grid spacing. Within our \"adaptation\" network model, the effect of a continuously varying time constant, which determines grid spacing in the isolated cell model, is modulated by recurrent collateral connections, which tend to produce a few subnetworks, akin to magnetic domains, each with its own grid spacing. In agreement with experimental evidence, the modular structure is tightly defined by grid spacing, but also involves grid orientation and distortion, due to interactions across modules. 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") if (false) { Aedu.setUpFigureCarousel('profile-work-104822614-figures'); } }); </script> <div class="js-work-strip profile--work_container" data-work-id="104822613"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/104822613/Autoassociation_memory"><img alt="Research paper thumbnail of Autoassociation memory" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title">Autoassociation memory</div><div class="wp-workCard_item"><span>Neural Networks and Brain Function</span><span>, 1997</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="104822613"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="104822613"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 104822613; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=104822613]").text(description); $(".js-view-count[data-work-id=104822613]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 104822613; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='104822613']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=104822613]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":104822613,"title":"Autoassociation memory","translated_title":"","metadata":{"publisher":"Oxford University Press","publication_date":{"day":null,"month":null,"year":1997,"errors":{}},"publication_name":"Neural Networks and Brain Function"},"translated_abstract":null,"internal_url":"https://www.academia.edu/104822613/Autoassociation_memory","translated_internal_url":"","created_at":"2023-07-22T09:22:54.570-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":32288545,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[],"slug":"Autoassociation_memory","translated_slug":"","page_count":null,"language":"en","content_type":"Work","summary":null,"owner":{"id":32288545,"first_name":"Alessandro","middle_initials":null,"last_name":"Treves","page_name":"ATreves","domain_name":"sissa","created_at":"2015-06-17T08:51:16.709-07:00","display_name":"Alessandro Treves","url":"https://sissa.academia.edu/ATreves"},"attachments":[],"research_interests":[{"id":237,"name":"Cognitive Science","url":"https://www.academia.edu/Documents/in/Cognitive_Science"},{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science"},{"id":1211304,"name":"Artificial Neural Network","url":"https://www.academia.edu/Documents/in/Artificial_Neural_Network"},{"id":3167069,"name":"attractor","url":"https://www.academia.edu/Documents/in/attractor"}],"urls":[]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") if (false) { Aedu.setUpFigureCarousel('profile-work-104822613-figures'); } }); </script> <div class="js-work-strip profile--work_container" data-work-id="104822612"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/104822612/Disappearance_of_spurious_states_in_analog_associative_memories"><img alt="Research paper thumbnail of Disappearance of spurious states in analog associative memories" class="work-thumbnail" src="https://attachments.academia-assets.com/104448916/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/104822612/Disappearance_of_spurious_states_in_analog_associative_memories">Disappearance of spurious states in analog associative memories</a></div><div class="wp-workCard_item"><span>Physical review. E, Statistical, nonlinear, and soft matter physics</span><span>, 2003</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">We show that symmetric n-mixture states, when they exist, are almost never stable in autoassociat...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">We show that symmetric n-mixture states, when they exist, are almost never stable in autoassociative networks with threshold-linear units. Only with a binary coding scheme, we could find a limited region of the parameter space in which either 2-mixture or 3-mixture states are stable attractors of the dynamics.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="08cc8225833ab53fb46b67a34e2cf2e9" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":104448916,"asset_id":104822612,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/104448916/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="104822612"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="104822612"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 104822612; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=104822612]").text(description); $(".js-view-count[data-work-id=104822612]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 104822612; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='104822612']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "08cc8225833ab53fb46b67a34e2cf2e9" } } $('.js-work-strip[data-work-id=104822612]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":104822612,"title":"Disappearance of spurious states in analog associative memories","translated_title":"","metadata":{"abstract":"We show that symmetric n-mixture states, when they exist, are almost never stable in autoassociative networks with threshold-linear units. 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") if (false) { Aedu.setUpFigureCarousel('profile-work-104822612-figures'); } }); </script> <div class="js-work-strip profile--work_container" data-work-id="104822611"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/104822611/After_effects_in_the_Perception_of_Emotion_Following_Brief_Masked_Adaptor_Faces"><img alt="Research paper thumbnail of After effects in the Perception of Emotion Following Brief, Masked Adaptor Faces" class="work-thumbnail" src="https://attachments.academia-assets.com/104448915/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/104822611/After_effects_in_the_Perception_of_Emotion_Following_Brief_Masked_Adaptor_Faces">After effects in the Perception of Emotion Following Brief, Masked Adaptor Faces</a></div><div class="wp-workCard_item"><span>The Open Behavioral Science Journal</span><span>, 2008</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Adaptation aftereffects are the tendency to perceive an ambiguous target stimulus, which follows ...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Adaptation aftereffects are the tendency to perceive an ambiguous target stimulus, which follows an adaptor stimulus, as different from the adaptor. A duration dependence of face adaptation aftereffects has been demonstrated for durations of at least 500ms, for identity related judgments. Here we describe the duration dependence of the adaptation aftereffects of very brief (11.7ms-500ms) backwardly masked faces, on both expression and identity category judgments of ambiguous target faces. We find significant aftereffects at minimum duration 23.5ms for emotional expression, and 47ms for identity, but these are abolished by backward masking with an inverted face, although these same adaptors can be correctly categorized above chance. The presence of a short duration adaptation effect in expression might be mediated by rapid transfer of low spatial frequency (LSF) information. We tested this possibility by comparing aftereffects in low pass and high pass filtered ambiguous targets, and found no evidence of independent adaptation of a LSF specific channel.