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class="social-profile-avatar-container"><img class="profile-avatar u-positionAbsolute" border="0" alt="" src="//a.academia-assets.com/images/s200_no_pic.png" /></div><div class="title-container"><h1 class="ds2-5-heading-sans-serif-sm">Maoz Shamir</h1><div class="affiliations-container fake-truncate js-profile-affiliations"><div><a class="u-tcGrayDarker" href="https://bgu.academia.edu/">Ben Gurion University of the Negev</a>, <a class="u-tcGrayDarker" href="https://bgu.academia.edu/Departments/Physiology_and_Cell_Biology/Documents">Physiology and Cell Biology</a>, <span class="u-tcGrayDarker">Faculty Member</span></div></div></div></div><div class="sidebar-cta-container"><button class="ds2-5-button hidden profile-cta-button grow js-profile-follow-button" data-broccoli-component="user-info.follow-button" data-click-track="profile-user-info-follow-button" data-follow-user-fname="Maoz" data-follow-user-id="246424110" data-follow-user-source="profile_button" data-has-google="false"><span 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data-component-name="Pill" data-props="{"color":"gray","children":["Genetics Cell Biology and Plant Physiology"]}" data-trace="false" data-dom-id="Pill-react-component-3deacf74-499c-45c0-af64-bc3cb59f8ca7"></div> <div id="Pill-react-component-3deacf74-499c-45c0-af64-bc3cb59f8ca7"></div> </a></div></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 Maoz Shamir</h3></div><div class="js-work-strip profile--work_container" data-work-id="112782991"><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/112782991/Robust_rhythmogenesis_via_spike_timing_dependent_plasticity"><img alt="Research paper thumbnail of Robust rhythmogenesis via spike-timing-dependent plasticity" class="work-thumbnail" src="https://attachments.academia-assets.com/109907349/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/112782991/Robust_rhythmogenesis_via_spike_timing_dependent_plasticity">Robust rhythmogenesis via spike-timing-dependent plasticity</a></div><div class="wp-workCard_item"><span>Physical review</span><span>, Aug 20, 2021</span></div><div class="wp-workCard_item wp-workCard--actions"><span 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plasticity","translated_title":"","metadata":{"publisher":"American Physical Society","grobid_abstract":"Rhythmic activity in the gamma band (30-100Hz) has been observed in numerous animal species ranging from insects to humans, and in relation to a wide range of cognitive tasks. Various experimental and theoretical studies have investigated this rhythmic activity. The theoretical efforts have mainly been focused on the neuronal dynamics, under the assumption that network connectivity satisfies certain fine-tuning conditions required to generate gamma oscillations. However, it remains unclear how this fine tuning is achieved. Here we investigated the hypothesis that spike timing dependent plasticity (STDP) can provide the underlying mechanism for tuning synaptic connectivity to generate rhythmic activity in the gamma band. We addressed this question in a modeling study. We examined STDP dynamics in the framework of a network of excitatory and inhibitory neuronal populations that has been suggested to underlie the generation of gamma. Mean field Fokker Planck equations for the synaptic weights dynamics are derived in the limit of slow learning. We drew on this approximation to determine which types of STDP rules drive the system to exhibit gamma oscillations, and demonstrate how the parameters that characterize the plasticity rule govern the rhythmic activity. Finally, we propose a novel mechanism that can ensure the robustness of self-developing processes, in general and for rhythmogenesis in particular.","publication_date":{"day":20,"month":8,"year":2021,"errors":{}},"publication_name":"Physical review","grobid_abstract_attachment_id":109907349},"translated_abstract":null,"internal_url":"https://www.academia.edu/112782991/Robust_rhythmogenesis_via_spike_timing_dependent_plasticity","translated_internal_url":"","created_at":"2024-01-02T03:46:09.055-08:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":246424110,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[{"id":109907349,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/109907349/thumbnails/1.jpg","file_name":"2007.pdf","download_url":"https://www.academia.edu/attachments/109907349/download_file?st=MTczMjgzMTA2MSw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Robust_rhythmogenesis_via_spike_timing_d.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/109907349/2007-libre.pdf?1704197173=\u0026response-content-disposition=attachment%3B+filename%3DRobust_rhythmogenesis_via_spike_timing_d.pdf\u0026Expires=1732834661\u0026Signature=GKO~JJirMxntOHK9M6UEZm6GpguGAuLyQ4b-KC5u4OARHp1PJHkMnLZuyZz6ApvXJpAbfw-4bp7kt2fG3ixXScNxV89bo9jP0xPznnvqTfmFI7ymm6o8~tlGzi1ZaBnr02TrY4kLA2p2Vy8X597mNgpYG1sViP9ZEzhW0-S7c0OwRvgMSozXfoSmfa~-kQvu7hPNmuSCuOzieZIpPcaxdzHamMW3uQqo4sTS5Kd5FtnjKQBFRmAVeGeb7OK5sBJNu~LMub5uAhWOCmFM10WhFzxFWK2PSzeT1sA3iLTHka2JHk9yzyt5-3KMUXUT011iN~yf6rOuzqsltMa3Caf9Ig__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Robust_rhythmogenesis_via_spike_timing_dependent_plasticity","translated_slug":"","page_count":9,"language":"en","content_type":"Work","owner":{"id":246424110,"first_name":"Maoz","middle_initials":null,"last_name":"Shamir","page_name":"MaozShamir","domain_name":"bgu","created_at":"2022-11-20T23:39:36.274-08:00","display_name":"Maoz Shamir","url":"https://bgu.academia.edu/MaozShamir"},"attachments":[{"id":109907349,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/109907349/thumbnails/1.jpg","file_name":"2007.pdf","download_url":"https://www.academia.edu/attachments/109907349/download_file?st=MTczMjgzMTA2MSw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Robust_rhythmogenesis_via_spike_timing_d.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/109907349/2007-libre.pdf?1704197173=\u0026response-content-disposition=attachment%3B+filename%3DRobust_rhythmogenesis_via_spike_timing_d.pdf\u0026Expires=1732834661\u0026Signature=GKO~JJirMxntOHK9M6UEZm6GpguGAuLyQ4b-KC5u4OARHp1PJHkMnLZuyZz6ApvXJpAbfw-4bp7kt2fG3ixXScNxV89bo9jP0xPznnvqTfmFI7ymm6o8~tlGzi1ZaBnr02TrY4kLA2p2Vy8X597mNgpYG1sViP9ZEzhW0-S7c0OwRvgMSozXfoSmfa~-kQvu7hPNmuSCuOzieZIpPcaxdzHamMW3uQqo4sTS5Kd5FtnjKQBFRmAVeGeb7OK5sBJNu~LMub5uAhWOCmFM10WhFzxFWK2PSzeT1sA3iLTHka2JHk9yzyt5-3KMUXUT011iN~yf6rOuzqsltMa3Caf9Ig__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"},{"id":109907350,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/109907350/thumbnails/1.jpg","file_name":"2007.pdf","download_url":"https://www.academia.edu/attachments/109907350/download_file","bulk_download_file_name":"Robust_rhythmogenesis_via_spike_timing_d.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/109907350/2007-libre.pdf?1704197185=\u0026response-content-disposition=attachment%3B+filename%3DRobust_rhythmogenesis_via_spike_timing_d.pdf\u0026Expires=1732834661\u0026Signature=ek~PQKiyZKpyKRDlQpx7ckFIiVwsprNm5ElRlraU5U-YCcijS22DsMxDWiZzWRHQQkht9HEnSwJP0G5YsaAdryY~AnaumtdPxSeQkLJbmE1S8GXLiqzMHxUu~C3m5QU~xHyWDjGO2XFj4b80rpgUdo6LfDG4o~juF~6bplev5D2URco5oLKti~sLbigjSil1WAP-fvSgPoDNH4kBsSRjkDhNqn9GN5XW6fHrYAn7gWSb5WHX-mf-8Z2WGxf7KlbErvfoWqLP4hnFt7-x4UUOCct7uBOmMH4nQ~YHDUjvxojJz16~YYIxfBs3Tr0zPc6BvoLd3S8SVQ4HIOMRRJQA2A__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":161,"name":"Neuroscience","url":"https://www.academia.edu/Documents/in/Neuroscience"},{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science"},{"id":3886,"name":"Rhythm","url":"https://www.academia.edu/Documents/in/Rhythm"},{"id":192257,"name":"Physical","url":"https://www.academia.edu/Documents/in/Physical"},{"id":210122,"name":"Robustness (evolution)","url":"https://www.academia.edu/Documents/in/Robustness_evolution_"}],"urls":[{"id":38081638,"url":"http://arxiv.org/pdf/2007.14127"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="112782990"><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/112782990/STDP_and_the_distribution_of_preferred_phases_in_the_whisker_system"><img alt="Research paper thumbnail of STDP and the distribution of preferred phases in the whisker system" class="work-thumbnail" src="https://attachments.academia-assets.com/109907383/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/112782990/STDP_and_the_distribution_of_preferred_phases_in_the_whisker_system">STDP and the distribution of preferred phases in the whisker system</a></div><div class="wp-workCard_item"><span>bioRxiv (Cold Spring Harbor Laboratory)</span><span>, Apr 30, 2021</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="6a5ee0db732491b38fd133aa5b222384" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":109907383,"asset_id":112782990,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/109907383/download_file?st=MTczMjgzMTA2MSw4LjIyMi4yMDguMTQ2&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="112782990"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="112782990"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 112782990; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=112782990]").text(description); $(".js-view-count[data-work-id=112782990]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 112782990; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='112782990']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 112782990, container: "", }); });</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-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.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: "6a5ee0db732491b38fd133aa5b222384" } } $('.js-work-strip[data-work-id=112782990]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":112782990,"title":"STDP and the distribution of preferred phases in the whisker system","translated_title":"","metadata":{"publisher":"Cold Spring Harbor Laboratory","grobid_abstract":"Rats and mice use their whiskers to probe the environment. By rhythmically swiping their whiskers back and forth they can detect the existence of an object, locate it, and identify its texture. Localization can be accomplished by inferring the whisker's position. Rhythmic neurons that track the phase of the whisking cycle encode information about the azimuthal location of the whisker. These neurons are characterized by preferred phases of firing that are narrowly distributed. Consequently, pooling the rhythmic signal from several upstream neurons is expected to result in a much narrower distribution of preferred phases in the downstream population, which however has not been observed empirically. Here, we show how spike timing dependent plasticity (STDP) can provide a solution to this conundrum. We investigated the effect of STDP on the utility of a neural population to transmit rhythmic information downstream using the framework of a modeling study. We found that under a wide range of parameters, STDP facilitated the transfer of rhythmic information despite the fact that all the synaptic weights remained dynamic. As a result, the preferred phase of the downstream neuron was not fixed, but rather drifted in time at a drift velocity that depended on the preferred phase, thus inducing a distribution of preferred phases. We further analyzed how the STDP rule governs the distribution of preferred phases in the downstream population. This link between the STDP rule and the distribution of preferred phases constitutes a natural test for our theory.","publication_date":{"day":30,"month":4,"year":2021,"errors":{}},"publication_name":"bioRxiv (Cold Spring Harbor Laboratory)","grobid_abstract_attachment_id":109907383},"translated_abstract":null,"internal_url":"https://www.academia.edu/112782990/STDP_and_the_distribution_of_preferred_phases_in_the_whisker_system","translated_internal_url":"","created_at":"2024-01-02T03:46:06.149-08:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":246424110,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[{"id":109907383,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/109907383/thumbnails/1.jpg","file_name":"pmc8480728.pdf","download_url":"https://www.academia.edu/attachments/109907383/download_file?st=MTczMjgzMTA2MSw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"STDP_and_the_distribution_of_preferred_p.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/109907383/pmc8480728-libre.pdf?1704197181=\u0026response-content-disposition=attachment%3B+filename%3DSTDP_and_the_distribution_of_preferred_p.pdf\u0026Expires=1732834661\u0026Signature=QT8L-v57Qx2xrddA0MVC02D2OR3Z6uGPX3jCvUd9q3MaH5CbiD9inN8uouwpcqzP6z2PT~PhTWtMvs83KTfjFJxjKUttl0zUpULX53JoEnOY~bwFQtCfa9H95mevHzrT0275-5KbXGCUH4U6yHuhUW-9lk0h3Xz3gG87XPlpBLW25i2QUdnU0wr31XKBigk9hh4-szs1ZujSx-dxp1-tdaUhcfNSs7Aw8kUrbGIxqPkN1TSPsOoeaoyG3bKImB43iPYX7RtZHw3MkD3DZtm1sKkyEDrkcoJF1AIXe2faD8DvHiP2GE8kZ5IyawtU2GgyR4Ee~wPtjVgpAgGs5BgMyg__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"STDP_and_the_distribution_of_preferred_phases_in_the_whisker_system","translated_slug":"","page_count":18,"language":"en","content_type":"Work","owner":{"id":246424110,"first_name":"Maoz","middle_initials":null,"last_name":"Shamir","page_name":"MaozShamir","domain_name":"bgu","created_at":"2022-11-20T23:39:36.274-08:00","display_name":"Maoz Shamir","url":"https://bgu.academia.edu/MaozShamir"},"attachments":[{"id":109907383,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/109907383/thumbnails/1.jpg","file_name":"pmc8480728.pdf","download_url":"https://www.academia.edu/attachments/109907383/download_file?st=MTczMjgzMTA2MSw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"STDP_and_the_distribution_of_preferred_p.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/109907383/pmc8480728-libre.pdf?1704197181=\u0026response-content-disposition=attachment%3B+filename%3DSTDP_and_the_distribution_of_preferred_p.pdf\u0026Expires=1732834661\u0026Signature=QT8L-v57Qx2xrddA0MVC02D2OR3Z6uGPX3jCvUd9q3MaH5CbiD9inN8uouwpcqzP6z2PT~PhTWtMvs83KTfjFJxjKUttl0zUpULX53JoEnOY~bwFQtCfa9H95mevHzrT0275-5KbXGCUH4U6yHuhUW-9lk0h3Xz3gG87XPlpBLW25i2QUdnU0wr31XKBigk9hh4-szs1ZujSx-dxp1-tdaUhcfNSs7Aw8kUrbGIxqPkN1TSPsOoeaoyG3bKImB43iPYX7RtZHw3MkD3DZtm1sKkyEDrkcoJF1AIXe2faD8DvHiP2GE8kZ5IyawtU2GgyR4Ee~wPtjVgpAgGs5BgMyg__\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":3886,"name":"Rhythm","url":"https://www.academia.edu/Documents/in/Rhythm"},{"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":64336,"name":"Population","url":"https://www.academia.edu/Documents/in/Population"},{"id":80414,"name":"Mathematical Sciences","url":"https://www.academia.edu/Documents/in/Mathematical_Sciences"},{"id":207853,"name":"Pooling","url":"https://www.academia.edu/Documents/in/Pooling"},{"id":2249025,"name":"Whisker","url":"https://www.academia.edu/Documents/in/Whisker"},{"id":2273259,"name":"Biological System","url":"https://www.academia.edu/Documents/in/Biological_System"}],"urls":[{"id":38081637,"url":"https://doi.org/10.1101/2021.04.29.442009"}]}, dispatcherData: dispatcherData }); 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Previous studies have shown that spike-timing-dependent plasticity (STDP) can facilitate the transfer of rhythmic activity downstream the information processing pathway. However, STDP has also been known to generate strong winner-take-all like competitions between subgroups of correlated synaptic inputs. Consequently, one might expect that STDP would induce strong competition between different rhythmicity channels thus preventing the multiplexing of information across different frequency channels. This study explored whether STDP facilitates the multiplexing of information across multiple frequency channels, and if so, under what conditions. We investigated the STDP dynamics in the framework of a model consisting of two competing sub-populations of neurons that synapse in a feedforward manner onto a single post-synaptic neuron. Each sub-population was assumed to oscillate in an independent manner and in a different frequency band. To investigate the STDP dynamics, a mean field Fokker-Planck theory was developed in the limit of the slow learning rate. Surprisingly, our theory predicted limited interactions between the different subgroups. Our analysis further revealed that the interaction between these channels was mainly mediated by the shared component of the mean activity. Next, we generalized these results beyond the simplistic model using numerical simulations. We found that for a wide range of parameters, the system converged to a solution in which the post-synaptic neuron responded to both rhythms. Nevertheless, all the synaptic weights remained dynamic and did not converge to a fixed point. These findings imply that STDP can support the multiplexing of rhythmic information, and demonstrate how functionality (multiplexing of information) can be retained in the face of continuous remodeling of all the synaptic weights.","publication_date":{"day":29,"month":6,"year":2020,"errors":{}},"publication_name":"PLOS Computational Biology","grobid_abstract_attachment_id":109907327},"translated_abstract":null,"internal_url":"https://www.academia.edu/112782929/Multiplexing_rhythmic_information_by_spike_timing_dependent_plasticity","translated_internal_url":"","created_at":"2024-01-02T03:44:17.406-08:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":246424110,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[{"id":109907327,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/109907327/thumbnails/1.jpg","file_name":"1911.11466v1.pdf","download_url":"https://www.academia.edu/attachments/109907327/download_file?st=MTczMjgzMTA2MSw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Multiplexing_rhythmic_information_by_spi.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/109907327/1911.11466v1-libre.pdf?1704197201=\u0026response-content-disposition=attachment%3B+filename%3DMultiplexing_rhythmic_information_by_spi.pdf\u0026Expires=1732834661\u0026Signature=aGnTEH4ifGqC6S4O~SC8xe-AOXGvWOhhAvLMtgzFKLjkHHeKcqmxPUoYo18uMDsmXNt-c6XETzmhASffDHt-5GgPFwGkeYKhvMz-7V121Twz1hdnPPmTo7AYOi95~KEqOGc8HPvWGqftSl12j7OengpPaJaLXR9DXVSTGYfCBEISG7wcBw9ePpIXUrvc~CZoNzTVbdsal61q0uAvdCrixwyipYrMYpv0PitU7Cuf2WsbD-VDHMYKnV0-ZKfRo17LMh0BPoWjx93H-c3dL8eNPWN0RkDosli-Apd8VQQpNO1YdFzH0MjzCiopmZY8v7jbECguflIIYqDWPKY3gd7SJA__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Multiplexing_rhythmic_information_by_spike_timing_dependent_plasticity","translated_slug":"","page_count":14,"language":"en","content_type":"Work","owner":{"id":246424110,"first_name":"Maoz","middle_initials":null,"last_name":"Shamir","page_name":"MaozShamir","domain_name":"bgu","created_at":"2022-11-20T23:39:36.274-08:00","display_name":"Maoz Shamir","url":"https://bgu.academia.edu/MaozShamir"},"attachments":[{"id":109907327,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/109907327/thumbnails/1.jpg","file_name":"1911.11466v1.pdf","download_url":"https://www.academia.edu/attachments/109907327/download_file?st=MTczMjgzMTA2MSw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Multiplexing_rhythmic_information_by_spi.