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="22d298caca432b3b9838d8ee2e38e9da" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":104448915,"asset_id":104822611,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/104448915/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="104822611"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="104822611"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 104822611; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=104822611]").text(description); $(".js-view-count[data-work-id=104822611]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 104822611; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='104822611']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "22d298caca432b3b9838d8ee2e38e9da" } } $('.js-work-strip[data-work-id=104822611]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":104822611,"title":"After effects in the Perception of Emotion Following Brief, Masked Adaptor Faces","translated_title":"","metadata":{"publisher":"Bentham Science Publishers Ltd.","ai_title_tag":"Duration-Dependent Adaptation Aftereffects of Masked Faces","grobid_abstract":"Adaptation aftereffects are the tendency to perceive an ambiguous target stimulus, which follows an adaptor stimulus, as different from the adaptor. A duration dependence of face adaptation aftereffects has been demonstrated for durations of at least 500ms, for identity related judgments. Here we describe the duration dependence of the adaptation aftereffects of very brief (11.7ms-500ms) backwardly masked faces, on both expression and identity category judgments of ambiguous target faces. We find significant aftereffects at minimum duration 23.5ms for emotional expression, and 47ms for identity, but these are abolished by backward masking with an inverted face, although these same adaptors can be correctly categorized above chance. The presence of a short duration adaptation effect in expression might be mediated by rapid transfer of low spatial frequency (LSF) information. We tested this possibility by comparing aftereffects in low pass and high pass filtered ambiguous targets, and found no evidence of independent adaptation of a LSF specific channel.","publication_date":{"day":null,"month":null,"year":2008,"errors":{}},"publication_name":"The Open Behavioral Science Journal","grobid_abstract_attachment_id":104448915},"translated_abstract":null,"internal_url":"https://www.academia.edu/104822611/After_effects_in_the_Perception_of_Emotion_Following_Brief_Masked_Adaptor_Faces","translated_internal_url":"","created_at":"2023-07-22T09:22:54.172-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":32288545,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[{"id":104448915,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/104448915/thumbnails/1.jpg","file_name":"van_08.pdf","download_url":"https://www.academia.edu/attachments/104448915/download_file","bulk_download_file_name":"After_effects_in_the_Perception_of_Emoti.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/104448915/van_08-libre.pdf?1690046100=\u0026response-content-disposition=attachment%3B+filename%3DAfter_effects_in_the_Perception_of_Emoti.pdf\u0026Expires=1743730653\u0026Signature=PJxlWVJU9vP7vDhXrfNdcVnOSNl0CxRawGSSIA3DXuca99UdnAeigX4t7GHAIJT3Zhwd2bEISGuhjLnh1LhikG53PnOdbyvd9Nl0L2T2iYmcmxDNSweMvt3-ArQ0DsB~osGTw9Ulkwa34DJDY2qsOyzZa-jOa1Pw7ppw5l2F2pSW4tUckjbbMvAeunJwTivjV2RDh8K3a49DBku9qaaUZ3UTxn00-dyCmhjwJuUkFe4YhDeGTr3wLpl9baYff9fvKTKRM9C4wGXsKkrkTCKQxnSUUv04sptC5n1TWQb5JQGjGBctbM38txoxyHl-GqDCUUU-NQ~LOe2zFI5w61QvAA__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"After_effects_in_the_Perception_of_Emotion_Following_Brief_Masked_Adaptor_Faces","translated_slug":"","page_count":17,"language":"en","content_type":"Work","summary":"Adaptation aftereffects are the tendency to perceive an ambiguous target stimulus, which follows an adaptor stimulus, as different from the adaptor. A duration dependence of face adaptation aftereffects has been demonstrated for durations of at least 500ms, for identity related judgments. Here we describe the duration dependence of the adaptation aftereffects of very brief (11.7ms-500ms) backwardly masked faces, on both expression and identity category judgments of ambiguous target faces. We find significant aftereffects at minimum duration 23.5ms for emotional expression, and 47ms for identity, but these are abolished by backward masking with an inverted face, although these same adaptors can be correctly categorized above chance. The presence of a short duration adaptation effect in expression might be mediated by rapid transfer of low spatial frequency (LSF) information. We tested this possibility by comparing aftereffects in low pass and high pass filtered ambiguous targets, and found no evidence of independent adaptation of a LSF specific channel.","owner":{"id":32288545,"first_name":"Alessandro","middle_initials":null,"last_name":"Treves","page_name":"ATreves","domain_name":"sissa","created_at":"2015-06-17T08:51:16.709-07:00","display_name":"Alessandro Treves","url":"https://sissa.academia.edu/ATreves"},"attachments":[{"id":104448915,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/104448915/thumbnails/1.jpg","file_name":"van_08.pdf","download_url":"https://www.academia.edu/attachments/104448915/download_file","bulk_download_file_name":"After_effects_in_the_Perception_of_Emoti.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/104448915/van_08-libre.pdf?1690046100=\u0026response-content-disposition=attachment%3B+filename%3DAfter_effects_in_the_Perception_of_Emoti.pdf\u0026Expires=1743730653\u0026Signature=PJxlWVJU9vP7vDhXrfNdcVnOSNl0CxRawGSSIA3DXuca99UdnAeigX4t7GHAIJT3Zhwd2bEISGuhjLnh1LhikG53PnOdbyvd9Nl0L2T2iYmcmxDNSweMvt3-ArQ0DsB~osGTw9Ulkwa34DJDY2qsOyzZa-jOa1Pw7ppw5l2F2pSW4tUckjbbMvAeunJwTivjV2RDh8K3a49DBku9qaaUZ3UTxn00-dyCmhjwJuUkFe4YhDeGTr3wLpl9baYff9fvKTKRM9C4wGXsKkrkTCKQxnSUUv04sptC5n1TWQb5JQGjGBctbM38txoxyHl-GqDCUUU-NQ~LOe2zFI5w61QvAA__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":221,"name":"Psychology","url":"https://www.academia.edu/Documents/in/Psychology"},{"id":867,"name":"Perception","url":"https://www.academia.edu/Documents/in/Perception"},{"id":2229,"name":"Vision Science","url":"https://www.academia.edu/Documents/in/Vision_Science"},{"id":5359,"name":"Visual perception","url":"https://www.academia.edu/Documents/in/Visual_perception"},{"id":56636,"name":"Masking","url":"https://www.academia.edu/Documents/in/Masking"},{"id":56639,"name":"Visual Masking","url":"https://www.academia.edu/Documents/in/Visual_Masking"},{"id":303594,"name":"Emotional Expression","url":"https://www.academia.edu/Documents/in/Emotional_Expression"},{"id":614273,"name":"Backward Masking","url":"https://www.academia.edu/Documents/in/Backward_Masking"},{"id":4019930,"name":"duration dependence","url":"https://www.academia.edu/Documents/in/duration_dependence"}],"urls":[]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") if (false) { Aedu.setUpFigureCarousel('profile-work-104822611-figures'); } }); </script> <div class="js-work-strip profile--work_container" data-work-id="104822610"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/104822610/How_much_do_they_tell_us_to_move"><img alt="Research paper thumbnail of How much do they tell us to move?" class="work-thumbnail" src="https://attachments.academia-assets.com/104448904/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/104822610/How_much_do_they_tell_us_to_move">How much do they tell us to move?</a></div><div class="wp-workCard_item"><span>Neurocomputing</span><span>, 2001</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">A previous study revealed that neuronal activity in primary motor cortex (MI) and supplementary m...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">A previous study revealed that neuronal activity in primary motor cortex (MI) and supplementary motor area (SMA) of the monkey depends both on which arm(s) moved and on the direction of movement. At the level of single cells, no di!erences were found between the areas in the information conveyed about each correlate. We constructed pseudosimultaneous response vectors and applied a decoding algorithm to quantify di!erences at a population level. We found that, on average, samples of 20 MI units carried less information about both movement type and direction than SMA units in a time window of 500 ms across the movement onset; a more detailed temporal analysis has revealed that SMA precedes M1 in motor planning and execution and that along the trial M1 cells carry as much information about direction as SMA cells.</span></div><div class="wp-workCard_item"><div class="carousel-container carousel-container--sm" id="profile-work-104822610-figures"><div class="prev-slide-container js-prev-button-container"><button aria-label="Previous" class="carousel-navigation-button js-profile-work-104822610-figures-prev"><span class="material-symbols-outlined" style="font-size: 24px" translate="no">arrow_back_ios</span></button></div><div class="slides-container js-slides-container"><figure class="figure-slide-container"><a href="https://www.academia.edu/figures/16587902/figure-1-information-about-dir-vs-type-in-and-sma-single"><img alt="Fig. 1. Information about dir vs type in M1 and SMA single cells. V. Del Prete et al. / Neurocomputing 38-40 (2001) 1181-1184 32 different correlates of the neural activity. Eighty-seven and 103 cells were recorded respectively, in the right M1 and in the right SMA areas. The activity was quantified by the number of spikes emitted in a given time window along the trial, and expressed as the firing rate rf of unit i in trial k. Trials (10-20) were typically recorded for each cell and each correlate. Experimental procedures are described in detail elsewhere [1]. First, we evaluated the mutual information J(s,r) at a single neuron level, to extract the amount of information each cell carried, separately, about either movement type or direction. " class="figure-slide-image" src="https://figures.academia-assets.com/104448904/figure_001.jpg" /></a></figure><figure class="figure-slide-container"><a href="https://www.academia.edu/figures/16587918/figure-2-info-in-and-sma-right-cells-for-time-window-of-ms"><img alt="Fig. 2. Info in M1 and SMA right cells for a time window of 500 ms (on the left) and for different time windows (on the right, curve for 20 cells). " class="figure-slide-image" src="https://figures.academia-assets.com/104448904/figure_002.jpg" /></a></figure></div><div class="next-slide-container js-next-button-container"><button aria-label="Next" class="carousel-navigation-button js-profile-work-104822610-figures-next"><span class="material-symbols-outlined" style="font-size: 24px" translate="no">arrow_forward_ios</span></button></div></div></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="475012ce591b56081fd31e20cc50ac95" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":104448904,"asset_id":104822610,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/104448904/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="104822610"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="104822610"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 104822610; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=104822610]").text(description); $(".js-view-count[data-work-id=104822610]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 104822610; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='104822610']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "475012ce591b56081fd31e20cc50ac95" } } $('.js-work-strip[data-work-id=104822610]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":104822610,"title":"How much do they tell us to move?","translated_title":"","metadata":{"publisher":"Elsevier BV","grobid_abstract":"A previous study revealed that neuronal activity in primary motor cortex (MI) and supplementary motor area (SMA) of the monkey depends both on which arm(s) moved and on the direction of movement. At the level of single cells, no di!erences were found between the areas in the information conveyed about each correlate. We constructed pseudosimultaneous response vectors and applied a decoding algorithm to quantify di!erences at a population level. We found that, on average, samples of 20 MI units carried less information about both movement type and direction than SMA units in a time window of 500 ms across the movement onset; a more detailed temporal analysis has revealed that SMA precedes M1 in motor planning and execution and that along the trial M1 cells carry as much information about direction as SMA cells.","publication_date":{"day":null,"month":null,"year":2001,"errors":{}},"publication_name":"Neurocomputing","grobid_abstract_attachment_id":104448904},"translated_abstract":null,"internal_url":"https://www.academia.edu/104822610/How_much_do_they_tell_us_to_move","translated_internal_url":"","created_at":"2023-07-22T09:22:53.918-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":32288545,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[{"id":104448904,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/104448904/thumbnails/1.jpg","file_name":"s0925-231228012900558-620230722-1-ckpdmw.pdf","download_url":"https://www.academia.edu/attachments/104448904/download_file","bulk_download_file_name":"How_much_do_they_tell_us_to_move.