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/109907327/1911.11466v1-libre.pdf?1704197201=\u0026response-content-disposition=attachment%3B+filename%3DMultiplexing_rhythmic_information_by_spi.pdf\u0026Expires=1732834661\u0026Signature=aGnTEH4ifGqC6S4O~SC8xe-AOXGvWOhhAvLMtgzFKLjkHHeKcqmxPUoYo18uMDsmXNt-c6XETzmhASffDHt-5GgPFwGkeYKhvMz-7V121Twz1hdnPPmTo7AYOi95~KEqOGc8HPvWGqftSl12j7OengpPaJaLXR9DXVSTGYfCBEISG7wcBw9ePpIXUrvc~CZoNzTVbdsal61q0uAvdCrixwyipYrMYpv0PitU7Cuf2WsbD-VDHMYKnV0-ZKfRo17LMh0BPoWjx93H-c3dL8eNPWN0RkDosli-Apd8VQQpNO1YdFzH0MjzCiopmZY8v7jbECguflIIYqDWPKY3gd7SJA__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":161,"name":"Neuroscience","url":"https://www.academia.edu/Documents/in/Neuroscience"},{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science"},{"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":4110287,"name":"Feed Forward","url":"https://www.academia.edu/Documents/in/Feed_Forward"}],"urls":[{"id":38081594,"url":"https://doi.org/10.1371/journal.pcbi.1008000"}]}, dispatcherData: dispatcherData }); 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="95888234"><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/95888234/Correlation_Codes_in_Neuronal_Populations"><img alt="Research paper thumbnail of Correlation Codes in Neuronal Populations" class="work-thumbnail" src="https://attachments.academia-assets.com/97942863/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/95888234/Correlation_Codes_in_Neuronal_Populations">Correlation Codes in Neuronal Populations</a></div><div class="wp-workCard_item"><span>Advances in Neural Information Processing Systems 14</span><span>, 2002</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="675628ae9986edb07c2dfe8cd83fb255" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":97942863,"asset_id":95888234,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/97942863/download_file?st=MTczMjgzMTA2MSw4LjIyMi4yMDguMTQ2&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="95888234"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="95888234"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 95888234; 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However, this information can be greatly suppressed by long range correlations. Here we study the efficiency of coding information in the second order statistics of the population responses. We show that the Fisher Information of this system grows linearly with the size of the system. We propose a bilinear readout model for extracting information from correlation codes, and evaluate its performance in discrimination and estimation tasks. It is shown that the main source of information in this system is the stimulus dependence of the variances of the single neuron responses.","publication_date":{"day":null,"month":null,"year":2002,"errors":{}},"publication_name":"Advances in Neural Information Processing Systems 14","grobid_abstract_attachment_id":97942863},"translated_abstract":null,"internal_url":"https://www.academia.edu/95888234/Correlation_Codes_in_Neuronal_Populations","translated_internal_url":"","created_at":"2023-01-29T00:57:30.335-08:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":246424110,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[{"id":97942863,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/97942863/thumbnails/1.jpg","file_name":"2031-correlation-codes-in-neuronal-populations.pdf","download_url":"https://www.academia.edu/attachments/97942863/download_file?st=MTczMjgzMTA2MSw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Correlation_Codes_in_Neuronal_Population.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/97942863/2031-correlation-codes-in-neuronal-populations-libre.pdf?1674994217=\u0026response-content-disposition=attachment%3B+filename%3DCorrelation_Codes_in_Neuronal_Population.pdf\u0026Expires=1732834661\u0026Signature=gIPoMoUL-F7phe2~H7N-xUGKYB11lR8v2AOWpnchWC1d5xBKgJ1~ru5JW7N1L4CazO2wMjVLR7oHP5KDCE1u5M5--qhNwdAFlcg58tMTNRamY7YVTdhWEAmIVpr~tKt6~kJhAsmQyPTCdMLlgqpR7ysH8MHhwGiw84v4DUl4a-9ZGUWsVE5j8WQ5XGfyh80KW8XprMywsQ~rXo5b7Y3H4ZflAXD9RvcP0UdbUhUZejTzUnUgExuMVD483P8ZQtYDzDauOkqcN~5-MAgZ4cCmaMlGQQytyx~0cIJmWvAM9Daw~2zh~oQXGran5JGEW0zX-LJIxp0Uyubt98veFORCcg__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Correlation_Codes_in_Neuronal_Populations","translated_slug":"","page_count":8,"language":"en","content_type":"Work","owner":{"id":246424110,"first_name":"Maoz","middle_initials":null,"last_name":"Shamir","page_name":"MaozShamir","domain_name":"bgu","created_at":"2022-11-20T23:39:36.274-08:00","display_name":"Maoz Shamir","url":"https://bgu.academia.edu/MaozShamir"},"attachments":[{"id":97942863,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/97942863/thumbnails/1.jpg","file_name":"2031-correlation-codes-in-neuronal-populations.pdf","download_url":"https://www.academia.edu/attachments/97942863/download_file?st=MTczMjgzMTA2MSw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Correlation_Codes_in_Neuronal_Population.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/97942863/2031-correlation-codes-in-neuronal-populations-libre.pdf?1674994217=\u0026response-content-disposition=attachment%3B+filename%3DCorrelation_Codes_in_Neuronal_Population.pdf\u0026Expires=1732834661\u0026Signature=gIPoMoUL-F7phe2~H7N-xUGKYB11lR8v2AOWpnchWC1d5xBKgJ1~ru5JW7N1L4CazO2wMjVLR7oHP5KDCE1u5M5--qhNwdAFlcg58tMTNRamY7YVTdhWEAmIVpr~tKt6~kJhAsmQyPTCdMLlgqpR7ysH8MHhwGiw84v4DUl4a-9ZGUWsVE5j8WQ5XGfyh80KW8XprMywsQ~rXo5b7Y3H4ZflAXD9RvcP0UdbUhUZejTzUnUgExuMVD483P8ZQtYDzDauOkqcN~5-MAgZ4cCmaMlGQQytyx~0cIJmWvAM9Daw~2zh~oQXGran5JGEW0zX-LJIxp0Uyubt98veFORCcg__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":300,"name":"Mathematics","url":"https://www.academia.edu/Documents/in/Mathematics"},{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science"},{"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":81504,"name":"Correlation","url":"https://www.academia.edu/Documents/in/Correlation"},{"id":135805,"name":"Fisher information","url":"https://www.academia.edu/Documents/in/Fisher_information"},{"id":384671,"name":"Population coding","url":"https://www.academia.edu/Documents/in/Population_coding"},{"id":2534325,"name":"Correlation Matrix","url":"https://www.academia.edu/Documents/in/Correlation_Matrix"}],"urls":[]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="95888233"><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/95888233/Robust_Rhythmogenesis_in_the_Gamma_Band_via_Spike_Timing_Dependent_Plasticity"><img alt="Research paper thumbnail of Robust Rhythmogenesis in the Gamma Band via Spike Timing Dependent Plasticity" class="work-thumbnail" src="https://attachments.academia-assets.com/97942840/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/95888233/Robust_Rhythmogenesis_in_the_Gamma_Band_via_Spike_Timing_Dependent_Plasticity">Robust Rhythmogenesis in the Gamma Band via Spike Timing Dependent Plasticity</a></div><div class="wp-workCard_item"><span>bioRxiv</span><span>, 2020</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Rhythmic activity in the gamma band (30-100Hz) has been observed in numerous animal species rangi...</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">Rhythmic activity in the gamma band (30-100Hz) has been observed in numerous animal species ranging from insects to humans, and in relation to a wide range of cognitive tasks. Various experimental and theoretical studies have investigated this rhythmic activity. The theoretical efforts have mainly been focused on the neuronal dynamics, under the assumption that network connectivity satisfies certain fine-tuning conditions required to generate gamma oscillations. However, it remains unclear how this fine tuning is achieved. Here we investigated the hypothesis that spike timing dependent plasticity (STDP) can provide the underlying mechanism for tuning synaptic connectivity to generate rhythmic activity in the gamma band. We addressed this question in a modeling study. We examined STDP dynamics in the framework of a network of excitatory and inhibitory neuronal populations that has been suggested to underlie the generation of gamma. Mean field Fokker Planck equations for the synaptic ...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="3344588001b0a76635bc518f4a4cfee2" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":97942840,"asset_id":95888233,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/97942840/download_file?st=MTczMjgzMTA2MSw4LjIyMi4yMDguMTQ2&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="95888233"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="95888233"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 95888233; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=95888233]").text(description); $(".js-view-count[data-work-id=95888233]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 95888233; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='95888233']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 95888233, container: "", }); });</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-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.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: "3344588001b0a76635bc518f4a4cfee2" } } $('.js-work-strip[data-work-id=95888233]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":95888233,"title":"Robust Rhythmogenesis in the Gamma Band via Spike Timing Dependent Plasticity","translated_title":"","metadata":{"abstract":"Rhythmic activity in the gamma band (30-100Hz) has been observed in numerous animal species ranging from insects to humans, and in relation to a wide range of cognitive tasks. Various experimental and theoretical studies have investigated this rhythmic activity. The theoretical efforts have mainly been focused on the neuronal dynamics, under the assumption that network connectivity satisfies certain fine-tuning conditions required to generate gamma oscillations. However, it remains unclear how this fine tuning is achieved. Here we investigated the hypothesis that spike timing dependent plasticity (STDP) can provide the underlying mechanism for tuning synaptic connectivity to generate rhythmic activity in the gamma band. We addressed this question in a modeling study. We examined STDP dynamics in the framework of a network of excitatory and inhibitory neuronal populations that has been suggested to underlie the generation of gamma. Mean field Fokker Planck equations for the synaptic ...","publisher":"bioRxiv","publication_date":{"day":null,"month":null,"year":2020,"errors":{}},"publication_name":"bioRxiv"},"translated_abstract":"Rhythmic activity in the gamma band (30-100Hz) has been observed in numerous animal species ranging from insects to humans, and in relation to a wide range of cognitive tasks. Various experimental and theoretical studies have investigated this rhythmic activity. The theoretical efforts have mainly been focused on the neuronal dynamics, under the assumption that network connectivity satisfies certain fine-tuning conditions required to generate gamma oscillations. However, it remains unclear how this fine tuning is achieved. Here we investigated the hypothesis that spike timing dependent plasticity (STDP) can provide the underlying mechanism for tuning synaptic connectivity to generate rhythmic activity in the gamma band. We addressed this question in a modeling study. We examined STDP dynamics in the framework of a network of excitatory and inhibitory neuronal populations that has been suggested to underlie the generation of gamma. Mean field Fokker Planck equations for the synaptic ...","internal_url":"https://www.academia.edu/95888233/Robust_Rhythmogenesis_in_the_Gamma_Band_via_Spike_Timing_Dependent_Plasticity","translated_internal_url":"","created_at":"2023-01-29T00:57:30.165-08:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":246424110,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[{"id":97942840,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/97942840/thumbnails/1.jpg","file_name":"2020.07.23.217026v1.full.pdf","download_url":"https://www.academia.edu/attachments/97942840/download_file?st=MTczMjgzMTA2MSw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Robust_Rhythmogenesis_in_the_Gamma_Band.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/97942840/2020.07.23.217026v1.full-libre.pdf?1674994218=\u0026response-content-disposition=attachment%3B+filename%3DRobust_Rhythmogenesis_in_the_Gamma_Band.pdf\u0026Expires=1732834661\u0026Signature=DSKKnSl-~FXDFNvsciGU72mMlKoLHdWBHx0hOJtm3so~ozl2iGzxn4ikelivkios5ynVSoqFUWDGKmqqAOHcIOeyhn9sfxgOy0nc84tgJZHfbE11f8bsFKOZfhYDbsPwECUH-eOKDfHySG3m2PzRzl0qa0XhMe3FSdBY44-lRLzZK4z8uJRRKoj7UykzVTE1ncuLgjRbfADEgEl8sCSZOO~etv5FtMjuNPv0b6F02WGTZl8w71QOAB9SwN8re7eLP4DE4xJlpoRL32jR5cA2rmoD-XykQcBA~wVYoUikLYCfr-Sp1YKSuogdK9qKjcs2dt1zUYB7I1n8OYDmCuCfMw__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Robust_Rhythmogenesis_in_the_Gamma_Band_via_Spike_Timing_Dependent_Plasticity","translated_slug":"","page_count":9,"language":"en","content_type":"Work","owner":{"id":246424110,"first_name":"Maoz","middle_initials":null,"last_name":"Shamir","page_name":"MaozShamir","domain_name":"bgu","created_at":"2022-11-20T23:39:36.274-08:00","display_name":"Maoz Shamir","url":"https://bgu.academia.edu/MaozShamir"},"attachments":[{"id":97942840,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/97942840/thumbnails/1.jpg","file_name":"2020.07.23.217026v1.full.pdf","download_url":"https://www.academia.edu/attachments/97942840/download_file?st=MTczMjgzMTA2MSw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Robust_Rhythmogenesis_in_the_Gamma_Band.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/97942840/2020.07.23.217026v1.full-libre.pdf?1674994218=\u0026response-content-disposition=attachment%3B+filename%3DRobust_Rhythmogenesis_in_the_Gamma_Band.pdf\u0026Expires=1732834661\u0026Signature=DSKKnSl-~FXDFNvsciGU72mMlKoLHdWBHx0hOJtm3so~ozl2iGzxn4ikelivkios5ynVSoqFUWDGKmqqAOHcIOeyhn9sfxgOy0nc84tgJZHfbE11f8bsFKOZfhYDbsPwECUH-eOKDfHySG3m2PzRzl0qa0XhMe3FSdBY44-lRLzZK4z8uJRRKoj7UykzVTE1ncuLgjRbfADEgEl8sCSZOO~etv5FtMjuNPv0b6F02WGTZl8w71QOAB9SwN8re7eLP4DE4xJlpoRL32jR5cA2rmoD-XykQcBA~wVYoUikLYCfr-Sp1YKSuogdK9qKjcs2dt1zUYB7I1n8OYDmCuCfMw__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":161,"name":"Neuroscience","url":"https://www.academia.edu/Documents/in/Neuroscience"},{"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":3886,"name":"Rhythm","url":"https://www.academia.edu/Documents/in/Rhythm"},{"id":7710,"name":"Biology","url":"https://www.academia.edu/Documents/in/Biology"},{"id":210122,"name":"Robustness (evolution)","url":"https://www.academia.edu/Documents/in/Robustness_evolution_"}],"urls":[{"id":28486452,"url":"https://www.biorxiv.org/content/10.1101/2020.07.23.217026v1.full.pdf"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="95888232"><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/95888232/STDP_and_the_distribution_of_preferred_phases_in_the_whisker_system"><img alt="Research paper thumbnail of STDP and the distribution of preferred phases in the whisker system" class="work-thumbnail" src="https://attachments.academia-assets.com/97942865/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/95888232/STDP_and_the_distribution_of_preferred_phases_in_the_whisker_system">STDP and the distribution of preferred phases in the whisker system</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">Rats and mice use their whiskers to probe the environment. By rhythmically swiping their whiskers...</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">Rats and mice use their whiskers to probe the environment. By rhythmically swiping their whiskers back and forth they can detect the existence of an object, locate it, and identify its texture. Localization can be accomplished by inferring the whisker鈥檚 position. Rhythmic neurons that track the phase of the whisking cycle encode information about the azimuthal location of the whisker. These neurons are characterized by preferred phases of firing that are narrowly distributed. Consequently, pooling the rhythmic signal from several upstream neurons is expected to result in a much narrower distribution of preferred phases in the downstream population, which however has not been observed empirically. Here, we show how spike timing dependent plasticity (STDP) can provide a solution to this conundrum. We investigated the effect of STDP on the utility of a neural population to transmit rhythmic information downstream using the framework of a modeling study. We found that under a wide range...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="914496609b565405c3d1054f5ac69819" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":97942865,"asset_id":95888232,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/97942865/download_file?st=MTczMjgzMTA2MSw4LjIyMi4yMDguMTQ2&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="95888232"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="95888232"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 95888232; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=95888232]").text(description); $(".js-view-count[data-work-id=95888232]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 95888232; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='95888232']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 95888232, container: "", }); });</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-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.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: "914496609b565405c3d1054f5ac69819" } } $('.js-work-strip[data-work-id=95888232]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":95888232,"title":"STDP and the distribution of preferred phases in the whisker system","translated_title":"","metadata":{"abstract":"Rats and mice use their whiskers to probe the environment. By rhythmically swiping their whiskers back and forth they can detect the existence of an object, locate it, and identify its texture. Localization can be accomplished by inferring the whisker鈥檚 position. Rhythmic neurons that track the phase of the whisking cycle encode information about the azimuthal location of the whisker. These neurons are characterized by preferred phases of firing that are narrowly distributed. Consequently, pooling the rhythmic signal from several upstream neurons is expected to result in a much narrower distribution of preferred phases in the downstream population, which however has not been observed empirically. Here, we show how spike timing dependent plasticity (STDP) can provide a solution to this conundrum. We investigated the effect of STDP on the utility of a neural population to transmit rhythmic information downstream using the framework of a modeling study. We found that under a wide range...","publisher":"Public Library of Science (PLoS)","publication_date":{"day":null,"month":null,"year":2021,"errors":{}},"publication_name":"PLOS Computational Biology"},"translated_abstract":"Rats and mice use their whiskers to probe the environment. By rhythmically swiping their whiskers back and forth they can detect the existence of an object, locate it, and identify its texture. Localization can be accomplished by inferring the whisker鈥檚 position. Rhythmic neurons that track the phase of the whisking cycle encode information about the azimuthal location of the whisker. These neurons are characterized by preferred phases of firing that are narrowly distributed. Consequently, pooling the rhythmic signal from several upstream neurons is expected to result in a much narrower distribution of preferred phases in the downstream population, which however has not been observed empirically. Here, we show how spike timing dependent plasticity (STDP) can provide a solution to this conundrum. We investigated the effect of STDP on the utility of a neural population to transmit rhythmic information downstream using the framework of a modeling study. We found that under a wide range...","internal_url":"https://www.academia.edu/95888232/STDP_and_the_distribution_of_preferred_phases_in_the_whisker_system","translated_internal_url":"","created_at":"2023-01-29T00:57:29.990-08:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":246424110,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[{"id":97942865,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/97942865/thumbnails/1.jpg","file_name":"pcbi.1009353.pdf","download_url":"https://www.academia.edu/attachments/97942865/download_file?st=MTczMjgzMTA2MSw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"STDP_and_the_distribution_of_preferred_p.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/97942865/pcbi.1009353-libre.pdf?1674994714=\u0026response-content-disposition=attachment%3B+filename%3DSTDP_and_the_distribution_of_preferred_p.pdf\u0026Expires=1732834661\u0026Signature=DOpGWuck3SumQdTNXS9P6julnWT8PByYMOzVGxfEelln2FCMaZwCqcPmTWNpKtmFHmK8aolnJxjB-HrVMD5vH1nNxeJktzMXC1fSuSdoDvWODq3ALqYyeuuottRdMjKDK-32suqN8d5-hx1NBtrky2oYh6nlrot9-GoamHGrb2VQLA~0IU32wkvYqk7KcLv13CNpMIuRKKnR1ogQ84YAtefsEammDso0dgVsXi8Mzf1T2GBISihfGq4tRyvkDbhQth1vV3LUQZDRi~ucu~7KJ51rF15G5zAaUGe1emTKmFMwm35d-9Vxc-1oLUZwiEDHQiUMG9ARLW~~4xXRhtEL~Q__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"STDP_and_the_distribution_of_preferred_phases_in_the_whisker_system","translated_slug":"","page_count":18,"language":"en","content_type":"Work","owner":{"id":246424110,"first_name":"Maoz","middle_initials":null,"last_name":"Shamir","page_name":"MaozShamir","domain_name":"bgu","created_at":"2022-11-20T23:39:36.274-08:00","display_name":"Maoz Shamir","url":"https://bgu.academia.edu/MaozShamir"},"attachments":[{"id":97942865,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/97942865/thumbnails/1.jpg","file_name":"pcbi.1009353.pdf","download_url":"https://www.academia.edu/attachments/97942865/download_file?st=MTczMjgzMTA2MSw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"STDP_and_the_distribution_of_preferred_p.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/97942865/pcbi.1009353-libre.pdf?1674994714=\u0026response-content-disposition=attachment%3B+filename%3DSTDP_and_the_distribution_of_preferred_p.pdf\u0026Expires=1732834661\u0026Signature=DOpGWuck3SumQdTNXS9P6julnWT8PByYMOzVGxfEelln2FCMaZwCqcPmTWNpKtmFHmK8aolnJxjB-HrVMD5vH1nNxeJktzMXC1fSuSdoDvWODq3ALqYyeuuottRdMjKDK-32suqN8d5-hx1NBtrky2oYh6nlrot9-GoamHGrb2VQLA~0IU32wkvYqk7KcLv13CNpMIuRKKnR1ogQ84YAtefsEammDso0dgVsXi8Mzf1T2GBISihfGq4tRyvkDbhQth1vV3LUQZDRi~ucu~7KJ51rF15G5zAaUGe1emTKmFMwm35d-9Vxc-1oLUZwiEDHQiUMG9ARLW~~4xXRhtEL~Q__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science"},{"id":3886,"name":"Rhythm","url":"https://www.academia.edu/Documents/in/Rhythm"},{"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":64336,"name":"Population","url":"https://www.academia.edu/Documents/in/Population"},{"id":80414,"name":"Mathematical Sciences","url":"https://www.academia.edu/Documents/in/Mathematical_Sciences"},{"id":207853,"name":"Pooling","url":"https://www.academia.edu/Documents/in/Pooling"},{"id":2249025,"name":"Whisker","url":"https://www.academia.edu/Documents/in/Whisker"},{"id":2273259,"name":"Biological System","url":"https://www.academia.edu/Documents/in/Biological_System"}],"urls":[{"id":28486451,"url":"https://dx.plos.org/10.1371/journal.pcbi.1009353"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="95888231"><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/95888231/Robust_Rhythmogenesis_via_Spike_Timing_Dependent_Plasticity"><img alt="Research paper thumbnail of Robust Rhythmogenesis via Spike Timing Dependent Plasticity" class="work-thumbnail" src="https://attachments.academia-assets.com/97942861/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/95888231/Robust_Rhythmogenesis_via_Spike_Timing_Dependent_Plasticity">Robust Rhythmogenesis via Spike Timing Dependent Plasticity</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Rhythmic activity has been observed in numerous animal species ranging from insects to humans, an...</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">Rhythmic activity has been observed in numerous animal species ranging from insects to humans, and in relation to a wide range of cognitive tasks. Various experimental and theoretical studies have investigated rhythmic activity. The theoretical efforts have mainly been focused on the neuronal dynamics, under the assumption that network connectivity satisfies certain fine-tuning conditions required to generate oscillations. However, it remains unclear how this fine tuning is achieved.Here we investigated the hypothesis that spike timing dependent plasticity (STDP) can provide the underlying mechanism for tuning synaptic connectivity to generate rhythmic activity. We addressed this question in a modeling study. We examined STDP dynamics in the framework of a network of excitatory and inhibitory neuronal populations that has been suggested to underlie the generation of oscillations in the gamma range. Mean field Fokker Planck equations for the synaptic weights dynamics are derived in t...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="f3d8f9b20e25099addcbff09525fa41c" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":97942861,"asset_id":95888231,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/97942861/download_file?st=MTczMjgzMTA2MSw4LjIyMi4yMDguMTQ2&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="95888231"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="95888231"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 95888231; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=95888231]").text(description); $(".js-view-count[data-work-id=95888231]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 95888231; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='95888231']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 95888231, container: "", }); });</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-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.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: "f3d8f9b20e25099addcbff09525fa41c" } } $('.js-work-strip[data-work-id=95888231]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":95888231,"title":"Robust Rhythmogenesis via Spike Timing Dependent Plasticity","translated_title":"","metadata":{"abstract":"Rhythmic activity has been observed in numerous animal species ranging from insects to humans, and in relation to a wide range of cognitive tasks. Various experimental and theoretical studies have investigated rhythmic activity. The theoretical efforts have mainly been focused on the neuronal dynamics, under the assumption that network connectivity satisfies certain fine-tuning conditions required to generate oscillations. However, it remains unclear how this fine tuning is achieved.Here we investigated the hypothesis that spike timing dependent plasticity (STDP) can provide the underlying mechanism for tuning synaptic connectivity to generate rhythmic activity. We addressed this question in a modeling study. We examined STDP dynamics in the framework of a network of excitatory and inhibitory neuronal populations that has been suggested to underlie the generation of oscillations in the gamma range. Mean field Fokker Planck equations for the synaptic weights dynamics are derived in t...","publisher":"Cold Spring Harbor Laboratory","publication_date":{"day":null,"month":null,"year":2020,"errors":{}}},"translated_abstract":"Rhythmic activity has been observed in numerous animal species ranging from insects to humans, and in relation to a wide range of cognitive tasks. Various experimental and theoretical studies have investigated rhythmic activity. The theoretical efforts have mainly been focused on the neuronal dynamics, under the assumption that network connectivity satisfies certain fine-tuning conditions required to generate oscillations. However, it remains unclear how this fine tuning is achieved.Here we investigated the hypothesis that spike timing dependent plasticity (STDP) can provide the underlying mechanism for tuning synaptic connectivity to generate rhythmic activity. We addressed this question in a modeling study. We examined STDP dynamics in the framework of a network of excitatory and inhibitory neuronal populations that has been suggested to underlie the generation of oscillations in the gamma range. Mean field Fokker Planck equations for the synaptic weights dynamics are derived in t...","internal_url":"https://www.academia.edu/95888231/Robust_Rhythmogenesis_via_Spike_Timing_Dependent_Plasticity","translated_internal_url":"","created_at":"2023-01-29T00:57:29.823-08:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":246424110,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[{"id":97942861,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/97942861/thumbnails/1.jpg","file_name":"2020.07.23.217026v1.full.pdf","download_url":"https://www.academia.edu/attachments/97942861/download_file?st=MTczMjgzMTA2MSw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Robust_Rhythmogenesis_via_Spike_Timing_D.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/97942861/2020.07.23.217026v1.full-libre.pdf?1674994220=\u0026response-content-disposition=attachment%3B+filename%3DRobust_Rhythmogenesis_via_Spike_Timing_D.pdf\u0026Expires=1732834661\u0026Signature=Rv1WMR4lGhBK2V3ho3V0RRE3rA4JwZQzc-6xcTDCDm00mu2F-uLBv3zpRg-KBJ~araIodpHNud8AYw8194AT~XVLxpA2iSgwPhGQ4tGuYiQ8~uMTQlNHIyey5MCpvrKsOoSQTjkv5GZ6XeLArSQi1AFFZLHgW8FvyY51WueHS6u8szbR08oFl~61zmOCiya5oE9AoQ9muE6F0iiCpQMKf6jattcTCd~gAidKsa~t1R6LQzYfwONquA8H9NYmX99UMZAET7XEg6PXdrdd8XRoGedbNtuxzWjG1xckU0XiYT3dERYCKX~g6o0CHLWSyAMCPDzOpU1QH7xAwFOnxDj6Xw__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Robust_Rhythmogenesis_via_Spike_Timing_Dependent_Plasticity","translated_slug":"","page_count":9,"language":"en","content_type":"Work","owner":{"id":246424110,"first_name":"Maoz","middle_initials":null,"last_name":"Shamir","page_name":"MaozShamir","domain_name":"bgu","created_at":"2022-11-20T23:39:36.274-08:00","display_name":"Maoz Shamir","url":"https://bgu.academia.edu/MaozShamir"},"attachments":[{"id":97942861,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/97942861/thumbnails/1.jpg","file_name":"2020.07.23.217026v1.full.pdf","download_url":"https://www.academia.edu/attachments/97942861/download_file?st=MTczMjgzMTA2MSw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Robust_Rhythmogenesis_via_Spike_Timing_D.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/97942861/2020.07.23.217026v1.full-libre.pdf?1674994220=\u0026response-content-disposition=attachment%3B+filename%3DRobust_Rhythmogenesis_via_Spike_Timing_D.pdf\u0026Expires=1732834661\u0026Signature=Rv1WMR4lGhBK2V3ho3V0RRE3rA4JwZQzc-6xcTDCDm00mu2F-uLBv3zpRg-KBJ~araIodpHNud8AYw8194AT~XVLxpA2iSgwPhGQ4tGuYiQ8~uMTQlNHIyey5MCpvrKsOoSQTjkv5GZ6XeLArSQi1AFFZLHgW8FvyY51WueHS6u8szbR08oFl~61zmOCiya5oE9AoQ9muE6F0iiCpQMKf6jattcTCd~gAidKsa~t1R6LQzYfwONquA8H9NYmX99UMZAET7XEg6PXdrdd8XRoGedbNtuxzWjG1xckU0XiYT3dERYCKX~g6o0CHLWSyAMCPDzOpU1QH7xAwFOnxDj6Xw__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":161,"name":"Neuroscience","url":"https://www.academia.edu/Documents/in/Neuroscience"},{"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":3886,"name":"Rhythm","url":"https://www.academia.edu/Documents/in/Rhythm"},{"id":7710,"name":"Biology","url":"https://www.academia.edu/Documents/in/Biology"},{"id":210122,"name":"Robustness (evolution)","url":"https://www.academia.edu/Documents/in/Robustness_evolution_"}],"urls":[{"id":28486450,"url":"https://syndication.highwire.org/content/doi/10.1101/2020.07.23.217026"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="95888229"><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/95888229/Multiplexing_rhythmic_information_by_spike_timing_dependent_plasticity"><img alt="Research paper thumbnail of Multiplexing rhythmic information by spike timing dependent plasticity" class="work-thumbnail" src="https://attachments.academia-assets.com/97942859/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/95888229/Multiplexing_rhythmic_information_by_spike_timing_dependent_plasticity">Multiplexing rhythmic information by spike timing dependent plasticity</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Rhythmic activity has been associated with a wide range of cognitive processes including the enco...</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">Rhythmic activity has been associated with a wide range of cognitive processes including the encoding of sensory information, navigation, the transfer of emotional information and others. Previous studies have shown that spike-timing-dependent plasticity (STDP) can facilitate the transfer of rhythmic activity downstream the information processing pathway. However, STDP has also been known to generate strong winner-take-all like competitions between subgroups of correlated synaptic inputs. Consequently, one might expect that STDP would induce strong competition between different rhythmicity channels thus preventing the multiplexing of information across different frequency channels. This study explored whether STDP facilitates the multiplexing of information across multiple frequency channels, and if so, under what conditions. We investigated the STDP dynamics in the framework of a model consisting of two competing sub-populations of neurons that synapse in a feedforward manner onto ...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="4fc9c2ea612e53d5b7949bb06270b9ee" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":97942859,"asset_id":95888229,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/97942859/download_file?st=MTczMjgzMTA2MSw4LjIyMi4yMDguMTQ2&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="95888229"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="95888229"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 95888229; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=95888229]").text(description); $(".js-view-count[data-work-id=95888229]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 95888229; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='95888229']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 95888229, container: "", }); });</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-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.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: "4fc9c2ea612e53d5b7949bb06270b9ee" } } $('.js-work-strip[data-work-id=95888229]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":95888229,"title":"Multiplexing rhythmic information by spike timing dependent plasticity","translated_title":"","metadata":{"abstract":"Rhythmic activity has been associated with a wide range of cognitive processes including the encoding of sensory information, navigation, the transfer of emotional information and others. Previous studies have shown that spike-timing-dependent plasticity (STDP) can facilitate the transfer of rhythmic activity downstream the information processing pathway. However, STDP has also been known to generate strong winner-take-all like competitions between subgroups of correlated synaptic inputs. Consequently, one might expect that STDP would induce strong competition between different rhythmicity channels thus preventing the multiplexing of information across different frequency channels. This study explored whether STDP facilitates the multiplexing of information across multiple frequency channels, and if so, under what conditions. We investigated the STDP dynamics in the framework of a model consisting of two competing sub-populations of neurons that synapse in a feedforward manner onto ...","publisher":"Cold Spring Harbor Laboratory","publication_date":{"day":null,"month":null,"year":2019,"errors":{}}},"translated_abstract":"Rhythmic activity has been associated with a wide range of cognitive processes including the encoding of sensory information, navigation, the transfer of emotional information and others. Previous studies have shown that spike-timing-dependent plasticity (STDP) can facilitate the transfer of rhythmic activity downstream the information processing pathway. However, STDP has also been known to generate strong winner-take-all like competitions between subgroups of correlated synaptic inputs. Consequently, one might expect that STDP would induce strong competition between different rhythmicity channels thus preventing the multiplexing of information across different frequency channels. This study explored whether STDP facilitates the multiplexing of information across multiple frequency channels, and if so, under what conditions. We investigated the STDP dynamics in the framework of a model consisting of two competing sub-populations of neurons that synapse in a feedforward manner onto ...","internal_url":"https://www.academia.edu/95888229/Multiplexing_rhythmic_information_by_spike_timing_dependent_plasticity","translated_internal_url":"","created_at":"2023-01-29T00:57:29.611-08:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":246424110,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[{"id":97942859,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/97942859/thumbnails/1.jpg","file_name":"1911.pdf","download_url":"https://www.academia.edu/attachments/97942859/download_file?st=MTczMjgzMTA2MSw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Multiplexing_rhythmic_information_by_spi.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/97942859/1911-libre.pdf?1674994233=\u0026response-content-disposition=attachment%3B+filename%3DMultiplexing_rhythmic_information_by_spi.pdf\u0026Expires=1732834661\u0026Signature=Hex5~xLgnmCVHncofFlDsdTbc0YiwS8N-yUfGXR38t~qiMYVQ1Jqb1oN7ivCORa1O1w0hc3rSJYq5RDKpwfx4Gc7lkc8HVmtfEYK0YgOl0BzmKQg~I-Rx1uwerroZBBE4-W15OBa5frqtaadU08H8LPAwmDh6bP5DFOsE9La5NqVqCk0ZC0eJcWr0t8SXn6cx~1IBw5Qa686WRGmhGLLaOr7nUU1exY46M3FVI96PJrBXAJfAAjSB1ZegFH5p0N~Wy4eFa78ac-s5xtCA0v0cAVJoOtRDFnrdCWqBg8LBOO5PR7ocf2sBzes~Y4ziOTf6HcZCn3Wrm9xCycPu3eNDw__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Multiplexing_rhythmic_information_by_spike_timing_dependent_plasticity","translated_slug":"","page_count":14,"language":"en","content_type":"Work","owner":{"id":246424110,"first_name":"Maoz","middle_initials":null,"last_name":"Shamir","page_name":"MaozShamir","domain_name":"bgu","created_at":"2022-11-20T23:39:36.274-08:00","display_name":"Maoz Shamir","url":"https://bgu.academia.edu/MaozShamir"},"attachments":[{"id":97942859,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/97942859/thumbnails/1.jpg","file_name":"1911.pdf","download_url":"https://www.academia.edu/attachments/97942859/download_file?st=MTczMjgzMTA2MSw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Multiplexing_rhythmic_information_by_spi.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/97942859/1911-libre.pdf?1674994233=\u0026response-content-disposition=attachment%3B+filename%3DMultiplexing_rhythmic_information_by_spi.pdf\u0026Expires=1732834661\u0026Signature=Hex5~xLgnmCVHncofFlDsdTbc0YiwS8N-yUfGXR38t~qiMYVQ1Jqb1oN7ivCORa1O1w0hc3rSJYq5RDKpwfx4Gc7lkc8HVmtfEYK0YgOl0BzmKQg~I-Rx1uwerroZBBE4-W15OBa5frqtaadU08H8LPAwmDh6bP5DFOsE9La5NqVqCk0ZC0eJcWr0t8SXn6cx~1IBw5Qa686WRGmhGLLaOr7nUU1exY46M3FVI96PJrBXAJfAAjSB1ZegFH5p0N~Wy4eFa78ac-s5xtCA0v0cAVJoOtRDFnrdCWqBg8LBOO5PR7ocf2sBzes~Y4ziOTf6HcZCn3Wrm9xCycPu3eNDw__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":161,"name":"Neuroscience","url":"https://www.academia.edu/Documents/in/Neuroscience"},{"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":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":118599,"name":"Information Transfer","url":"https://www.academia.edu/Documents/in/Information_Transfer"},{"id":4110287,"name":"Feed Forward","url":"https://www.academia.edu/Documents/in/Feed_Forward"}],"urls":[{"id":28486449,"url":"https://syndication.highwire.org/content/doi/10.1101/855965"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="95888228"><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/95888228/Relating_the_Structure_of_Noise_Correlations_in_Macaque_Primary_Visual_Cortex_to_Decoder_Performance"><img alt="Research paper thumbnail of Relating the Structure of Noise Correlations in Macaque Primary Visual Cortex to Decoder Performance" class="work-thumbnail" src="https://attachments.academia-assets.com/97942864/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/95888228/Relating_the_Structure_of_Noise_Correlations_in_Macaque_Primary_Visual_Cortex_to_Decoder_Performance">Relating the Structure of Noise Correlations in Macaque Primary Visual Cortex to Decoder Performance</a></div><div class="wp-workCard_item"><span>Frontiers in Computational Neuroscience</span><span>, 2018</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="fc3c951765de164cdff90db603179ff2" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":97942864,"asset_id":95888228,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/97942864/download_file?st=MTczMjgzMTA2MSw4LjIyMi4yMDguMTQ2&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="95888228"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="95888228"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 95888228; 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="95888227"><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/95888227/Theories_of_rhythmogenesis"><img alt="Research paper thumbnail of Theories of rhythmogenesis" 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"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/95888227/Theories_of_rhythmogenesis">Theories of rhythmogenesis</a></div><div class="wp-workCard_item"><span>Current Opinion in Neurobiology</span><span>, 2019</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Rhythmogenesis is the process that develops the capacity for rhythmic activity in a non-rhythmic ...