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/104448904/s0925-231228012900558-620230722-1-ckpdmw-libre.pdf?1690046094=\u0026response-content-disposition=attachment%3B+filename%3DHow_much_do_they_tell_us_to_move.pdf\u0026Expires=1743730653\u0026Signature=NwaDzOQc3NApZtC8upcp1f4JZyoYXHDEFlnOhAsWDzYYDTUzs5NMxZRVaSLrR0eq5yKB~FNZ15A6CREK0ZZT7w6BzLDeUsUzs~hBmF2dbC2ilpC04ZDYpfQXknAmpJP3FQClIprflLrpqCg3yIBa79jDwWjRSW3mp3~ihfXDBkXGmyHQolgrtZq4lYaSlUAVl0nmLzcpq1A5SYP3mnBq2FsObA31~iqTr~5U3KsNy4rdRVX-5V-~hBf9PDcZbDnjAjAExeOCsbrtW8-wxeoYaRfw9Kxh8msQoTJ4f2NQ-xxcFsoOTTXU5tpkvIsNEAMGkTcBnV2jsAuQFF3XXtDKBA__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"How_much_do_they_tell_us_to_move","translated_slug":"","page_count":4,"language":"en","content_type":"Work","summary":"A previous study revealed that neuronal activity in primary motor cortex (MI) and supplementary motor area (SMA) of the monkey depends both on which arm(s) moved and on the direction of movement. At the level of single cells, no di!erences were found between the areas in the information conveyed about each correlate. We constructed pseudosimultaneous response vectors and applied a decoding algorithm to quantify di!erences at a population level. We found that, on average, samples of 20 MI units carried less information about both movement type and direction than SMA units in a time window of 500 ms across the movement onset; a more detailed temporal analysis has revealed that SMA precedes M1 in motor planning and execution and that along the trial M1 cells carry as much information about direction as SMA cells.","owner":{"id":32288545,"first_name":"Alessandro","middle_initials":null,"last_name":"Treves","page_name":"ATreves","domain_name":"sissa","created_at":"2015-06-17T08:51:16.709-07:00","display_name":"Alessandro Treves","url":"https://sissa.academia.edu/ATreves"},"attachments":[{"id":104448904,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/104448904/thumbnails/1.jpg","file_name":"s0925-231228012900558-620230722-1-ckpdmw.pdf","download_url":"https://www.academia.edu/attachments/104448904/download_file","bulk_download_file_name":"How_much_do_they_tell_us_to_move.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/104448904/s0925-231228012900558-620230722-1-ckpdmw-libre.pdf?1690046094=\u0026response-content-disposition=attachment%3B+filename%3DHow_much_do_they_tell_us_to_move.pdf\u0026Expires=1743730653\u0026Signature=NwaDzOQc3NApZtC8upcp1f4JZyoYXHDEFlnOhAsWDzYYDTUzs5NMxZRVaSLrR0eq5yKB~FNZ15A6CREK0ZZT7w6BzLDeUsUzs~hBmF2dbC2ilpC04ZDYpfQXknAmpJP3FQClIprflLrpqCg3yIBa79jDwWjRSW3mp3~ihfXDBkXGmyHQolgrtZq4lYaSlUAVl0nmLzcpq1A5SYP3mnBq2FsObA31~iqTr~5U3KsNy4rdRVX-5V-~hBf9PDcZbDnjAjAExeOCsbrtW8-wxeoYaRfw9Kxh8msQoTJ4f2NQ-xxcFsoOTTXU5tpkvIsNEAMGkTcBnV2jsAuQFF3XXtDKBA__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":48,"name":"Engineering","url":"https://www.academia.edu/Documents/in/Engineering"},{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science"},{"id":465,"name":"Artificial Intelligence","url":"https://www.academia.edu/Documents/in/Artificial_Intelligence"},{"id":1410,"name":"Information Theory","url":"https://www.academia.edu/Documents/in/Information_Theory"},{"id":44433,"name":"Motor planning","url":"https://www.academia.edu/Documents/in/Motor_planning"},{"id":49482,"name":"Neural coding","url":"https://www.academia.edu/Documents/in/Neural_coding"},{"id":64336,"name":"Population","url":"https://www.academia.edu/Documents/in/Population"},{"id":153836,"name":"Motor Cortex","url":"https://www.academia.edu/Documents/in/Motor_Cortex"},{"id":306573,"name":"Neurocomputing","url":"https://www.academia.edu/Documents/in/Neurocomputing"},{"id":337457,"name":"Supplementary Motor Area","url":"https://www.academia.edu/Documents/in/Supplementary_Motor_Area"},{"id":473567,"name":"Neuronal Activity","url":"https://www.academia.edu/Documents/in/Neuronal_Activity"},{"id":594811,"name":"Cellular and Molecular Neuroscience","url":"https://www.academia.edu/Documents/in/Cellular_and_Molecular_Neuroscience"},{"id":668846,"name":"Sma","url":"https://www.academia.edu/Documents/in/Sma"},{"id":766410,"name":"Primary Motor Cortex","url":"https://www.academia.edu/Documents/in/Primary_Motor_Cortex"},{"id":980062,"name":"Temporal Analysis","url":"https://www.academia.edu/Documents/in/Temporal_Analysis"},{"id":2540355,"name":"neural code","url":"https://www.academia.edu/Documents/in/neural_code"},{"id":2922956,"name":"Psychology and Cognitive Sciences","url":"https://www.academia.edu/Documents/in/Psychology_and_Cognitive_Sciences"},{"id":3370271,"name":"Time window","url":"https://www.academia.edu/Documents/in/Time_window"}],"urls":[]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") if (true) { Aedu.setUpFigureCarousel('profile-work-104822610-figures'); } }); </script> <div class="js-work-strip profile--work_container" data-work-id="104822609"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/104822609/Disorders_of_Brain_Behavior_and_Cognition_The_Neurocomputational_Perspective_edited_by_James_A_Reggia_Eytan_Ruppin_and_Dennis_L_Glanzman"><img alt="Research paper thumbnail of Disorders of Brain, Behavior and Cognition: The Neurocomputational Perspective edited by James A. Reggia, Eytan Ruppin and Dennis L. Glanzman" class="work-thumbnail" src="https://attachments.academia-assets.com/104448902/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/104822609/Disorders_of_Brain_Behavior_and_Cognition_The_Neurocomputational_Perspective_edited_by_James_A_Reggia_Eytan_Ruppin_and_Dennis_L_Glanzman">Disorders of Brain, Behavior and Cognition: The Neurocomputational Perspective edited by James A. Reggia, Eytan Ruppin and Dennis L. Glanzman</a></div><div class="wp-workCard_item"><span>Trends in Neurosciences</span><span>, 2000</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="6e16bcb26c6fd505d0a0b4c7633b09dc" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":104448902,"asset_id":104822609,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/104448902/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="104822609"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="104822609"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 104822609; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=104822609]").text(description); $(".js-view-count[data-work-id=104822609]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 104822609; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='104822609']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "6e16bcb26c6fd505d0a0b4c7633b09dc" } } $('.js-work-strip[data-work-id=104822609]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":104822609,"title":"Disorders of Brain, Behavior and Cognition: The Neurocomputational Perspective edited by James A. Reggia, Eytan Ruppin and Dennis L. Glanzman","translated_title":"","metadata":{"publisher":"Elsevier BV","ai_abstract":"This volume, edited by James A. Reggia, Eytan Ruppin, and Dennis L. Glanzman, explores the realm of neurocomputational studies related to brain disorders, with its contents stemming from a diverse set of contributions presented at a key international workshop. The collection aims to demonstrate how computational models can enhance our understanding of memory, neuropsychological, neurological, and psychiatric disorders through quantitative analysis and theoretical advancements. While the book includes notable chapters such as the one on semantic dementia, it raises questions about the ability of these models to offer new predictions and rigorous testing of theoretical constructs in neuroscience.","publication_date":{"day":null,"month":null,"year":2000,"errors":{}},"publication_name":"Trends in Neurosciences"},"translated_abstract":null,"internal_url":"https://www.academia.edu/104822609/Disorders_of_Brain_Behavior_and_Cognition_The_Neurocomputational_Perspective_edited_by_James_A_Reggia_Eytan_Ruppin_and_Dennis_L_Glanzman","translated_internal_url":"","created_at":"2023-07-22T09:22:53.706-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":32288545,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[{"id":104448902,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/104448902/thumbnails/1.jpg","file_name":"s0166-223628002901565-420230722-1-pwada9.pdf","download_url":"https://www.academia.edu/attachments/104448902/download_file","bulk_download_file_name":"Disorders_of_Brain_Behavior_and_Cognitio.