</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">Rhythmogenesis is the process that develops the capacity for rhythmic activity in a non-rhythmic system. Theoretical works suggested a wide array of possible mechanisms for rhythmogenesis ranging from the regulation of cellular properties to top-down control. Here we discuss theories of rhythmogenesis with an emphasis on spike timing-dependent plasticity. We argue that even though the specifics of different mechanisms vary greatly they all share certain key features. Namely, rhythmogenesis can be described as a flow on the phase diagram leading the system into a rhythmic region and stabilizing it on a specific manifold characterized by the desired rhythmic activity. Functionality is retained despite biological diversity by forcing the system into a specific manifold, but allowing fluctuations within that manifold.</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="95888227"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="95888227"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 95888227; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=95888227]").text(description); $(".js-view-count[data-work-id=95888227]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 95888227; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='95888227']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 95888227, container: "", }); });</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-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.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" } } $('.js-work-strip[data-work-id=95888227]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":95888227,"title":"Theories of rhythmogenesis","translated_title":"","metadata":{"abstract":"Rhythmogenesis is the process that develops the capacity for rhythmic activity in a non-rhythmic system. Theoretical works suggested a wide array of possible mechanisms for rhythmogenesis ranging from the regulation of cellular properties to top-down control. Here we discuss theories of rhythmogenesis with an emphasis on spike timing-dependent plasticity. We argue that even though the specifics of different mechanisms vary greatly they all share certain key features. Namely, rhythmogenesis can be described as a flow on the phase diagram leading the system into a rhythmic region and stabilizing it on a specific manifold characterized by the desired rhythmic activity. Functionality is retained despite biological diversity by forcing the system into a specific manifold, but allowing fluctuations within that manifold.","publisher":"Elsevier BV","publication_date":{"day":null,"month":null,"year":2019,"errors":{}},"publication_name":"Current Opinion in Neurobiology"},"translated_abstract":"Rhythmogenesis is the process that develops the capacity for rhythmic activity in a non-rhythmic system. Theoretical works suggested a wide array of possible mechanisms for rhythmogenesis ranging from the regulation of cellular properties to top-down control. Here we discuss theories of rhythmogenesis with an emphasis on spike timing-dependent plasticity. We argue that even though the specifics of different mechanisms vary greatly they all share certain key features. Namely, rhythmogenesis can be described as a flow on the phase diagram leading the system into a rhythmic region and stabilizing it on a specific manifold characterized by the desired rhythmic activity. Functionality is retained despite biological diversity by forcing the system into a specific manifold, but allowing fluctuations within that manifold.","internal_url":"https://www.academia.edu/95888227/Theories_of_rhythmogenesis","translated_internal_url":"","created_at":"2023-01-29T00:57:29.076-08:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":246424110,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[],"slug":"Theories_of_rhythmogenesis","translated_slug":"","page_count":null,"language":"en","content_type":"Work","owner":{"id":246424110,"first_name":"Maoz","middle_initials":null,"last_name":"Shamir","page_name":"MaozShamir","domain_name":"bgu","created_at":"2022-11-20T23:39:36.274-08:00","display_name":"Maoz Shamir","url":"https://bgu.academia.edu/MaozShamir"},"attachments":[],"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":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science"},{"id":26327,"name":"Medicine","url":"https://www.academia.edu/Documents/in/Medicine"},{"id":1239755,"name":"Neurosciences","url":"https://www.academia.edu/Documents/in/Neurosciences"}],"urls":[{"id":28486447,"url":"https://api.elsevier.com/content/article/PII:S0959438818302241?httpAccept=text/xml"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="95888226"><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/95888226/Rhythmogenesis_evolves_as_a_consequence_of_long_term_plasticity_of_inhibitory_synapses"><img alt="Research paper thumbnail of Rhythmogenesis evolves as a consequence of long-term plasticity of inhibitory synapses" class="work-thumbnail" src="https://attachments.academia-assets.com/97942839/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/95888226/Rhythmogenesis_evolves_as_a_consequence_of_long_term_plasticity_of_inhibitory_synapses">Rhythmogenesis evolves as a consequence of long-term plasticity of inhibitory synapses</a></div><div class="wp-workCard_item"><span>Scientific Reports</span><span>, 2018</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Brain rhythms are widely believed to reflect numerous cognitive processes. Changes in rhythmicity...</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">Brain rhythms are widely believed to reflect numerous cognitive processes. Changes in rhythmicity have been associated with pathological states. However, the mechanism underlying these rhythms remains unknown. Here, we present a theoretical analysis of the evolvement of rhythm generating capabilities in neuronal circuits. We tested the hypothesis that brain rhythms can be acquired via an intrinsic unsupervised learning process of activity dependent plasticity. Specifically, we focused on spike timing dependent plasticity (STDP) of inhibitory synapses. We detail how rhythmicity can develop via STDP under certain conditions that serve as a natural prediction of the hypothesis. We show how global features of the STDP rule govern and stabilize the resultant rhythmic activity. Finally, we demonstrate how rhythmicity is retained even in the face of synaptic variability. This study suggests a role for inhibitory plasticity that is beyond homeostatic processes.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="fd13c95e0a134bdea4b057cb903796a8" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":97942839,"asset_id":95888226,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/97942839/download_file?st=MTczMjgzMTA2MSw4LjIyMi4yMDguMTQ2&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="95888226"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="95888226"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 95888226; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=95888226]").text(description); $(".js-view-count[data-work-id=95888226]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 95888226; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='95888226']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 95888226, container: "", }); });</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-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.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: "fd13c95e0a134bdea4b057cb903796a8" } } $('.js-work-strip[data-work-id=95888226]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":95888226,"title":"Rhythmogenesis evolves as a consequence of long-term plasticity of inhibitory synapses","translated_title":"","metadata":{"abstract":"Brain rhythms are widely believed to reflect numerous cognitive processes. 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This study suggests a role for inhibitory plasticity that is beyond homeostatic processes.","publisher":"Springer Science and Business Media LLC","publication_date":{"day":null,"month":null,"year":2018,"errors":{}},"publication_name":"Scientific Reports"},"translated_abstract":"Brain rhythms are widely believed to reflect numerous cognitive processes. Changes in rhythmicity have been associated with pathological states. However, the mechanism underlying these rhythms remains unknown. Here, we present a theoretical analysis of the evolvement of rhythm generating capabilities in neuronal circuits. We tested the hypothesis that brain rhythms can be acquired via an intrinsic unsupervised learning process of activity dependent plasticity. Specifically, we focused on spike timing dependent plasticity (STDP) of inhibitory synapses. We detail how rhythmicity can develop via STDP under certain conditions that serve as a natural prediction of the hypothesis. We show how global features of the STDP rule govern and stabilize the resultant rhythmic activity. Finally, we demonstrate how rhythmicity is retained even in the face of synaptic variability. 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="95888225"><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/95888225/Emergence_of_oscillations_via_spike_timing_dependent_plasticity"><img alt="Research paper thumbnail of Emergence of oscillations via spike timing dependent plasticity" class="work-thumbnail" src="https://attachments.academia-assets.com/97942858/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/95888225/Emergence_of_oscillations_via_spike_timing_dependent_plasticity">Emergence of oscillations via spike timing dependent plasticity</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Neuronal oscillatory activity has been reported in relation to a wide range of cognitive processe...</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">Neuronal oscillatory activity has been reported in relation to a wide range of cognitive processes. In certain cases changes in oscillatory activity has been associated with pathological states. Although the specific role of these oscillations has yet to be determined, it is clear that neuronal oscillations are abundant in the central nervous system. These observations raise the question of the origin of these oscillations; and specifically whether the mechanisms responsible for the generation and stabilization of these oscillations are genetically hard-wired or whether they can be acquired via a learning process.Here we focus on spike timing dependent plasticity (STDP) to investigate whether oscillatory activity can emerge in a neuronal network via an unsupervised learning process of STDP dynamics, and if so, what features of the STDP learning rule govern and stabilize the resultant oscillatory activity?Here, the STDP dynamics of the effective coupling between two competing neurona...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="d6b21efc0264f4dfc6a815dd12f09c7b" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":97942858,"asset_id":95888225,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/97942858/download_file?st=MTczMjgzMTA2MSw4LjIyMi4yMDguMTQ2&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="95888225"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="95888225"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 95888225; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=95888225]").text(description); $(".js-view-count[data-work-id=95888225]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 95888225; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='95888225']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 95888225, container: "", }); });</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-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.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: "d6b21efc0264f4dfc6a815dd12f09c7b" } } $('.js-work-strip[data-work-id=95888225]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":95888225,"title":"Emergence of oscillations via spike timing dependent plasticity","translated_title":"","metadata":{"abstract":"Neuronal oscillatory activity has been reported in relation to a wide range of cognitive processes. 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These observations raise the question of the origin of these oscillations; and specifically whether the mechanisms responsible for the generation and stabilization of these oscillations are genetically hard-wired or whether they can be acquired via a learning process.Here we focus on spike timing dependent plasticity (STDP) to investigate whether oscillatory activity can emerge in a neuronal network via an unsupervised learning process of STDP dynamics, and if so, what features of the STDP learning rule govern and stabilize the resultant oscillatory activity?Here, the STDP dynamics of the effective coupling between two competing neurona...","publisher":"Cold Spring Harbor Laboratory","publication_date":{"day":null,"month":null,"year":2018,"errors":{}}},"translated_abstract":"Neuronal oscillatory activity has been reported in relation to a wide range of cognitive processes. In certain cases changes in oscillatory activity has been associated with pathological states. Although the specific role of these oscillations has yet to be determined, it is clear that neuronal oscillations are abundant in the central nervous system. These observations raise the question of the origin of these oscillations; and specifically whether the mechanisms responsible for the generation and stabilization of these oscillations are genetically hard-wired or whether they can be acquired via a learning process.Here we focus on spike timing dependent plasticity (STDP) to investigate whether oscillatory activity can emerge in a neuronal network via an unsupervised learning process of STDP dynamics, and if so, what features of the STDP learning rule govern and stabilize the resultant oscillatory activity?Here, the STDP dynamics of the effective coupling between two competing neurona...","internal_url":"https://www.academia.edu/95888225/Emergence_of_oscillations_via_spike_timing_dependent_plasticity","translated_internal_url":"","created_at":"2023-01-29T00:57:28.706-08:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":246424110,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[{"id":97942858,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/97942858/thumbnails/1.jpg","file_name":"269712.full.pdf","download_url":"https://www.academia.edu/attachments/97942858/download_file?st=MTczMjgzMTA2MSw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Emergence_of_oscillations_via_spike_timi.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/97942858/269712.full-libre.pdf?1674994232=\u0026response-content-disposition=attachment%3B+filename%3DEmergence_of_oscillations_via_spike_timi.pdf\u0026Expires=1732834661\u0026Signature=bkOIaeMjvWHA6gUCVu-DjdlfijiFbPLPj87Yh5X5q87B7cTw5oE-fu67Ym5cb1i07QIETDCfjqBNOLFs08BmT6kErSYF843YR2kP8rlCG9UrTqOO-ME5pbbobMstY0hAKhihkp~pX~g6YblHTTw1f-seSgQIUOQKD3sgeQK-9pfQ5CS2RDcSgYDnKMj77yoyyImtd7a2qappJEARjinX3aLSKNuVjivUDH78W2uVpCIioCobSb0Bn-KWMGBsjyxTcqxWRHsm7i2PV878GNmGoyEA16R0dtLuVBeqTZ9yVwCQuocXaoxLWwtFn695cjyU1KgmYhTPZOy1Z86juAv32Q__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Emergence_of_oscillations_via_spike_timing_dependent_plasticity","translated_slug":"","page_count":19,"language":"en","content_type":"Work","owner":{"id":246424110,"first_name":"Maoz","middle_initials":null,"last_name":"Shamir","page_name":"MaozShamir","domain_name":"bgu","created_at":"2022-11-20T23:39:36.274-08:00","display_name":"Maoz Shamir","url":"https://bgu.academia.edu/MaozShamir"},"attachments":[{"id":97942858,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/97942858/thumbnails/1.jpg","file_name":"269712.full.pdf","download_url":"https://www.academia.edu/attachments/97942858/download_file?st=MTczMjgzMTA2MSw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Emergence_of_oscillations_via_spike_timi.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/97942858/269712.full-libre.pdf?1674994232=\u0026response-content-disposition=attachment%3B+filename%3DEmergence_of_oscillations_via_spike_timi.pdf\u0026Expires=1732834661\u0026Signature=bkOIaeMjvWHA6gUCVu-DjdlfijiFbPLPj87Yh5X5q87B7cTw5oE-fu67Ym5cb1i07QIETDCfjqBNOLFs08BmT6kErSYF843YR2kP8rlCG9UrTqOO-ME5pbbobMstY0hAKhihkp~pX~g6YblHTTw1f-seSgQIUOQKD3sgeQK-9pfQ5CS2RDcSgYDnKMj77yoyyImtd7a2qappJEARjinX3aLSKNuVjivUDH78W2uVpCIioCobSb0Bn-KWMGBsjyxTcqxWRHsm7i2PV878GNmGoyEA16R0dtLuVBeqTZ9yVwCQuocXaoxLWwtFn695cjyU1KgmYhTPZOy1Z86juAv32Q__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":161,"name":"Neuroscience","url":"https://www.academia.edu/Documents/in/Neuroscience"},{"id":498,"name":"Physics","url":"https://www.academia.edu/Documents/in/Physics"},{"id":7710,"name":"Biology","url":"https://www.academia.edu/Documents/in/Biology"}],"urls":[{"id":28486445,"url":"https://syndication.highwire.org/content/doi/10.1101/269712"}]}, dispatcherData: dispatcherData }); 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The accuracy of latency codes was studied in the past using a simplified readout algorithm termed the temporal-winner-take-all (tWTA). The tWTA is a competitive readout algorithm in which populations of neurons with a similar decision preference compete, and the algorithm selects according to the preference of the population that reaches the decision threshold first. It has been shown that this algorithm can account for accurate decisions among a small number of alternatives during short biologically relevant time periods. However, one of the major points of criticism of latency codes has been that it is unclear how can such a readout be implemented by the central nervous system. Here we show that the solution to this long standing puzzle may be rather simple. We suggest a mechanism that is based on reciprocal inhibition architecture, similar to that of the conventional winner-take-all, and show that under a wide range of parameters this mechanism is sufficient to implement the tWTA algorithm. This is done by first analyzing a rate toy model, and demonstrating its ability to discriminate short latency differences between its inputs. We then study the sensitivity of this mechanism to fine-tuning of its initial conditions, and show that it is robust to wide range of noise levels in the initial conditions. These results are then generalized to a Hodgkin-Huxley type of neuron model, using numerical simulations. Latency codes have been criticized for requiring a reliable stimulus-onset detection mechanism as a reference for measuring latency. Here we show that this frequent assumption does not hold, and that, an additional onset estimator is not needed to trigger this simple tWTA mechanism.","publication_date":{"day":null,"month":null,"year":2016,"errors":{}},"publication_name":"Frontiers in Computational Neuroscience","grobid_abstract_attachment_id":97942856},"translated_abstract":null,"internal_url":"https://www.academia.edu/95888224/A_Readout_Mechanism_for_Latency_Codes","translated_internal_url":"","created_at":"2023-01-29T00:57:28.540-08:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":246424110,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[{"id":97942856,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/97942856/thumbnails/1.jpg","file_name":"3ec5e6a27d02cc3178373cccaac1a15ab085.pdf","download_url":"https://www.academia.edu/attachments/97942856/download_file?st=MTczMjgzMTA2MSw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"A_Readout_Mechanism_for_Latency_Codes.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/97942856/3ec5e6a27d02cc3178373cccaac1a15ab085-libre.pdf?1674994216=\u0026response-content-disposition=attachment%3B+filename%3DA_Readout_Mechanism_for_Latency_Codes.pdf\u0026Expires=1732834661\u0026Signature=SBKJdV5FV8zYncr8S7eTtvTGtKN~xsE3YR6bbbJnlqdeisS6-1VAxCWFyY0l7egUqTzpWVRHyGXnuykbUxxH-Z-AGsQ28-ArBV0~hSLBhioTnOj~aYctUWUiIdl4v8djyIbEC4esWpOYLY~P7joa~J2h-HAVlxa88t66SmO-WcNu8jypg59WXjbfps8nvnjK0749iVJjaX~esDXqALi0u9gMPWf41OB0LeZlJCetqtiq-RmtxaTwr2MjIaMvo4ZGiX0Y2LFwW~9dy7GcvHB-dCjhFanCHETuvPY9EGyVo5W4TSiMe0OFo3w3GST5-BIWZivVsENx6y~MzWQea9qbyA__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"A_Readout_Mechanism_for_Latency_Codes","translated_slug":"","page_count":9,"language":"en","content_type":"Work","owner":{"id":246424110,"first_name":"Maoz","middle_initials":null,"last_name":"Shamir","page_name":"MaozShamir","domain_name":"bgu","created_at":"2022-11-20T23:39:36.274-08:00","display_name":"Maoz Shamir","url":"https://bgu.academia.edu/MaozShamir"},"attachments":[{"id":97942856,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/97942856/thumbnails/1.jpg","file_name":"3ec5e6a27d02cc3178373cccaac1a15ab085.pdf","download_url":"https://www.academia.edu/attachments/97942856/download_file?st=MTczMjgzMTA2MSw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"A_Readout_Mechanism_for_Latency_Codes.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/97942856/3ec5e6a27d02cc3178373cccaac1a15ab085-libre.pdf?1674994216=\u0026response-content-disposition=attachment%3B+filename%3DA_Readout_Mechanism_for_Latency_Codes.pdf\u0026Expires=1732834661\u0026Signature=SBKJdV5FV8zYncr8S7eTtvTGtKN~xsE3YR6bbbJnlqdeisS6-1VAxCWFyY0l7egUqTzpWVRHyGXnuykbUxxH-Z-AGsQ28-ArBV0~hSLBhioTnOj~aYctUWUiIdl4v8djyIbEC4esWpOYLY~P7joa~J2h-HAVlxa88t66SmO-WcNu8jypg59WXjbfps8nvnjK0749iVJjaX~esDXqALi0u9gMPWf41OB0LeZlJCetqtiq-RmtxaTwr2MjIaMvo4ZGiX0Y2LFwW~9dy7GcvHB-dCjhFanCHETuvPY9EGyVo5W4TSiMe0OFo3w3GST5-BIWZivVsENx6y~MzWQea9qbyA__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science"},{"id":26327,"name":"Medicine","url":"https://www.academia.edu/Documents/in/Medicine"},{"id":244814,"name":"Clinical Sciences","url":"https://www.academia.edu/Documents/in/Clinical_Sciences"},{"id":810972,"name":"Mechanism in Biology","url":"https://www.academia.edu/Documents/in/Mechanism_in_Biology"},{"id":1239755,"name":"Neurosciences","url":"https://www.academia.edu/Documents/in/Neurosciences"}],"urls":[{"id":28486444,"url":"http://journal.frontiersin.org/article/10.3389/fncom.2016.00107/full"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="95888223"><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/95888223/Oscillations_via_Spike_Timing_Dependent_Plasticity_in_a_Feed_Forward_Model"><img alt="Research paper thumbnail of Oscillations via Spike-Timing Dependent Plasticity in a Feed-Forward Model" class="work-thumbnail" src="https://attachments.academia-assets.com/97942860/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/95888223/Oscillations_via_Spike_Timing_Dependent_Plasticity_in_a_Feed_Forward_Model">Oscillations via Spike-Timing Dependent Plasticity in a Feed-Forward Model</a></div><div class="wp-workCard_item"><span>PLoS computational biology</span><span>, 2016</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Neuronal oscillatory activity has been reported in relation to a wide range of cognitive processe...</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">Neuronal oscillatory activity has been reported in relation to a wide range of cognitive processes including the encoding of external stimuli, attention, and learning. Although the specific role of these oscillations has yet to be determined, it is clear that neuronal oscillations are abundant in the central nervous system. This raises the question of the origin of these oscillations: are the mechanisms for generating these oscillations genetically hard-wired or can they be acquired via a learning process? Here, we study the conditions under which oscillatory activity emerges through a process of spike timing dependent plasticity (STDP) in a feed-forward architecture. First, we analyze the effect of oscillations on STDP-driven synaptic dynamics of a single synapse, and study how the parameters that characterize the STDP rule and the oscillations affect the resultant synaptic weight. Next, we analyze STDP-driven synaptic dynamics of a pre-synaptic population of neurons onto a single ...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="cdeafe1a80cb399730d60245ccaaeb53" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":97942860,"asset_id":95888223,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/97942860/download_file?st=MTczMjgzMTA2MSw4LjIyMi4yMDguMTQ2&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="95888223"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="95888223"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 95888223; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=95888223]").text(description); $(".js-view-count[data-work-id=95888223]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 95888223; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='95888223']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 95888223, container: "", }); });</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-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.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: "cdeafe1a80cb399730d60245ccaaeb53" } } $('.js-work-strip[data-work-id=95888223]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":95888223,"title":"Oscillations via Spike-Timing Dependent Plasticity in a Feed-Forward Model","translated_title":"","metadata":{"abstract":"Neuronal oscillatory activity has been reported in relation to a wide range of cognitive processes including the encoding of external stimuli, attention, and learning. Although the specific role of these oscillations has yet to be determined, it is clear that neuronal oscillations are abundant in the central nervous system. This raises the question of the origin of these oscillations: are the mechanisms for generating these oscillations genetically hard-wired or can they be acquired via a learning process? Here, we study the conditions under which oscillatory activity emerges through a process of spike timing dependent plasticity (STDP) in a feed-forward architecture. First, we analyze the effect of oscillations on STDP-driven synaptic dynamics of a single synapse, and study how the parameters that characterize the STDP rule and the oscillations affect the resultant synaptic weight. Next, we analyze STDP-driven synaptic dynamics of a pre-synaptic population of neurons onto a single ...","publication_date":{"day":null,"month":null,"year":2016,"errors":{}},"publication_name":"PLoS computational biology"},"translated_abstract":"Neuronal oscillatory activity has been reported in relation to a wide range of cognitive processes including the encoding of external stimuli, attention, and learning. Although the specific role of these oscillations has yet to be determined, it is clear that neuronal oscillations are abundant in the central nervous system. This raises the question of the origin of these oscillations: are the mechanisms for generating these oscillations genetically hard-wired or can they be acquired via a learning process? Here, we study the conditions under which oscillatory activity emerges through a process of spike timing dependent plasticity (STDP) in a feed-forward architecture. First, we analyze the effect of oscillations on STDP-driven synaptic dynamics of a single synapse, and study how the parameters that characterize the STDP rule and the oscillations affect the resultant synaptic weight. 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Here we present a derivation of the TAP equation for the Hopfield model by the cavity method and show that it agrees with the form derived by perturbation theory. We also use the cavity method to derive TAP equations for the pseudoinverse neural network model. These equations are consistent with the results of the replica theory of these models.","publication_date":{"day":null,"month":null,"year":2000,"errors":{}},"publication_name":"Physical Review E","grobid_abstract_attachment_id":97942854},"translated_abstract":null,"internal_url":"https://www.academia.edu/95888221/Thouless_Anderson_Palmer_equations_for_neural_networks","translated_internal_url":"","created_at":"2023-01-29T00:57:28.151-08:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":246424110,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[{"id":97942854,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/97942854/thumbnails/1.jpg","file_name":"bd93e5c27165b4a6c98142782a750172ff44.pdf","download_url":"https://www.academia.edu/attachments/97942854/download_file?st=MTczMjgzMTA2MSw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Thouless_Anderson_Palmer_equations_for_n.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/97942854/bd93e5c27165b4a6c98142782a750172ff44-libre.pdf?1674994218=\u0026response-content-disposition=attachment%3B+filename%3DThouless_Anderson_Palmer_equations_for_n.pdf\u0026Expires=1732834661\u0026Signature=CRpddNOFbsLVIt5LfT0yg-6smokmlQTjF~YPOJpELagoIhj561-DHggQvVikaDfiyOzlHYynBIaRIBn~oX7dHM434t4O6H4b5AcsHcXI-Q7IQGGfJEhSCEs9PByptFFLcGqNqY10LqSrkruU8b4SJlUzUBGpHkANlcrHnyh7ksM~F0caV0QWbdTesQEeOs8qxPpSfvhRjMgLA9jyv-30StAH7ozoCNykRd6xrQkS~SUcTxaJToy4R2psLK2iUELp1HNWRSXWK4QxoEG2-cepmq4gkzUWfihllah0LZH~FgadxPf1AWG91cKWPBLlkebsWGPFFDVsFpx2csSd~2mIww__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Thouless_Anderson_Palmer_equations_for_neural_networks","translated_slug":"","page_count":6,"language":"en","content_type":"Work","owner":{"id":246424110,"first_name":"Maoz","middle_initials":null,"last_name":"Shamir","page_name":"MaozShamir","domain_name":"bgu","created_at":"2022-11-20T23:39:36.274-08:00","display_name":"Maoz Shamir","url":"https://bgu.academia.edu/MaozShamir"},"attachments":[{"id":97942854,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/97942854/thumbnails/1.jpg","file_name":"bd93e5c27165b4a6c98142782a750172ff44.pdf","download_url":"https://www.academia.edu/attachments/97942854/download_file?st=MTczMjgzMTA2MSw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Thouless_Anderson_Palmer_equations_for_n.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/97942854/bd93e5c27165b4a6c98142782a750172ff44-libre.pdf?1674994218=\u0026response-content-disposition=attachment%3B+filename%3DThouless_Anderson_Palmer_equations_for_n.pdf\u0026Expires=1732834661\u0026Signature=CRpddNOFbsLVIt5LfT0yg-6smokmlQTjF~YPOJpELagoIhj561-DHggQvVikaDfiyOzlHYynBIaRIBn~oX7dHM434t4O6H4b5AcsHcXI-Q7IQGGfJEhSCEs9PByptFFLcGqNqY10LqSrkruU8b4SJlUzUBGpHkANlcrHnyh7ksM~F0caV0QWbdTesQEeOs8qxPpSfvhRjMgLA9jyv-30StAH7ozoCNykRd6xrQkS~SUcTxaJToy4R2psLK2iUELp1HNWRSXWK4QxoEG2-cepmq4gkzUWfihllah0LZH~FgadxPf1AWG91cKWPBLlkebsWGPFFDVsFpx2csSd~2mIww__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"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":192257,"name":"Physical","url":"https://www.academia.edu/Documents/in/Physical"},{"id":639432,"name":"Replica","url":"https://www.academia.edu/Documents/in/Replica"},{"id":1211304,"name":"Artificial Neural Network","url":"https://www.academia.edu/Documents/in/Artificial_Neural_Network"}],"urls":[{"id":28486443,"url":"http://link.aps.org/article/10.1103/PhysRevE.61.1839"}]}, dispatcherData: dispatcherData }); 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However, the accuracy of such a mechanism has not been analyzed rigorously. Here, we investigate the utility of first spike latency for encoding information about the location of a sound source, based on the responses of inferior colliculus (IC) neurons in the guinea pig to interaural phase differences (IPDs). First spike latencies of many cells in the guinea pig IC show unimodal tuning to stimulus IPD. We investigated the discrimination accuracy of a simple latency code that estimates stimulus IPD from the preferred IPD of the single cell that fired first. Surprisingly, despite being based on only a single spike, the accuracy of the latency code is comparable to that of a conventional rate code computed over the entire response. We show that spontaneous firing limits the capacity of the latency code to accumulate information from large neural populations. This detrimental effect can be overcome by generalizing the latency code to estimate the stimulus IPD from the preferred IPDs of the population of cells that fired the first n spikes. In addition, we show that a good estimate of the neural response time to the stimulus, which can be obtained from the responses of the cells whose response latency is invariant to stimulus identity, limits the detrimental effect of spontaneous firing. Thus, a latency code may provide great improvement in response speed at a small cost to the accuracy of the decision.","publication_date":{"day":null,"month":null,"year":2011,"errors":{}},"publication_name":"Journal of Neuroscience","grobid_abstract_attachment_id":97942862},"translated_abstract":null,"internal_url":"https://www.academia.edu/95888220/First_Spike_Latency_Code_for_Interaural_Phase_Difference_Discrimination_in_the_Guinea_Pig_Inferior_Colliculus","translated_internal_url":"","created_at":"2023-01-29T00:57:27.987-08:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":246424110,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[{"id":97942862,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/97942862/thumbnails/1.jpg","file_name":"9192.full.pdf","download_url":"https://www.academia.edu/attachments/97942862/download_file?st=MTczMjgzMTA2MSw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"First_Spike_Latency_Code_for_Interaural.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/97942862/9192.full-libre.pdf?1674994218=\u0026response-content-disposition=attachment%3B+filename%3DFirst_Spike_Latency_Code_for_Interaural.pdf\u0026Expires=1732834661\u0026Signature=B5lZSwJ5n2o2paRACUAeJCytfffWX0XOzdxeaXGaUIPRVOg7ac~4kCyHgkMALw1DUg3npMURWhwmb084TzxtXKYE~EFxSaQAiOx6PoM9hBXVMlDH7nWganGitYyF53yVhTGDhs3zIDvcfzW7qPMSzH2dxnlAVAOgCnNO6X4KGyo3MAsSj9w7xwPqZnr3xLU1Gs5ddFWJoQdnBbA6Jl4vizH1Ep6tTFToFXU333O~a8~PmlKVdWYyzzDV12p4wK68vb-~YtMFEM8dExlPhvxxgbWzeRlFr3zUf9giOD4EMq0w04zZB3zI0GJGGBN4JVtCPBjvcgDfj-jAKHn29AUKpw__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"First_Spike_Latency_Code_for_Interaural_Phase_Difference_Discrimination_in_the_Guinea_Pig_Inferior_Colliculus","translated_slug":"","page_count":13,"language":"en","content_type":"Work","owner":{"id":246424110,"first_name":"Maoz","middle_initials":null,"last_name":"Shamir","page_name":"MaozShamir","domain_name":"bgu","created_at":"2022-11-20T23:39:36.274-08:00","display_name":"Maoz Shamir","url":"https://bgu.academia.edu/MaozShamir"},"attachments":[{"id":97942862,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/97942862/thumbnails/1.jpg","file_name":"9192.full.pdf","download_url":"https://www.academia.edu/attachments/97942862/download_file?st=MTczMjgzMTA2MSw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"First_Spike_Latency_Code_for_Interaural.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/97942862/9192.full-libre.pdf?1674994218=\u0026response-content-disposition=attachment%3B+filename%3DFirst_Spike_Latency_Code_for_Interaural.pdf\u0026Expires=1732834661\u0026Signature=B5lZSwJ5n2o2paRACUAeJCytfffWX0XOzdxeaXGaUIPRVOg7ac~4kCyHgkMALw1DUg3npMURWhwmb084TzxtXKYE~EFxSaQAiOx6PoM9hBXVMlDH7nWganGitYyF53yVhTGDhs3zIDvcfzW7qPMSzH2dxnlAVAOgCnNO6X4KGyo3MAsSj9w7xwPqZnr3xLU1Gs5ddFWJoQdnBbA6Jl4vizH1Ep6tTFToFXU333O~a8~PmlKVdWYyzzDV12p4wK68vb-~YtMFEM8dExlPhvxxgbWzeRlFr3zUf9giOD4EMq0w04zZB3zI0GJGGBN4JVtCPBjvcgDfj-jAKHn29AUKpw__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":161,"name":"Neuroscience","url":"https://www.academia.edu/Documents/in/Neuroscience"},{"id":300,"name":"Mathematics","url":"https://www.academia.edu/Documents/in/Mathematics"},{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science"},{"id":26327,"name":"Medicine","url":"https://www.academia.edu/Documents/in/Medicine"},{"id":176503,"name":"Synaptic Transmission","url":"https://www.academia.edu/Documents/in/Synaptic_Transmission"},{"id":193974,"name":"Neurons","url":"https://www.academia.edu/Documents/in/Neurons"},{"id":955727,"name":"Action Potentials","url":"https://www.academia.edu/Documents/in/Action_Potentials"},{"id":1505827,"name":"Inferior Colliculus","url":"https://www.academia.edu/Documents/in/Inferior_Colliculus"},{"id":2217019,"name":"Sound Localization","url":"https://www.academia.edu/Documents/in/Sound_Localization"},{"id":2922956,"name":"Psychology and Cognitive Sciences","url":"https://www.academia.edu/Documents/in/Psychology_and_Cognitive_Sciences"},{"id":3763225,"name":"Medical and Health Sciences","url":"https://www.academia.edu/Documents/in/Medical_and_Health_Sciences"},{"id":3769209,"name":"Guinea pigs","url":"https://www.academia.edu/Documents/in/Guinea_pigs"}],"urls":[{"id":28486442,"url":"https://syndication.highwire.org/content/doi/10.1523/JNEUROSCI.6193-10.2011"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="95888219"><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/95888219/Balancing_Feed_Forward_Excitation_and_Inhibition_via_Hebbian_Inhibitory_Synaptic_Plasticity"><img alt="Research paper thumbnail of Balancing Feed-Forward Excitation and Inhibition via Hebbian Inhibitory Synaptic Plasticity" class="work-thumbnail" src="https://attachments.academia-assets.com/97942857/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/95888219/Balancing_Feed_Forward_Excitation_and_Inhibition_via_Hebbian_Inhibitory_Synaptic_Plasticity">Balancing Feed-Forward Excitation and Inhibition via Hebbian Inhibitory Synaptic Plasticity</a></div><div class="wp-workCard_item"><span>PLoS Computational Biology</span><span>, 2012</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="3dd3a8b160db71d0dfcaf6bf26396306" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":97942857,"asset_id":95888219,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/97942857/download_file?st=MTczMjgzMTA2MSw4LjIyMi4yMDguMTQ2&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="95888219"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="95888219"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 95888219; 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There are two hypotheses as to the origin of this balance. One assumes that it results from a stable solution of the recurrent neuronal dynamics. This model can account for a balance of steady state excitation and inhibition without fine tuning of parameters, but not for transient inputs. The second hypothesis suggests that the feed forward excitatory and inhibitory inputs to a postsynaptic cell are already balanced. This latter hypothesis thus does account for the balance of transient inputs. However, it remains unclear what mechanism underlies the fine tuning required for balancing feed forward excitatory and inhibitory inputs. Here we investigated whether inhibitory synaptic plasticity is responsible for the balance of transient feed forward excitation and inhibition. We address this issue in the framework of a model characterizing the stochastic dynamics of temporally anti-symmetric Hebbian spike timing dependent plasticity of feed forward excitatory and inhibitory synaptic inputs to a single post-synaptic cell. Our analysis shows that inhibitory Hebbian plasticity generates 'negative feedback' that balances excitation and inhibition, which contrasts with the 'positive feedback' of excitatory Hebbian synaptic plasticity. As a result, this balance may increase the sensitivity of the learning dynamics to the correlation structure of the excitatory inputs.","publication_date":{"day":null,"month":null,"year":2012,"errors":{}},"publication_name":"PLoS Computational Biology","grobid_abstract_attachment_id":97942857},"translated_abstract":null,"internal_url":"https://www.academia.edu/95888219/Balancing_Feed_Forward_Excitation_and_Inhibition_via_Hebbian_Inhibitory_Synaptic_Plasticity","translated_internal_url":"","created_at":"2023-01-29T00:57:27.823-08:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":246424110,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[{"id":97942857,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/97942857/thumbnails/1.jpg","file_name":"57002b7abe365553e9059ecc5fe897a7f8a1.pdf","download_url":"https://www.academia.edu/attachments/97942857/download_file?st=MTczMjgzMTA2MSw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Balancing_Feed_Forward_Excitation_and_In.