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/104448902/s0166-223628002901565-420230722-1-pwada9-libre.pdf?1690046096=\u0026response-content-disposition=attachment%3B+filename%3DDisorders_of_Brain_Behavior_and_Cognitio.pdf\u0026Expires=1743730653\u0026Signature=BpGYVJeeUV8qf7vO7WEdwhnTX6JWr8pDRYviLNmFWfgnAQTqCDbXaujoHYBzw7wDqqACPQ3RVf98uXyDP2BEd8tYB-cCGJbA8HUi0LoImM6HF68Z4NRxOxMkhMKmzDGQugaB6zRVKScEzLMuS-HGt80vo8DT3FJDcwKO28KsYcGSCCz-h4E9OtBCHKiAIWE9kM0wPQXZXhvT-wLmfyjs6aTPOh1B2uuXwhOsYxU2HN1FOOV5~giH4jB4Ke4yw8JR88TTiIOdMMyXO719Zq2dxGO~6idHN4vXpxy4Vpb1wUm4Nsj8ThFGPgHK3Bua7W7URmmRE14j95VHH9ZipD5S-w__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Disorders_of_Brain_Behavior_and_Cognition_The_Neurocomputational_Perspective_edited_by_James_A_Reggia_Eytan_Ruppin_and_Dennis_L_Glanzman","translated_slug":"","page_count":2,"language":"en","content_type":"Work","summary":null,"owner":{"id":32288545,"first_name":"Alessandro","middle_initials":null,"last_name":"Treves","page_name":"ATreves","domain_name":"sissa","created_at":"2015-06-17T08:51:16.709-07:00","display_name":"Alessandro Treves","url":"https://sissa.academia.edu/ATreves"},"attachments":[{"id":104448902,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/104448902/thumbnails/1.jpg","file_name":"s0166-223628002901565-420230722-1-pwada9.pdf","download_url":"https://www.academia.edu/attachments/104448902/download_file","bulk_download_file_name":"Disorders_of_Brain_Behavior_and_Cognitio.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/104448902/s0166-223628002901565-420230722-1-pwada9-libre.pdf?1690046096=\u0026response-content-disposition=attachment%3B+filename%3DDisorders_of_Brain_Behavior_and_Cognitio.pdf\u0026Expires=1743730653\u0026Signature=BpGYVJeeUV8qf7vO7WEdwhnTX6JWr8pDRYviLNmFWfgnAQTqCDbXaujoHYBzw7wDqqACPQ3RVf98uXyDP2BEd8tYB-cCGJbA8HUi0LoImM6HF68Z4NRxOxMkhMKmzDGQugaB6zRVKScEzLMuS-HGt80vo8DT3FJDcwKO28KsYcGSCCz-h4E9OtBCHKiAIWE9kM0wPQXZXhvT-wLmfyjs6aTPOh1B2uuXwhOsYxU2HN1FOOV5~giH4jB4Ke4yw8JR88TTiIOdMMyXO719Zq2dxGO~6idHN4vXpxy4Vpb1wUm4Nsj8ThFGPgHK3Bua7W7URmmRE14j95VHH9ZipD5S-w__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":161,"name":"Neuroscience","url":"https://www.academia.edu/Documents/in/Neuroscience"},{"id":221,"name":"Psychology","url":"https://www.academia.edu/Documents/in/Psychology"},{"id":237,"name":"Cognitive Science","url":"https://www.academia.edu/Documents/in/Cognitive_Science"},{"id":4212,"name":"Cognition","url":"https://www.academia.edu/Documents/in/Cognition"},{"id":59487,"name":"Computation","url":"https://www.academia.edu/Documents/in/Computation"},{"id":72667,"name":"Behaviour","url":"https://www.academia.edu/Documents/in/Behaviour"},{"id":1239755,"name":"Neurosciences","url":"https://www.academia.edu/Documents/in/Neurosciences"}],"urls":[]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") if (false) { Aedu.setUpFigureCarousel('profile-work-104822609-figures'); } }); </script> <div class="js-work-strip profile--work_container" data-work-id="104822608"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/104822608/Differential_impact_of_brain_damage_on_the_access_mode_to_memory_representations_an_information_theoretic_approach"><img alt="Research paper thumbnail of Differential impact of brain damage on the access mode to memory representations: an information theoretic approach" class="work-thumbnail" src="https://attachments.academia-assets.com/104448910/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/104822608/Differential_impact_of_brain_damage_on_the_access_mode_to_memory_representations_an_information_theoretic_approach">Differential impact of brain damage on the access mode to memory representations: an information theoretic approach</a></div><div class="wp-workCard_item"><span>European Journal of Neuroscience</span><span>, 2007</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Different access modes to information stored in long-term memory can lead to different distributi...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Different access modes to information stored in long-term memory can lead to different distributions of errors in classification tasks. We have designed a famous faces memory classification task that allows for the extraction of a measure of metric content, an index of the relevance of semantic cues for classification performance. High levels of metric content are indicative of a relatively preferred semantic access mode, while low levels, and similar correct performance, suggest a preferential episodic access mode. Compared with normal controls, the metric content index was increased in patients with Alzheimer's disease (AD), decreased in patients with herpes simplex encephalitis, and unvaried in patients with insult in the prefrontal cortex. Moreover, the metric content index was found to correlate with a measure of the severity of dementia in patients with AD, and to track the progression of the disease. These results underline the role of the medial-temporal lobes and of the temporal cortex, respectively, for the episodic and semantic routes to memory retrieval. Moreover, they confirm the reliability of information theoretic measures for characterizing the structure of the surviving memory representations in memory-impaired patient populations.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="9e0611c229575c15f50d0d2d86517035" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":104448910,"asset_id":104822608,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/104448910/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="104822608"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="104822608"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 104822608; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=104822608]").text(description); $(".js-view-count[data-work-id=104822608]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 104822608; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='104822608']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "9e0611c229575c15f50d0d2d86517035" } } $('.js-work-strip[data-work-id=104822608]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":104822608,"title":"Differential impact of brain damage on the access mode to memory representations: an information theoretic approach","translated_title":"","metadata":{"publisher":"Wiley","ai_title_tag":"Brain Damage Effects on Memory Access Modes","grobid_abstract":"Different access modes to information stored in long-term memory can lead to different distributions of errors in classification tasks. 