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/97942857/57002b7abe365553e9059ecc5fe897a7f8a1-libre.pdf?1674994233=\u0026response-content-disposition=attachment%3B+filename%3DBalancing_Feed_Forward_Excitation_and_In.pdf\u0026Expires=1732834661\u0026Signature=Vfvb2E3~1CW4sG-h3Ur~gAFwMmFJLP2YoEGM6LPOMvq0QME4xRsfPUApkd4LJIYJUxGvi5BI~q1xCA2l4eF1Oug8QJfwOMmmAw5jRZhzGYapPfTdF0SOe8QAYtAVOMnkdt5MinG2h9xTRtkUXdpz6pK~IdX~ZEjjEoi5LhUxuo66PoDhKSHXnz9DXdHCeiij2~~boB0Q1wuu-HoaCiKqCXiHLKtcmH9BIxvPjel-yU5WyOqX0sOm-PIngtBn2xKCv1FSSRzRC1IH4ArrVF~larHlM-shz36U79rAlNdQzkpkjGqNT1kDvsI42cJUW4V5h4xXmEJK57wuOW0~W~Z3ug__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Balancing_Feed_Forward_Excitation_and_Inhibition_via_Hebbian_Inhibitory_Synaptic_Plasticity","translated_slug":"","page_count":12,"language":"en","content_type":"Work","owner":{"id":246424110,"first_name":"Maoz","middle_initials":null,"last_name":"Shamir","page_name":"MaozShamir","domain_name":"bgu","created_at":"2022-11-20T23:39:36.274-08:00","display_name":"Maoz Shamir","url":"https://bgu.academia.edu/MaozShamir"},"attachments":[{"id":97942857,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/97942857/thumbnails/1.jpg","file_name":"57002b7abe365553e9059ecc5fe897a7f8a1.pdf","download_url":"https://www.academia.edu/attachments/97942857/download_file?st=MTczMjgzMTA2MSw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Balancing_Feed_Forward_Excitation_and_In.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/97942857/57002b7abe365553e9059ecc5fe897a7f8a1-libre.pdf?1674994233=\u0026response-content-disposition=attachment%3B+filename%3DBalancing_Feed_Forward_Excitation_and_In.pdf\u0026Expires=1732834661\u0026Signature=Vfvb2E3~1CW4sG-h3Ur~gAFwMmFJLP2YoEGM6LPOMvq0QME4xRsfPUApkd4LJIYJUxGvi5BI~q1xCA2l4eF1Oug8QJfwOMmmAw5jRZhzGYapPfTdF0SOe8QAYtAVOMnkdt5MinG2h9xTRtkUXdpz6pK~IdX~ZEjjEoi5LhUxuo66PoDhKSHXnz9DXdHCeiij2~~boB0Q1wuu-HoaCiKqCXiHLKtcmH9BIxvPjel-yU5WyOqX0sOm-PIngtBn2xKCv1FSSRzRC1IH4ArrVF~larHlM-shz36U79rAlNdQzkpkjGqNT1kDvsI42cJUW4V5h4xXmEJK57wuOW0~W~Z3ug__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":161,"name":"Neuroscience","url":"https://www.academia.edu/Documents/in/Neuroscience"},{"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":32003,"name":"Synaptic Plasticity","url":"https://www.academia.edu/Documents/in/Synaptic_Plasticity"},{"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":132020,"name":"Neuronal Plasticity","url":"https://www.academia.edu/Documents/in/Neuronal_Plasticity"},{"id":176503,"name":"Synaptic Transmission","url":"https://www.academia.edu/Documents/in/Synaptic_Transmission"},{"id":193974,"name":"Neurons","url":"https://www.academia.edu/Documents/in/Neurons"},{"id":2367569,"name":"Excitatory Postsynaptic Potentials","url":"https://www.academia.edu/Documents/in/Excitatory_Postsynaptic_Potentials"}],"urls":[{"id":28486441,"url":"http://dx.plos.org/10.1371/journal.pcbi.1002334"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="95888217"><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/95888217/Representation_of_Time_Varying_Stimuli_by_a_Network_Exhibiting_Oscillations_on_a_Faster_Time_Scale"><img alt="Research paper thumbnail of Representation of Time-Varying Stimuli by a Network Exhibiting Oscillations on a Faster Time Scale" class="work-thumbnail" src="https://attachments.academia-assets.com/97942868/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/95888217/Representation_of_Time_Varying_Stimuli_by_a_Network_Exhibiting_Oscillations_on_a_Faster_Time_Scale">Representation of Time-Varying Stimuli by a Network Exhibiting Oscillations on a Faster Time Scale</a></div><div class="wp-workCard_item"><span>PLoS Computational Biology</span><span>, 2009</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="b85b82c859054ff39763926fb182eab8" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":97942868,"asset_id":95888217,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/97942868/download_file?st=MTczMjgzMTA2Miw4LjIyMi4yMDguMTQ2&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="95888217"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="95888217"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 95888217; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=95888217]").text(description); $(".js-view-count[data-work-id=95888217]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 95888217; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='95888217']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 95888217, container: "", }); });</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-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.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: "b85b82c859054ff39763926fb182eab8" } } $('.js-work-strip[data-work-id=95888217]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":95888217,"title":"Representation of Time-Varying Stimuli by a Network Exhibiting Oscillations on a Faster Time Scale","translated_title":"","metadata":{"publisher":"Public Library of Science (PLoS)","grobid_abstract":"Sensory processing is associated with gamma frequency oscillations (30-80 Hz) in sensory cortices. This raises the question whether gamma oscillations can be directly involved in the representation of time-varying stimuli, including stimuli whose time scale is longer than a gamma cycle. We are interested in the ability of the system to reliably distinguish different stimuli while being robust to stimulus variations such as uniform time-warp. We address this issue with a dynamical model of spiking neurons and study the response to an asymmetric sawtooth input current over a range of shape parameters. These parameters describe how fast the input current rises and falls in time. Our network consists of inhibitory and excitatory populations that are sufficient for generating oscillations in the gamma range. The oscillations period is about one-third of the stimulus duration. Embedded in this network is a subpopulation of excitatory cells that respond to the sawtooth stimulus and a subpopulation of cells that respond to an onset cue. The intrinsic gamma oscillations generate a temporally sparse code for the external stimuli. In this code, an excitatory cell may fire a single spike during a gamma cycle, depending on its tuning properties and on the temporal structure of the specific input; the identity of the stimulus is coded by the list of excitatory cells that fire during each cycle. We quantify the properties of this representation in a series of simulations and show that the sparseness of the code makes it robust to uniform warping of the time scale. We find that resetting of the oscillation phase at stimulus onset is important for a reliable representation of the stimulus and that there is a tradeoff between the resolution of the neural representation of the stimulus and robustness to time-warp.","publication_date":{"day":null,"month":null,"year":2009,"errors":{}},"publication_name":"PLoS Computational Biology","grobid_abstract_attachment_id":97942868},"translated_abstract":null,"internal_url":"https://www.academia.edu/95888217/Representation_of_Time_Varying_Stimuli_by_a_Network_Exhibiting_Oscillations_on_a_Faster_Time_Scale","translated_internal_url":"","created_at":"2023-01-29T00:57:27.660-08:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":246424110,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[{"id":97942868,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/97942868/thumbnails/1.jpg","file_name":"shamir_plos_09.pdf","download_url":"https://www.academia.edu/attachments/97942868/download_file?st=MTczMjgzMTA2Miw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Representation_of_Time_Varying_Stimuli_b.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/97942868/shamir_plos_09-libre.pdf?1674994220=\u0026response-content-disposition=attachment%3B+filename%3DRepresentation_of_Time_Varying_Stimuli_b.pdf\u0026Expires=1732834662\u0026Signature=LwFJiENQwIwwFVOoYEou050fBBl2~97v3fuqEV9DpGmSSoz6QeZdf7TXkKwWfGimS-UHg2F-8Q80rbS~idSuhVNWjCbEKPhlPNvv652Lq8bc~76CfNA7GsRzRZauR44XAHT1RYxmJJL~azUAt8dr13fweZPHZCtgl8Gk-4RLYHqBsSAvrUCWhBB0JEf3Ry7mWvYEZzkjmtPx3JLg0GB5oG6kaNz6tSItSquXLwCAdU98LBCUxsJcAMDNcwjY-gmCjuu94N1MKTS6116SxLa3kQbaLTsXULtpe~JrHMNYtk6WxIUjRdqwzl3qgdaa~ENADKfhZscrEn-uAWhWeYIXtw__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Representation_of_Time_Varying_Stimuli_by_a_Network_Exhibiting_Oscillations_on_a_Faster_Time_Scale","translated_slug":"","page_count":12,"language":"en","content_type":"Work","owner":{"id":246424110,"first_name":"Maoz","middle_initials":null,"last_name":"Shamir","page_name":"MaozShamir","domain_name":"bgu","created_at":"2022-11-20T23:39:36.274-08:00","display_name":"Maoz Shamir","url":"https://bgu.academia.edu/MaozShamir"},"attachments":[{"id":97942868,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/97942868/thumbnails/1.jpg","file_name":"shamir_plos_09.pdf","download_url":"https://www.academia.edu/attachments/97942868/download_file?st=MTczMjgzMTA2Miw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Representation_of_Time_Varying_Stimuli_b.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/97942868/shamir_plos_09-libre.pdf?1674994220=\u0026response-content-disposition=attachment%3B+filename%3DRepresentation_of_Time_Varying_Stimuli_b.pdf\u0026Expires=1732834662\u0026Signature=LwFJiENQwIwwFVOoYEou050fBBl2~97v3fuqEV9DpGmSSoz6QeZdf7TXkKwWfGimS-UHg2F-8Q80rbS~idSuhVNWjCbEKPhlPNvv652Lq8bc~76CfNA7GsRzRZauR44XAHT1RYxmJJL~azUAt8dr13fweZPHZCtgl8Gk-4RLYHqBsSAvrUCWhBB0JEf3Ry7mWvYEZzkjmtPx3JLg0GB5oG6kaNz6tSItSquXLwCAdU98LBCUxsJcAMDNcwjY-gmCjuu94N1MKTS6116SxLa3kQbaLTsXULtpe~JrHMNYtk6WxIUjRdqwzl3qgdaa~ENADKfhZscrEn-uAWhWeYIXtw__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":146,"name":"Bioinformatics","url":"https://www.academia.edu/Documents/in/Bioinformatics"},{"id":161,"name":"Neuroscience","url":"https://www.academia.edu/Documents/in/Neuroscience"},{"id":422,"name":"Computer 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"profile_work_strip") }); </script> </div><div class="profile--tab_content_container js-tab-pane tab-pane" data-section-id="16441667" id="papers"><div class="js-work-strip profile--work_container" data-work-id="112782991"><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/112782991/Robust_rhythmogenesis_via_spike_timing_dependent_plasticity"><img alt="Research paper thumbnail of Robust rhythmogenesis via spike-timing-dependent plasticity" class="work-thumbnail" src="https://attachments.academia-assets.com/109907349/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/112782991/Robust_rhythmogenesis_via_spike_timing_dependent_plasticity">Robust rhythmogenesis via spike-timing-dependent plasticity</a></div><div class="wp-workCard_item"><span>Physical review</span><span>, Aug 20, 2021</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="feb74c34dbb4c2a56c7e9c04f6eef78e" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":109907349,"asset_id":112782991,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/109907349/download_file?st=MTczMjgzMTA2Miw4LjIyMi4yMDguMTQ2&st=MTczMjgzMTA2MSw4LjIyMi4yMDguMTQ2&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="112782991"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa 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$('.js-work-strip[data-work-id=112782991]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":112782991,"title":"Robust rhythmogenesis via spike-timing-dependent plasticity","translated_title":"","metadata":{"publisher":"American Physical Society","grobid_abstract":"Rhythmic activity in the gamma band (30-100Hz) has been observed in numerous animal species ranging from insects to humans, and in relation to a wide range of cognitive tasks. Various experimental and theoretical studies have investigated this rhythmic activity. The theoretical efforts have mainly been focused on the neuronal dynamics, under the assumption that network connectivity satisfies certain fine-tuning conditions required to generate gamma oscillations. However, it remains unclear how this fine tuning is achieved. Here we investigated the hypothesis that spike timing dependent plasticity (STDP) can provide the underlying mechanism for tuning synaptic connectivity to generate rhythmic activity in the gamma band. We addressed this question in a modeling study. We examined STDP dynamics in the framework of a network of excitatory and inhibitory neuronal populations that has been suggested to underlie the generation of gamma. Mean field Fokker Planck equations for the synaptic weights dynamics are derived in the limit of slow learning. We drew on this approximation to determine which types of STDP rules drive the system to exhibit gamma oscillations, and demonstrate how the parameters that characterize the plasticity rule govern the rhythmic activity. Finally, we propose a novel mechanism that can ensure the robustness of self-developing processes, in general and for rhythmogenesis in particular.","publication_date":{"day":20,"month":8,"year":2021,"errors":{}},"publication_name":"Physical review","grobid_abstract_attachment_id":109907349},"translated_abstract":null,"internal_url":"https://www.academia.edu/112782991/Robust_rhythmogenesis_via_spike_timing_dependent_plasticity","translated_internal_url":"","created_at":"2024-01-02T03:46:09.055-08:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":246424110,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[{"id":109907349,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/109907349/thumbnails/1.jpg","file_name":"2007.pdf","download_url":"https://www.academia.edu/attachments/109907349/download_file?st=MTczMjgzMTA2Miw4LjIyMi4yMDguMTQ2&st=MTczMjgzMTA2MSw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Robust_rhythmogenesis_via_spike_timing_d.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/109907349/2007-libre.pdf?1704197173=\u0026response-content-disposition=attachment%3B+filename%3DRobust_rhythmogenesis_via_spike_timing_d.pdf\u0026Expires=1732834661\u0026Signature=GKO~JJirMxntOHK9M6UEZm6GpguGAuLyQ4b-KC5u4OARHp1PJHkMnLZuyZz6ApvXJpAbfw-4bp7kt2fG3ixXScNxV89bo9jP0xPznnvqTfmFI7ymm6o8~tlGzi1ZaBnr02TrY4kLA2p2Vy8X597mNgpYG1sViP9ZEzhW0-S7c0OwRvgMSozXfoSmfa~-kQvu7hPNmuSCuOzieZIpPcaxdzHamMW3uQqo4sTS5Kd5FtnjKQBFRmAVeGeb7OK5sBJNu~LMub5uAhWOCmFM10WhFzxFWK2PSzeT1sA3iLTHka2JHk9yzyt5-3KMUXUT011iN~yf6rOuzqsltMa3Caf9Ig__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Robust_rhythmogenesis_via_spike_timing_dependent_plasticity","translated_slug":"","page_count":9,"language":"en","content_type":"Work","owner":{"id":246424110,"first_name":"Maoz","middle_initials":null,"last_name":"Shamir","page_name":"MaozShamir","domain_name":"bgu","created_at":"2022-11-20T23:39:36.274-08:00","display_name":"Maoz Shamir","url":"https://bgu.academia.edu/MaozShamir"},"attachments":[{"id":109907349,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/109907349/thumbnails/1.jpg","file_name":"2007.pdf","download_url":"https://www.academia.edu/attachments/109907349/download_file?st=MTczMjgzMTA2Miw4LjIyMi4yMDguMTQ2&st=MTczMjgzMTA2MSw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Robust_rhythmogenesis_via_spike_timing_d.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/109907349/2007-libre.pdf?1704197173=\u0026response-content-disposition=attachment%3B+filename%3DRobust_rhythmogenesis_via_spike_timing_d.pdf\u0026Expires=1732834661\u0026Signature=GKO~JJirMxntOHK9M6UEZm6GpguGAuLyQ4b-KC5u4OARHp1PJHkMnLZuyZz6ApvXJpAbfw-4bp7kt2fG3ixXScNxV89bo9jP0xPznnvqTfmFI7ymm6o8~tlGzi1ZaBnr02TrY4kLA2p2Vy8X597mNgpYG1sViP9ZEzhW0-S7c0OwRvgMSozXfoSmfa~-kQvu7hPNmuSCuOzieZIpPcaxdzHamMW3uQqo4sTS5Kd5FtnjKQBFRmAVeGeb7OK5sBJNu~LMub5uAhWOCmFM10WhFzxFWK2PSzeT1sA3iLTHka2JHk9yzyt5-3KMUXUT011iN~yf6rOuzqsltMa3Caf9Ig__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"},{"id":109907350,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/109907350/thumbnails/1.jpg","file_name":"2007.pdf","download_url":"https://www.academia.edu/attachments/109907350/download_file","bulk_download_file_name":"Robust_rhythmogenesis_via_spike_timing_d.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/109907350/2007-libre.pdf?1704197185=\u0026response-content-disposition=attachment%3B+filename%3DRobust_rhythmogenesis_via_spike_timing_d.pdf\u0026Expires=1732834661\u0026Signature=ek~PQKiyZKpyKRDlQpx7ckFIiVwsprNm5ElRlraU5U-YCcijS22DsMxDWiZzWRHQQkht9HEnSwJP0G5YsaAdryY~AnaumtdPxSeQkLJbmE1S8GXLiqzMHxUu~C3m5QU~xHyWDjGO2XFj4b80rpgUdo6LfDG4o~juF~6bplev5D2URco5oLKti~sLbigjSil1WAP-fvSgPoDNH4kBsSRjkDhNqn9GN5XW6fHrYAn7gWSb5WHX-mf-8Z2WGxf7KlbErvfoWqLP4hnFt7-x4UUOCct7uBOmMH4nQ~YHDUjvxojJz16~YYIxfBs3Tr0zPc6BvoLd3S8SVQ4HIOMRRJQA2A__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":161,"name":"Neuroscience","url":"https://www.academia.edu/Documents/in/Neuroscience"},{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science"},{"id":3886,"name":"Rhythm","url":"https://www.academia.edu/Documents/in/Rhythm"},{"id":192257,"name":"Physical","url":"https://www.academia.edu/Documents/in/Physical"},{"id":210122,"name":"Robustness (evolution)","url":"https://www.academia.edu/Documents/in/Robustness_evolution_"}],"urls":[{"id":38081638,"url":"http://arxiv.org/pdf/2007.14127"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="112782990"><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/112782990/STDP_and_the_distribution_of_preferred_phases_in_the_whisker_system"><img alt="Research paper thumbnail of STDP and the distribution of preferred phases in the whisker system" class="work-thumbnail" src="https://attachments.academia-assets.com/109907383/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/112782990/STDP_and_the_distribution_of_preferred_phases_in_the_whisker_system">STDP and the distribution of preferred phases in the whisker system</a></div><div class="wp-workCard_item"><span>bioRxiv (Cold Spring Harbor Laboratory)</span><span>, Apr 30, 2021</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="6a5ee0db732491b38fd133aa5b222384" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":109907383,"asset_id":112782990,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/109907383/download_file?st=MTczMjgzMTA2Miw4LjIyMi4yMDguMTQ2&st=MTczMjgzMTA2MSw4LjIyMi4yMDguMTQ2&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="112782990"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="112782990"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 112782990; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=112782990]").text(description); $(".js-view-count[data-work-id=112782990]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 112782990; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='112782990']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 112782990, container: "", }); });</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-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.