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Moreover, they confirm the reliability of information theoretic measures for characterizing the structure of the surviving memory representations in memory-impaired patient populations.","publication_date":{"day":null,"month":null,"year":2007,"errors":{}},"publication_name":"European Journal of Neuroscience","grobid_abstract_attachment_id":104448910},"translated_abstract":null,"internal_url":"https://www.academia.edu/104822608/Differential_impact_of_brain_damage_on_the_access_mode_to_memory_representations_an_information_theoretic_approach","translated_internal_url":"","created_at":"2023-07-22T09:22:53.406-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":32288545,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[{"id":104448910,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/104448910/thumbnails/1.jpg","file_name":"Lau_07.pdf","download_url":"https://www.academia.edu/attachments/104448910/download_file","bulk_download_file_name":"Differential_impact_of_brain_damage_on_t.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/104448910/Lau_07-libre.pdf?1690046098=\u0026response-content-disposition=attachment%3B+filename%3DDifferential_impact_of_brain_damage_on_t.pdf\u0026Expires=1743730653\u0026Signature=I5v2~Gyqa8Y9VctxyJhIAL50ficX2H2Iyc0WKbar2FR0ZAParPFeltNIWXTovOienjQ3xfTbIz8Gh~d4mVO3LtvZ6gDnyem3ddtZYkYuoOhzcDIaLZUsKIjw4d3DpMCX82ZWFnriIWeaqtwHVY72uP4-xE76gq~F5b4zBcLUY2mL~L4om0qlg4~QcM3j5f7u8yO4E3g2Qhccmn6AxcBhNX-3WgmXpTmycU4Hum0ExapvssgnNnULIWkskZggxzlnzD0U~aZuC2PhXob0s32dGF5QznRQZnfNYWUBfFX8510Wj5mS7RvXBHNVcq5Zj8AkWVnfUQUpQkVvXyZ3Fri4Hg__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Differential_impact_of_brain_damage_on_the_access_mode_to_memory_representations_an_information_theoretic_approach","translated_slug":"","page_count":11,"language":"en","content_type":"Work","summary":"Different access modes to information stored in long-term memory can lead to different distributions of errors in classification tasks. We have designed a famous faces memory classification task that allows for the extraction of a measure of metric content, an index of the relevance of semantic cues for classification performance. High levels of metric content are indicative of a relatively preferred semantic access mode, while low levels, and similar correct performance, suggest a preferential episodic access mode. Compared with normal controls, the metric content index was increased in patients with Alzheimer's disease (AD), decreased in patients with herpes simplex encephalitis, and unvaried in patients with insult in the prefrontal cortex. Moreover, the metric content index was found to correlate with a measure of the severity of dementia in patients with AD, and to track the progression of the disease. These results underline the role of the medial-temporal lobes and of the temporal cortex, respectively, for the episodic and semantic routes to memory retrieval. Moreover, they confirm the reliability of information theoretic measures for characterizing the structure of the surviving memory representations in memory-impaired patient populations.","owner":{"id":32288545,"first_name":"Alessandro","middle_initials":null,"last_name":"Treves","page_name":"ATreves","domain_name":"sissa","created_at":"2015-06-17T08:51:16.709-07:00","display_name":"Alessandro Treves","url":"https://sissa.academia.edu/ATreves"},"attachments":[{"id":104448910,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/104448910/thumbnails/1.jpg","file_name":"Lau_07.pdf","download_url":"https://www.academia.edu/attachments/104448910/download_file","bulk_download_file_name":"Differential_impact_of_brain_damage_on_t.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/104448910/Lau_07-libre.pdf?1690046098=\u0026response-content-disposition=attachment%3B+filename%3DDifferential_impact_of_brain_damage_on_t.pdf\u0026Expires=1743730653\u0026Signature=I5v2~Gyqa8Y9VctxyJhIAL50ficX2H2Iyc0WKbar2FR0ZAParPFeltNIWXTovOienjQ3xfTbIz8Gh~d4mVO3LtvZ6gDnyem3ddtZYkYuoOhzcDIaLZUsKIjw4d3DpMCX82ZWFnriIWeaqtwHVY72uP4-xE76gq~F5b4zBcLUY2mL~L4om0qlg4~QcM3j5f7u8yO4E3g2Qhccmn6AxcBhNX-3WgmXpTmycU4Hum0ExapvssgnNnULIWkskZggxzlnzD0U~aZuC2PhXob0s32dGF5QznRQZnfNYWUBfFX8510Wj5mS7RvXBHNVcq5Zj8AkWVnfUQUpQkVvXyZ3Fri4Hg__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":237,"name":"Cognitive Science","url":"https://www.academia.edu/Documents/in/Cognitive_Science"},{"id":3303,"name":"Human Memory","url":"https://www.academia.edu/Documents/in/Human_Memory"},{"id":26327,"name":"Medicine","url":"https://www.academia.edu/Documents/in/Medicine"},{"id":32361,"name":"Episodic Memory","url":"https://www.academia.edu/Documents/in/Episodic_Memory"},{"id":46858,"name":"Memory","url":"https://www.academia.edu/Documents/in/Memory"},{"id":57556,"name":"Hippocampus","url":"https://www.academia.edu/Documents/in/Hippocampus"},{"id":178419,"name":"Memory Retrieval","url":"https://www.academia.edu/Documents/in/Memory_Retrieval"},{"id":279027,"name":"European","url":"https://www.academia.edu/Documents/in/European"},{"id":289271,"name":"Aged","url":"https://www.academia.edu/Documents/in/Aged"},{"id":522465,"name":"Long Term Memory","url":"https://www.academia.edu/Documents/in/Long_Term_Memory"},{"id":609835,"name":"Medial Temporal Lobe","url":"https://www.academia.edu/Documents/in/Medial_Temporal_Lobe"},{"id":749302,"name":"Indexation","url":"https://www.academia.edu/Documents/in/Indexation"},{"id":1120234,"name":"Alzheimer Disease","url":"https://www.academia.edu/Documents/in/Alzheimer_Disease"},{"id":1431361,"name":"Brain Damage","url":"https://www.academia.edu/Documents/in/Brain_Damage"},{"id":2450733,"name":"Brain injuries","url":"https://www.academia.edu/Documents/in/Brain_injuries"}],"urls":[{"id":33020594,"url":"http://onlinelibrary.wiley.com/wol1/doi/10.1111/j.1460-9568.2007.05881.x/fullpdf"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") if (false) { Aedu.setUpFigureCarousel('profile-work-104822608-figures'); } }); </script> <div class="js-work-strip profile--work_container" data-work-id="104822593"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/104822593/Grid_Cells_Lose_Coherence_in_Realistic_Environments"><img alt="Research paper thumbnail of Grid Cells Lose Coherence in Realistic Environments" class="work-thumbnail" src="https://attachments.academia-assets.com/104448882/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/104822593/Grid_Cells_Lose_Coherence_in_Realistic_Environments">Grid Cells Lose Coherence in Realistic Environments</a></div><div class="wp-workCard_item"><span>Hippocampus - New Advances [Working Title]</span><span>, 2021</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Spatial cognition in naturalistic environments, for freely moving animals, may pose quite differe...