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: "6a5ee0db732491b38fd133aa5b222384" } } $('.js-work-strip[data-work-id=112782990]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":112782990,"title":"STDP and the distribution of preferred phases in the whisker system","translated_title":"","metadata":{"publisher":"Cold Spring Harbor Laboratory","grobid_abstract":"Rats and mice use their whiskers to probe the environment. By rhythmically swiping their whiskers back and forth they can detect the existence of an object, locate it, and identify its texture. Localization can be accomplished by inferring the whisker's position. Rhythmic neurons that track the phase of the whisking cycle encode information about the azimuthal location of the whisker. These neurons are characterized by preferred phases of firing that are narrowly distributed. Consequently, pooling the rhythmic signal from several upstream neurons is expected to result in a much narrower distribution of preferred phases in the downstream population, which however has not been observed empirically. Here, we show how spike timing dependent plasticity (STDP) can provide a solution to this conundrum. We investigated the effect of STDP on the utility of a neural population to transmit rhythmic information downstream using the framework of a modeling study. We found that under a wide range of parameters, STDP facilitated the transfer of rhythmic information despite the fact that all the synaptic weights remained dynamic. As a result, the preferred phase of the downstream neuron was not fixed, but rather drifted in time at a drift velocity that depended on the preferred phase, thus inducing a distribution of preferred phases. We further analyzed how the STDP rule governs the distribution of preferred phases in the downstream population. This link between the STDP rule and the distribution of preferred phases constitutes a natural test for our theory.","publication_date":{"day":30,"month":4,"year":2021,"errors":{}},"publication_name":"bioRxiv (Cold Spring Harbor Laboratory)","grobid_abstract_attachment_id":109907383},"translated_abstract":null,"internal_url":"https://www.academia.edu/112782990/STDP_and_the_distribution_of_preferred_phases_in_the_whisker_system","translated_internal_url":"","created_at":"2024-01-02T03:46:06.149-08:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":246424110,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[{"id":109907383,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/109907383/thumbnails/1.jpg","file_name":"pmc8480728.pdf","download_url":"https://www.academia.edu/attachments/109907383/download_file?st=MTczMjgzMTA2Miw4LjIyMi4yMDguMTQ2&st=MTczMjgzMTA2MSw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"STDP_and_the_distribution_of_preferred_p.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/109907383/pmc8480728-libre.pdf?1704197181=\u0026response-content-disposition=attachment%3B+filename%3DSTDP_and_the_distribution_of_preferred_p.pdf\u0026Expires=1732834661\u0026Signature=QT8L-v57Qx2xrddA0MVC02D2OR3Z6uGPX3jCvUd9q3MaH5CbiD9inN8uouwpcqzP6z2PT~PhTWtMvs83KTfjFJxjKUttl0zUpULX53JoEnOY~bwFQtCfa9H95mevHzrT0275-5KbXGCUH4U6yHuhUW-9lk0h3Xz3gG87XPlpBLW25i2QUdnU0wr31XKBigk9hh4-szs1ZujSx-dxp1-tdaUhcfNSs7Aw8kUrbGIxqPkN1TSPsOoeaoyG3bKImB43iPYX7RtZHw3MkD3DZtm1sKkyEDrkcoJF1AIXe2faD8DvHiP2GE8kZ5IyawtU2GgyR4Ee~wPtjVgpAgGs5BgMyg__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"STDP_and_the_distribution_of_preferred_phases_in_the_whisker_system","translated_slug":"","page_count":18,"language":"en","content_type":"Work","owner":{"id":246424110,"first_name":"Maoz","middle_initials":null,"last_name":"Shamir","page_name":"MaozShamir","domain_name":"bgu","created_at":"2022-11-20T23:39:36.274-08:00","display_name":"Maoz Shamir","url":"https://bgu.academia.edu/MaozShamir"},"attachments":[{"id":109907383,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/109907383/thumbnails/1.jpg","file_name":"pmc8480728.pdf","download_url":"https://www.academia.edu/attachments/109907383/download_file?st=MTczMjgzMTA2Miw4LjIyMi4yMDguMTQ2&st=MTczMjgzMTA2MSw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"STDP_and_the_distribution_of_preferred_p.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/109907383/pmc8480728-libre.pdf?1704197181=\u0026response-content-disposition=attachment%3B+filename%3DSTDP_and_the_distribution_of_preferred_p.pdf\u0026Expires=1732834661\u0026Signature=QT8L-v57Qx2xrddA0MVC02D2OR3Z6uGPX3jCvUd9q3MaH5CbiD9inN8uouwpcqzP6z2PT~PhTWtMvs83KTfjFJxjKUttl0zUpULX53JoEnOY~bwFQtCfa9H95mevHzrT0275-5KbXGCUH4U6yHuhUW-9lk0h3Xz3gG87XPlpBLW25i2QUdnU0wr31XKBigk9hh4-szs1ZujSx-dxp1-tdaUhcfNSs7Aw8kUrbGIxqPkN1TSPsOoeaoyG3bKImB43iPYX7RtZHw3MkD3DZtm1sKkyEDrkcoJF1AIXe2faD8DvHiP2GE8kZ5IyawtU2GgyR4Ee~wPtjVgpAgGs5BgMyg__\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":3886,"name":"Rhythm","url":"https://www.academia.edu/Documents/in/Rhythm"},{"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":64336,"name":"Population","url":"https://www.academia.edu/Documents/in/Population"},{"id":80414,"name":"Mathematical Sciences","url":"https://www.academia.edu/Documents/in/Mathematical_Sciences"},{"id":207853,"name":"Pooling","url":"https://www.academia.edu/Documents/in/Pooling"},{"id":2249025,"name":"Whisker","url":"https://www.academia.edu/Documents/in/Whisker"},{"id":2273259,"name":"Biological System","url":"https://www.academia.edu/Documents/in/Biological_System"}],"urls":[{"id":38081637,"url":"https://doi.org/10.1101/2021.04.29.442009"}]}, dispatcherData: dispatcherData }); 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dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "d006006a3fa56948b80dc597810d4cef" } } $('.js-work-strip[data-work-id=112782929]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":112782929,"title":"Multiplexing rhythmic information by spike timing dependent plasticity","translated_title":"","metadata":{"publisher":"International Society for Computational Biology","grobid_abstract":"Rhythmic activity has been associated with a wide range of cognitive processes including the encoding of sensory information, navigation, the transfer of emotional information and others. Previous studies have shown that spike-timing-dependent plasticity (STDP) can facilitate the transfer of rhythmic activity downstream the information processing pathway. However, STDP has also been known to generate strong winner-take-all like competitions between subgroups of correlated synaptic inputs. Consequently, one might expect that STDP would induce strong competition between different rhythmicity channels thus preventing the multiplexing of information across different frequency channels. This study explored whether STDP facilitates the multiplexing of information across multiple frequency channels, and if so, under what conditions. We investigated the STDP dynamics in the framework of a model consisting of two competing sub-populations of neurons that synapse in a feedforward manner onto a single post-synaptic neuron. Each sub-population was assumed to oscillate in an independent manner and in a different frequency band. To investigate the STDP dynamics, a mean field Fokker-Planck theory was developed in the limit of the slow learning rate. Surprisingly, our theory predicted limited interactions between the different subgroups. Our analysis further revealed that the interaction between these channels was mainly mediated by the shared component of the mean activity. Next, we generalized these results beyond the simplistic model using numerical simulations. We found that for a wide range of parameters, the system converged to a solution in which the post-synaptic neuron responded to both rhythms. Nevertheless, all the synaptic weights remained dynamic and did not converge to a fixed point. These findings imply that STDP can support the multiplexing of rhythmic information, and demonstrate how functionality (multiplexing of information) can be retained in the face of continuous remodeling of all the synaptic weights.","publication_date":{"day":29,"month":6,"year":2020,"errors":{}},"publication_name":"PLOS Computational Biology","grobid_abstract_attachment_id":109907327},"translated_abstract":null,"internal_url":"https://www.academia.edu/112782929/Multiplexing_rhythmic_information_by_spike_timing_dependent_plasticity","translated_internal_url":"","created_at":"2024-01-02T03:44:17.406-08:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":246424110,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[{"id":109907327,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/109907327/thumbnails/1.jpg","file_name":"1911.11466v1.pdf","download_url":"https://www.academia.edu/attachments/109907327/download_file?st=MTczMjgzMTA2Miw4LjIyMi4yMDguMTQ2&st=MTczMjgzMTA2MSw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Multiplexing_rhythmic_information_by_spi.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/109907327/1911.11466v1-libre.pdf?1704197201=\u0026response-content-disposition=attachment%3B+filename%3DMultiplexing_rhythmic_information_by_spi.pdf\u0026Expires=1732834661\u0026Signature=aGnTEH4ifGqC6S4O~SC8xe-AOXGvWOhhAvLMtgzFKLjkHHeKcqmxPUoYo18uMDsmXNt-c6XETzmhASffDHt-5GgPFwGkeYKhvMz-7V121Twz1hdnPPmTo7AYOi95~KEqOGc8HPvWGqftSl12j7OengpPaJaLXR9DXVSTGYfCBEISG7wcBw9ePpIXUrvc~CZoNzTVbdsal61q0uAvdCrixwyipYrMYpv0PitU7Cuf2WsbD-VDHMYKnV0-ZKfRo17LMh0BPoWjx93H-c3dL8eNPWN0RkDosli-Apd8VQQpNO1YdFzH0MjzCiopmZY8v7jbECguflIIYqDWPKY3gd7SJA__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Multiplexing_rhythmic_information_by_spike_timing_dependent_plasticity","translated_slug":"","page_count":14,"language":"en","content_type":"Work","owner":{"id":246424110,"first_name":"Maoz","middle_initials":null,"last_name":"Shamir","page_name":"MaozShamir","domain_name":"bgu","created_at":"2022-11-20T23:39:36.274-08:00","display_name":"Maoz Shamir","url":"https://bgu.academia.edu/MaozShamir"},"attachments":[{"id":109907327,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/109907327/thumbnails/1.jpg","file_name":"1911.11466v1.pdf","download_url":"https://www.academia.edu/attachments/109907327/download_file?st=MTczMjgzMTA2Miw4LjIyMi4yMDguMTQ2&st=MTczMjgzMTA2MSw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Multiplexing_rhythmic_information_by_spi.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/109907327/1911.11466v1-libre.pdf?1704197201=\u0026response-content-disposition=attachment%3B+filename%3DMultiplexing_rhythmic_information_by_spi.pdf\u0026Expires=1732834661\u0026Signature=aGnTEH4ifGqC6S4O~SC8xe-AOXGvWOhhAvLMtgzFKLjkHHeKcqmxPUoYo18uMDsmXNt-c6XETzmhASffDHt-5GgPFwGkeYKhvMz-7V121Twz1hdnPPmTo7AYOi95~KEqOGc8HPvWGqftSl12j7OengpPaJaLXR9DXVSTGYfCBEISG7wcBw9ePpIXUrvc~CZoNzTVbdsal61q0uAvdCrixwyipYrMYpv0PitU7Cuf2WsbD-VDHMYKnV0-ZKfRo17LMh0BPoWjx93H-c3dL8eNPWN0RkDosli-Apd8VQQpNO1YdFzH0MjzCiopmZY8v7jbECguflIIYqDWPKY3gd7SJA__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":161,"name":"Neuroscience","url":"https://www.academia.edu/Documents/in/Neuroscience"},{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science"},{"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":4110287,"name":"Feed Forward","url":"https://www.academia.edu/Documents/in/Feed_Forward"}],"urls":[{"id":38081594,"url":"https://doi.org/10.1371/journal.pcbi.1008000"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="98885682"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/98885682/Different_strategies_for_coding_What_and_When_in_the_archer_fish_retina"><img alt="Research paper thumbnail of Different strategies for coding What and When in the archer fish retina" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/98885682/Different_strategies_for_coding_What_and_When_in_the_archer_fish_retina">Different strategies for coding What and When in the archer fish retina</a></div><div class="wp-workCard_item"><span>Frontiers in Systems Neuroscience</span><span>, 2009</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="98885682"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="98885682"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 98885682; 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="95888233"><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/95888233/Robust_Rhythmogenesis_in_the_Gamma_Band_via_Spike_Timing_Dependent_Plasticity"><img alt="Research paper thumbnail of Robust Rhythmogenesis in the Gamma Band via Spike Timing Dependent Plasticity" class="work-thumbnail" src="https://attachments.academia-assets.com/97942840/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/95888233/Robust_Rhythmogenesis_in_the_Gamma_Band_via_Spike_Timing_Dependent_Plasticity">Robust Rhythmogenesis in the Gamma Band via Spike Timing Dependent Plasticity</a></div><div class="wp-workCard_item"><span>bioRxiv</span><span>, 2020</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Rhythmic activity in the gamma band (30-100Hz) has been observed in numerous animal species rangi...</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">Rhythmic activity in the gamma band (30-100Hz) has been observed in numerous animal species ranging from insects to humans, and in relation to a wide range of cognitive tasks. Various experimental and theoretical studies have investigated this rhythmic activity. The theoretical efforts have mainly been focused on the neuronal dynamics, under the assumption that network connectivity satisfies certain fine-tuning conditions required to generate gamma oscillations. However, it remains unclear how this fine tuning is achieved. Here we investigated the hypothesis that spike timing dependent plasticity (STDP) can provide the underlying mechanism for tuning synaptic connectivity to generate rhythmic activity in the gamma band. We addressed this question in a modeling study. We examined STDP dynamics in the framework of a network of excitatory and inhibitory neuronal populations that has been suggested to underlie the generation of gamma. Mean field Fokker Planck equations for the synaptic ...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="3344588001b0a76635bc518f4a4cfee2" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":97942840,"asset_id":95888233,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/97942840/download_file?st=MTczMjgzMTA2Miw4LjIyMi4yMDguMTQ2&st=MTczMjgzMTA2MSw4LjIyMi4yMDguMTQ2&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="95888233"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="95888233"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 95888233; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=95888233]").text(description); $(".js-view-count[data-work-id=95888233]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 95888233; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='95888233']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 95888233, container: "", }); });</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-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.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: "3344588001b0a76635bc518f4a4cfee2" } } $('.js-work-strip[data-work-id=95888233]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":95888233,"title":"Robust Rhythmogenesis in the Gamma Band via Spike Timing Dependent Plasticity","translated_title":"","metadata":{"abstract":"Rhythmic activity in the gamma band (30-100Hz) has been observed in numerous animal species ranging from insects to humans, and in relation to a wide range of cognitive tasks. 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By rhythmically swiping their whiskers...</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">Rats and mice use their whiskers to probe the environment. By rhythmically swiping their whiskers back and forth they can detect the existence of an object, locate it, and identify its texture. Localization can be accomplished by inferring the whisker鈥檚 position. Rhythmic neurons that track the phase of the whisking cycle encode information about the azimuthal location of the whisker. These neurons are characterized by preferred phases of firing that are narrowly distributed. Consequently, pooling the rhythmic signal from several upstream neurons is expected to result in a much narrower distribution of preferred phases in the downstream population, which however has not been observed empirically. Here, we show how spike timing dependent plasticity (STDP) can provide a solution to this conundrum. We investigated the effect of STDP on the utility of a neural population to transmit rhythmic information downstream using the framework of a modeling study. We found that under a wide range...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="914496609b565405c3d1054f5ac69819" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":97942865,"asset_id":95888232,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/97942865/download_file?st=MTczMjgzMTA2Miw4LjIyMi4yMDguMTQ2&st=MTczMjgzMTA2MSw4LjIyMi4yMDguMTQ2&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="95888232"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="95888232"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 95888232; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=95888232]").text(description); $(".js-view-count[data-work-id=95888232]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 95888232; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='95888232']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 95888232, container: "", }); });</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-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.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: "914496609b565405c3d1054f5ac69819" } } $('.js-work-strip[data-work-id=95888232]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":95888232,"title":"STDP and the distribution of preferred phases in the whisker system","translated_title":"","metadata":{"abstract":"Rats and mice use their whiskers to probe the environment. By rhythmically swiping their whiskers back and forth they can detect the existence of an object, locate it, and identify its texture. Localization can be accomplished by inferring the whisker鈥檚 position. Rhythmic neurons that track the phase of the whisking cycle encode information about the azimuthal location of the whisker. These neurons are characterized by preferred phases of firing that are narrowly distributed. Consequently, pooling the rhythmic signal from several upstream neurons is expected to result in a much narrower distribution of preferred phases in the downstream population, which however has not been observed empirically. Here, we show how spike timing dependent plasticity (STDP) can provide a solution to this conundrum. We investigated the effect of STDP on the utility of a neural population to transmit rhythmic information downstream using the framework of a modeling study. 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Here, we show how spike timing dependent plasticity (STDP) can provide a solution to this conundrum. We investigated the effect of STDP on the utility of a neural population to transmit rhythmic information downstream using the framework of a modeling study. 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="95888231"><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/95888231/Robust_Rhythmogenesis_via_Spike_Timing_Dependent_Plasticity"><img alt="Research paper thumbnail of Robust Rhythmogenesis via Spike Timing Dependent Plasticity" class="work-thumbnail" src="https://attachments.academia-assets.com/97942861/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/95888231/Robust_Rhythmogenesis_via_Spike_Timing_Dependent_Plasticity">Robust Rhythmogenesis via Spike Timing Dependent Plasticity</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Rhythmic activity has been observed in numerous animal species ranging from insects to humans, an...</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">Rhythmic activity has been observed in numerous animal species ranging from insects to humans, and in relation to a wide range of cognitive tasks. Various experimental and theoretical studies have investigated rhythmic activity. The theoretical efforts have mainly been focused on the neuronal dynamics, under the assumption that network connectivity satisfies certain fine-tuning conditions required to generate oscillations. However, it remains unclear how this fine tuning is achieved.Here we investigated the hypothesis that spike timing dependent plasticity (STDP) can provide the underlying mechanism for tuning synaptic connectivity to generate rhythmic activity. We addressed this question in a modeling study. We examined STDP dynamics in the framework of a network of excitatory and inhibitory neuronal populations that has been suggested to underlie the generation of oscillations in the gamma range. Mean field Fokker Planck equations for the synaptic weights dynamics are derived in t...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="f3d8f9b20e25099addcbff09525fa41c" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":97942861,"asset_id":95888231,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/97942861/download_file?st=MTczMjgzMTA2Miw4LjIyMi4yMDguMTQ2&st=MTczMjgzMTA2MSw4LjIyMi4yMDguMTQ2&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="95888231"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="95888231"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 95888231; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=95888231]").text(description); $(".js-view-count[data-work-id=95888231]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 95888231; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='95888231']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 95888231, container: "", }); });</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-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.