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Spatial cognition in naturalistic environments, for freely moving animals, may pose quite different constraints from that studied in artificial laboratory settings. Hippocampal place cells indeed look quite different, but almost nothing is known about entorhinal cortex grid cells, in the wild. Simulating our self-organizing adaptation model of grid cell pattern formation, we consider a virtual rat randomly exploring a virtual burrow, with feedforward connectivity from place to grid units and recurrent connectivity between grid units. The virtual burrow was based on those observed by John B. Calhoun, including several chambers and tunnels. Our results indicate that lateral connectivity between grid units may enhance their “gridness” within a limited strength range, but the overall effect of the irregular geometry is to disable long-range and obstruct short-range order. What appears as a smooth continuous attractor in a flat box, kept rigid by recurrent connections, turns into an inco...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="e8b24aa56a3b5dfef561b2aa4ff66389" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":104448882,"asset_id":104822593,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/104448882/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="104822593"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="104822593"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 104822593; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=104822593]").text(description); $(".js-view-count[data-work-id=104822593]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 104822593; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='104822593']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "e8b24aa56a3b5dfef561b2aa4ff66389" } } $('.js-work-strip[data-work-id=104822593]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":104822593,"title":"Grid Cells Lose Coherence in Realistic Environments","translated_title":"","metadata":{"abstract":"Spatial cognition in naturalistic environments, for freely moving animals, may pose quite different constraints from that studied in artificial laboratory settings. 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Calhoun, including several chambers and tunnels. Our results indicate that lateral connectivity between grid units may enhance their “gridness” within a limited strength range, but the overall effect of the irregular geometry is to disable long-range and obstruct short-range order. 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Hippocampal place cells indeed look quite different, but almost nothing is known about entorhinal cortex grid cells, in the wild. Simulating our self-organizing adaptation model of grid cell pattern formation, we consider a virtual rat randomly exploring a virtual burrow, with feedforward connectivity from place to grid units and recurrent connectivity between grid units. The virtual burrow was based on those observed by John B. Calhoun, including several chambers and tunnels. Our results indicate that lateral connectivity between grid units may enhance their “gridness” within a limited strength range, but the overall effect of the irregular geometry is to disable long-range and obstruct short-range order. 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The manner by which transitions between map fragments are generated is unknown. We recorded grid cells while rats were trained in two rectangular compartments, A and B (each 1 m × 2 m), separated by a wall. Once distinct grid maps were established in each environment, we removed the partition and allowed the rat to explore the merged environment (2 m × 2 m). The grid patterns were largely retained along the distal walls of the box. Nearer the former partition line, individual grid fields changed location, resulting almost immediately in local spatial periodicity and continuity between the two original maps. Grid cells belonging to the same grid module retained phase relationships during the transformation. Thus, when environments are merged, grid fields reorganize rapidly to establish spatial periodicity in the area where the environments meet.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="f0c34fc24b5c9a70dc76933f9afcaae7" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":78602721,"asset_id":67945547,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/78602721/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="67945547"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="67945547"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 67945547; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=67945547]").text(description); $(".js-view-count[data-work-id=67945547]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 67945547; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='67945547']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "f0c34fc24b5c9a70dc76933f9afcaae7" } } $('.js-work-strip[data-work-id=67945547]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":67945547,"title":"Integration of grid maps in merged environments","translated_title":"","metadata":{"abstract":"Natural environments are represented by local maps of grid cells and place cells that are stitched together. 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") if (false) { Aedu.setUpFigureCarousel('profile-work-67945541-figures'); } }); </script> <div class="js-work-strip profile--work_container" data-work-id="67945538"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/67945538/Chapter_19_Information_coding_in_higher_sensory_and_memory_areas"><img alt="Research paper thumbnail of Chapter 19 Information coding in higher sensory and memory areas" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title">Chapter 19 Information coding in higher sensory and memory areas</div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Publisher Summary This chapter discusses information coding in higher sensory and memory areas. N...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Publisher Summary This chapter discusses information coding in higher sensory and memory areas. Neurons are vastly simpler than human beings are, but the metaphor is not completely silly because it illustrates the volatility of the notion of neural codes. Information theory has been developed precisely to quantify communication and is quintessential to an appraisal of neural codes. Applying information theory to neural activity (rather than to the synthetic communication systems for which it was developed) is however riddled with practical problems and subtleties, which must be clarified before reporting experimental results. The chapter considers other means of neuronal communication than the emission of action potentials or spikes and regards them as self-similar all-or-none events whose only distinctive features are the time of emission and the identity of the emitting neuron. The extent to which the firing rates of a population of neurons may or may not carry most of the information represented in the complete list of spike emission times is a question to be addressed experimentally in any given situation.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="67945538"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="67945538"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 67945538; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=67945538]").text(description); $(".js-view-count[data-work-id=67945538]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 67945538; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='67945538']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=67945538]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":67945538,"title":"Chapter 19 Information coding in higher sensory and memory areas","translated_title":"","metadata":{"abstract":"Publisher Summary This chapter discusses information coding in higher sensory and memory areas. 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The extent to which the firing rates of a population of neurons may or may not carry most of the information represented in the complete list of spike emission times is a question to be addressed experimentally in any given situation.","owner":{"id":32288545,"first_name":"Alessandro","middle_initials":null,"last_name":"Treves","page_name":"ATreves","domain_name":"sissa","created_at":"2015-06-17T08:51:16.709-07:00","display_name":"Alessandro Treves","url":"https://sissa.academia.edu/ATreves"},"attachments":[],"research_interests":[],"urls":[{"id":16348381,"url":"http://sciencedirect.com/science/article/pii/s1383812101800221"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") if (false) { Aedu.setUpFigureCarousel('profile-work-67945538-figures'); } }); </script> <div class="js-work-strip profile--work_container" data-work-id="67945536"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/67945536/Tov%C3%A9e_M_J_Rolls_E_T_and_Treves_A_Information_encoding_and_the_responses_of_single_neurons_in_the_primate_temporal_visual_cortex_J_Neurophysiol_70_640_654"><img alt="Research paper thumbnail of Tovée, M. J., Rolls, E. T. & Treves, A. Information encoding and the responses of single neurons in the primate temporal visual cortex. J. Neurophysiol. 70, 640-654" class="work-thumbnail" src="https://attachments.academia-assets.com/78602724/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/67945536/Tov%C3%A9e_M_J_Rolls_E_T_and_Treves_A_Information_encoding_and_the_responses_of_single_neurons_in_the_primate_temporal_visual_cortex_J_Neurophysiol_70_640_654">Tovée, M. J., Rolls, E. T. & Treves, A. Information encoding and the responses of single neurons in the primate temporal visual cortex. J. Neurophysiol. 70, 640-654</a></div><div class="wp-workCard_item"><span>Journal of Neurophysiology</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="66de1c9a98ba0d99f2643f13b2a72ac8" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":78602724,"asset_id":67945536,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/78602724/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="67945536"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="67945536"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 67945536; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=67945536]").text(description); $(".js-view-count[data-work-id=67945536]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 67945536; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='67945536']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "66de1c9a98ba0d99f2643f13b2a72ac8" } } $('.js-work-strip[data-work-id=67945536]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":67945536,"title":"Tovée, M. 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") if (false) { Aedu.setUpFigureCarousel('profile-work-67945536-figures'); } }); </script> <div class="js-work-strip profile--work_container" data-work-id="67945534"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/67945534/Analytical_estimates_of_limited_sampling_in_different_information_measures"><img alt="Research paper thumbnail of Analytical estimates of limited sampling in different information measures" class="work-thumbnail" src="https://attachments.academia-assets.com/78602723/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/67945534/Analytical_estimates_of_limited_sampling_in_different_information_measures">Analytical estimates of limited sampling in different information measures</a></div><div class="wp-workCard_item"><span>Network Computation in Neural Systems</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Measuring the information carried by neuronal activity is made difficult, particularly when recor...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Measuring the information carried by neuronal activity is made difficult, particularly when recording from mammalian cells, by the limited amount of data usually available, which results in a systematic error. While empirical ad hoc procedures have been used to correct for such error, we have recently proposed a direct procedure consisting of the analytical calculation of the average error, its estimation (up to subleading terms) from the data, and its subtraction from raw information measures to yield unbiased measures. We calculate here the leading correction terms for both the average transmitted information and the conditional information and, since usually one must first regularize the data, we specify the expressions appropriate to different regularizations. Computer simulations indicate a broad range of validity of the analytical results, suggest the effectiveness of regularizing by simple binning and illustrate the advantage of this over the previously used 'bootstrap' procedure.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="62f46cb8ddd36803986db324e68c9428" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":78602723,"asset_id":67945534,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/78602723/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="67945534"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="67945534"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 67945534; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=67945534]").text(description); $(".js-view-count[data-work-id=67945534]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 67945534; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='67945534']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "62f46cb8ddd36803986db324e68c9428" } } $('.js-work-strip[data-work-id=67945534]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":67945534,"title":"Analytical estimates of limited sampling in different information measures","translated_title":"","metadata":{"grobid_abstract":"Measuring the information carried by neuronal activity is made difficult, particularly when recording from mammalian cells, by the limited amount of data usually available, which results in a systematic error. 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