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: "f3d8f9b20e25099addcbff09525fa41c" } } $('.js-work-strip[data-work-id=95888231]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":95888231,"title":"Robust Rhythmogenesis via Spike Timing Dependent Plasticity","translated_title":"","metadata":{"abstract":"Rhythmic activity has been observed in numerous animal species ranging from insects to humans, and in relation to a wide range of cognitive tasks. Various experimental and theoretical studies have investigated rhythmic activity. The theoretical efforts have mainly been focused on the neuronal dynamics, under the assumption that network connectivity satisfies certain fine-tuning conditions required to generate oscillations. However, it remains unclear how this fine tuning is achieved.Here we investigated the hypothesis that spike timing dependent plasticity (STDP) can provide the underlying mechanism for tuning synaptic connectivity to generate rhythmic activity. We addressed this question in a modeling study. We examined STDP dynamics in the framework of a network of excitatory and inhibitory neuronal populations that has been suggested to underlie the generation of oscillations in the gamma range. Mean field Fokker Planck equations for the synaptic weights dynamics are derived in t...","publisher":"Cold Spring Harbor Laboratory","publication_date":{"day":null,"month":null,"year":2020,"errors":{}}},"translated_abstract":"Rhythmic activity has been observed in numerous animal species ranging from insects to humans, and in relation to a wide range of cognitive tasks. Various experimental and theoretical studies have investigated rhythmic activity. The theoretical efforts have mainly been focused on the neuronal dynamics, under the assumption that network connectivity satisfies certain fine-tuning conditions required to generate oscillations. However, it remains unclear how this fine tuning is achieved.Here we investigated the hypothesis that spike timing dependent plasticity (STDP) can provide the underlying mechanism for tuning synaptic connectivity to generate rhythmic activity. We addressed this question in a modeling study. We examined STDP dynamics in the framework of a network of excitatory and inhibitory neuronal populations that has been suggested to underlie the generation of oscillations in the gamma range. Mean field Fokker Planck equations for the synaptic weights dynamics are derived in t...","internal_url":"https://www.academia.edu/95888231/Robust_Rhythmogenesis_via_Spike_Timing_Dependent_Plasticity","translated_internal_url":"","created_at":"2023-01-29T00:57:29.823-08:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":246424110,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[{"id":97942861,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/97942861/thumbnails/1.jpg","file_name":"2020.07.23.217026v1.full.pdf","download_url":"https://www.academia.edu/attachments/97942861/download_file?st=MTczMjgzMTA2Miw4LjIyMi4yMDguMTQ2&st=MTczMjgzMTA2MSw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Robust_Rhythmogenesis_via_Spike_Timing_D.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/97942861/2020.07.23.217026v1.full-libre.pdf?1674994220=\u0026response-content-disposition=attachment%3B+filename%3DRobust_Rhythmogenesis_via_Spike_Timing_D.pdf\u0026Expires=1732834661\u0026Signature=Rv1WMR4lGhBK2V3ho3V0RRE3rA4JwZQzc-6xcTDCDm00mu2F-uLBv3zpRg-KBJ~araIodpHNud8AYw8194AT~XVLxpA2iSgwPhGQ4tGuYiQ8~uMTQlNHIyey5MCpvrKsOoSQTjkv5GZ6XeLArSQi1AFFZLHgW8FvyY51WueHS6u8szbR08oFl~61zmOCiya5oE9AoQ9muE6F0iiCpQMKf6jattcTCd~gAidKsa~t1R6LQzYfwONquA8H9NYmX99UMZAET7XEg6PXdrdd8XRoGedbNtuxzWjG1xckU0XiYT3dERYCKX~g6o0CHLWSyAMCPDzOpU1QH7xAwFOnxDj6Xw__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Robust_Rhythmogenesis_via_Spike_Timing_Dependent_Plasticity","translated_slug":"","page_count":9,"language":"en","content_type":"Work","owner":{"id":246424110,"first_name":"Maoz","middle_initials":null,"last_name":"Shamir","page_name":"MaozShamir","domain_name":"bgu","created_at":"2022-11-20T23:39:36.274-08:00","display_name":"Maoz Shamir","url":"https://bgu.academia.edu/MaozShamir"},"attachments":[{"id":97942861,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/97942861/thumbnails/1.jpg","file_name":"2020.07.23.217026v1.full.pdf","download_url":"https://www.academia.edu/attachments/97942861/download_file?st=MTczMjgzMTA2Miw4LjIyMi4yMDguMTQ2&st=MTczMjgzMTA2MSw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Robust_Rhythmogenesis_via_Spike_Timing_D.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/97942861/2020.07.23.217026v1.full-libre.pdf?1674994220=\u0026response-content-disposition=attachment%3B+filename%3DRobust_Rhythmogenesis_via_Spike_Timing_D.pdf\u0026Expires=1732834661\u0026Signature=Rv1WMR4lGhBK2V3ho3V0RRE3rA4JwZQzc-6xcTDCDm00mu2F-uLBv3zpRg-KBJ~araIodpHNud8AYw8194AT~XVLxpA2iSgwPhGQ4tGuYiQ8~uMTQlNHIyey5MCpvrKsOoSQTjkv5GZ6XeLArSQi1AFFZLHgW8FvyY51WueHS6u8szbR08oFl~61zmOCiya5oE9AoQ9muE6F0iiCpQMKf6jattcTCd~gAidKsa~t1R6LQzYfwONquA8H9NYmX99UMZAET7XEg6PXdrdd8XRoGedbNtuxzWjG1xckU0XiYT3dERYCKX~g6o0CHLWSyAMCPDzOpU1QH7xAwFOnxDj6Xw__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":161,"name":"Neuroscience","url":"https://www.academia.edu/Documents/in/Neuroscience"},{"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":3886,"name":"Rhythm","url":"https://www.academia.edu/Documents/in/Rhythm"},{"id":7710,"name":"Biology","url":"https://www.academia.edu/Documents/in/Biology"},{"id":210122,"name":"Robustness (evolution)","url":"https://www.academia.edu/Documents/in/Robustness_evolution_"}],"urls":[{"id":28486450,"url":"https://syndication.highwire.org/content/doi/10.1101/2020.07.23.217026"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="95888229"><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/95888229/Multiplexing_rhythmic_information_by_spike_timing_dependent_plasticity"><img alt="Research paper thumbnail of Multiplexing rhythmic information by spike timing dependent plasticity" class="work-thumbnail" src="https://attachments.academia-assets.com/97942859/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/95888229/Multiplexing_rhythmic_information_by_spike_timing_dependent_plasticity">Multiplexing rhythmic information by spike timing dependent plasticity</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Rhythmic activity has been associated with a wide range of cognitive processes including the enco...</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">Rhythmic activity has been associated with a wide range of cognitive processes including the encoding of sensory information, navigation, the transfer of emotional information and others. Previous studies have shown that spike-timing-dependent plasticity (STDP) can facilitate the transfer of rhythmic activity downstream the information processing pathway. However, STDP has also been known to generate strong winner-take-all like competitions between subgroups of correlated synaptic inputs. Consequently, one might expect that STDP would induce strong competition between different rhythmicity channels thus preventing the multiplexing of information across different frequency channels. This study explored whether STDP facilitates the multiplexing of information across multiple frequency channels, and if so, under what conditions. We investigated the STDP dynamics in the framework of a model consisting of two competing sub-populations of neurons that synapse in a feedforward manner onto ...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="4fc9c2ea612e53d5b7949bb06270b9ee" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":97942859,"asset_id":95888229,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/97942859/download_file?st=MTczMjgzMTA2Miw4LjIyMi4yMDguMTQ2&st=MTczMjgzMTA2MSw4LjIyMi4yMDguMTQ2&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="95888229"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="95888229"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 95888229; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=95888229]").text(description); $(".js-view-count[data-work-id=95888229]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 95888229; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='95888229']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 95888229, container: "", }); });</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-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.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: "4fc9c2ea612e53d5b7949bb06270b9ee" } } $('.js-work-strip[data-work-id=95888229]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":95888229,"title":"Multiplexing rhythmic information by spike timing dependent plasticity","translated_title":"","metadata":{"abstract":"Rhythmic activity has been associated with a wide range of cognitive processes including the encoding of sensory information, navigation, the transfer of emotional information and others. Previous studies have shown that spike-timing-dependent plasticity (STDP) can facilitate the transfer of rhythmic activity downstream the information processing pathway. However, STDP has also been known to generate strong winner-take-all like competitions between subgroups of correlated synaptic inputs. Consequently, one might expect that STDP would induce strong competition between different rhythmicity channels thus preventing the multiplexing of information across different frequency channels. This study explored whether STDP facilitates the multiplexing of information across multiple frequency channels, and if so, under what conditions. We investigated the STDP dynamics in the framework of a model consisting of two competing sub-populations of neurons that synapse in a feedforward manner onto ...","publisher":"Cold Spring Harbor Laboratory","publication_date":{"day":null,"month":null,"year":2019,"errors":{}}},"translated_abstract":"Rhythmic activity has been associated with a wide range of cognitive processes including the encoding of sensory information, navigation, the transfer of emotional information and others. Previous studies have shown that spike-timing-dependent plasticity (STDP) can facilitate the transfer of rhythmic activity downstream the information processing pathway. However, STDP has also been known to generate strong winner-take-all like competitions between subgroups of correlated synaptic inputs. Consequently, one might expect that STDP would induce strong competition between different rhythmicity channels thus preventing the multiplexing of information across different frequency channels. This study explored whether STDP facilitates the multiplexing of information across multiple frequency channels, and if so, under what conditions. We investigated the STDP dynamics in the framework of a model consisting of two competing sub-populations of neurons that synapse in a feedforward manner onto ...","internal_url":"https://www.academia.edu/95888229/Multiplexing_rhythmic_information_by_spike_timing_dependent_plasticity","translated_internal_url":"","created_at":"2023-01-29T00:57:29.611-08:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":246424110,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[{"id":97942859,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/97942859/thumbnails/1.jpg","file_name":"1911.pdf","download_url":"https://www.academia.edu/attachments/97942859/download_file?st=MTczMjgzMTA2Miw4LjIyMi4yMDguMTQ2&st=MTczMjgzMTA2MSw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Multiplexing_rhythmic_information_by_spi.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/97942859/1911-libre.pdf?1674994233=\u0026response-content-disposition=attachment%3B+filename%3DMultiplexing_rhythmic_information_by_spi.pdf\u0026Expires=1732834661\u0026Signature=Hex5~xLgnmCVHncofFlDsdTbc0YiwS8N-yUfGXR38t~qiMYVQ1Jqb1oN7ivCORa1O1w0hc3rSJYq5RDKpwfx4Gc7lkc8HVmtfEYK0YgOl0BzmKQg~I-Rx1uwerroZBBE4-W15OBa5frqtaadU08H8LPAwmDh6bP5DFOsE9La5NqVqCk0ZC0eJcWr0t8SXn6cx~1IBw5Qa686WRGmhGLLaOr7nUU1exY46M3FVI96PJrBXAJfAAjSB1ZegFH5p0N~Wy4eFa78ac-s5xtCA0v0cAVJoOtRDFnrdCWqBg8LBOO5PR7ocf2sBzes~Y4ziOTf6HcZCn3Wrm9xCycPu3eNDw__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Multiplexing_rhythmic_information_by_spike_timing_dependent_plasticity","translated_slug":"","page_count":14,"language":"en","content_type":"Work","owner":{"id":246424110,"first_name":"Maoz","middle_initials":null,"last_name":"Shamir","page_name":"MaozShamir","domain_name":"bgu","created_at":"2022-11-20T23:39:36.274-08:00","display_name":"Maoz Shamir","url":"https://bgu.academia.edu/MaozShamir"},"attachments":[{"id":97942859,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/97942859/thumbnails/1.jpg","file_name":"1911.pdf","download_url":"https://www.academia.edu/attachments/97942859/download_file?st=MTczMjgzMTA2Miw4LjIyMi4yMDguMTQ2&st=MTczMjgzMTA2MSw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Multiplexing_rhythmic_information_by_spi.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/97942859/1911-libre.pdf?1674994233=\u0026response-content-disposition=attachment%3B+filename%3DMultiplexing_rhythmic_information_by_spi.pdf\u0026Expires=1732834661\u0026Signature=Hex5~xLgnmCVHncofFlDsdTbc0YiwS8N-yUfGXR38t~qiMYVQ1Jqb1oN7ivCORa1O1w0hc3rSJYq5RDKpwfx4Gc7lkc8HVmtfEYK0YgOl0BzmKQg~I-Rx1uwerroZBBE4-W15OBa5frqtaadU08H8LPAwmDh6bP5DFOsE9La5NqVqCk0ZC0eJcWr0t8SXn6cx~1IBw5Qa686WRGmhGLLaOr7nUU1exY46M3FVI96PJrBXAJfAAjSB1ZegFH5p0N~Wy4eFa78ac-s5xtCA0v0cAVJoOtRDFnrdCWqBg8LBOO5PR7ocf2sBzes~Y4ziOTf6HcZCn3Wrm9xCycPu3eNDw__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":161,"name":"Neuroscience","url":"https://www.academia.edu/Documents/in/Neuroscience"},{"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":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":118599,"name":"Information Transfer","url":"https://www.academia.edu/Documents/in/Information_Transfer"},{"id":4110287,"name":"Feed Forward","url":"https://www.academia.edu/Documents/in/Feed_Forward"}],"urls":[{"id":28486449,"url":"https://syndication.highwire.org/content/doi/10.1101/855965"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="95888228"><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/95888228/Relating_the_Structure_of_Noise_Correlations_in_Macaque_Primary_Visual_Cortex_to_Decoder_Performance"><img alt="Research paper thumbnail of Relating the Structure of Noise Correlations in Macaque Primary Visual Cortex to Decoder Performance" class="work-thumbnail" src="https://attachments.academia-assets.com/97942864/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/95888228/Relating_the_Structure_of_Noise_Correlations_in_Macaque_Primary_Visual_Cortex_to_Decoder_Performance">Relating the Structure of Noise Correlations in Macaque Primary Visual Cortex to Decoder Performance</a></div><div class="wp-workCard_item"><span>Frontiers in Computational Neuroscience</span><span>, 2018</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="fc3c951765de164cdff90db603179ff2" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":97942864,"asset_id":95888228,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/97942864/download_file?st=MTczMjgzMTA2Miw4LjIyMi4yMDguMTQ2&st=MTczMjgzMTA2MSw4LjIyMi4yMDguMTQ2&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="95888228"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="95888228"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 95888228; 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="95888227"><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/95888227/Theories_of_rhythmogenesis"><img alt="Research paper thumbnail of Theories of rhythmogenesis" 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"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/95888227/Theories_of_rhythmogenesis">Theories of rhythmogenesis</a></div><div class="wp-workCard_item"><span>Current Opinion in Neurobiology</span><span>, 2019</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Rhythmogenesis is the process that develops the capacity for rhythmic activity in a non-rhythmic ...</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">Rhythmogenesis is the process that develops the capacity for rhythmic activity in a non-rhythmic system. Theoretical works suggested a wide array of possible mechanisms for rhythmogenesis ranging from the regulation of cellular properties to top-down control. Here we discuss theories of rhythmogenesis with an emphasis on spike timing-dependent plasticity. We argue that even though the specifics of different mechanisms vary greatly they all share certain key features. Namely, rhythmogenesis can be described as a flow on the phase diagram leading the system into a rhythmic region and stabilizing it on a specific manifold characterized by the desired rhythmic activity. Functionality is retained despite biological diversity by forcing the system into a specific manifold, but allowing fluctuations within that manifold.</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="95888227"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="95888227"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 95888227; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=95888227]").text(description); $(".js-view-count[data-work-id=95888227]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 95888227; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='95888227']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 95888227, container: "", }); });</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-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.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" } } $('.js-work-strip[data-work-id=95888227]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":95888227,"title":"Theories of rhythmogenesis","translated_title":"","metadata":{"abstract":"Rhythmogenesis is the process that develops the capacity for rhythmic activity in a non-rhythmic system. Theoretical works suggested a wide array of possible mechanisms for rhythmogenesis ranging from the regulation of cellular properties to top-down control. Here we discuss theories of rhythmogenesis with an emphasis on spike timing-dependent plasticity. We argue that even though the specifics of different mechanisms vary greatly they all share certain key features. Namely, rhythmogenesis can be described as a flow on the phase diagram leading the system into a rhythmic region and stabilizing it on a specific manifold characterized by the desired rhythmic activity. Functionality is retained despite biological diversity by forcing the system into a specific manifold, but allowing fluctuations within that manifold.","publisher":"Elsevier BV","publication_date":{"day":null,"month":null,"year":2019,"errors":{}},"publication_name":"Current Opinion in Neurobiology"},"translated_abstract":"Rhythmogenesis is the process that develops the capacity for rhythmic activity in a non-rhythmic system. Theoretical works suggested a wide array of possible mechanisms for rhythmogenesis ranging from the regulation of cellular properties to top-down control. Here we discuss theories of rhythmogenesis with an emphasis on spike timing-dependent plasticity. We argue that even though the specifics of different mechanisms vary greatly they all share certain key features. Namely, rhythmogenesis can be described as a flow on the phase diagram leading the system into a rhythmic region and stabilizing it on a specific manifold characterized by the desired rhythmic activity. Functionality is retained despite biological diversity by forcing the system into a specific manifold, but allowing fluctuations within that manifold.","internal_url":"https://www.academia.edu/95888227/Theories_of_rhythmogenesis","translated_internal_url":"","created_at":"2023-01-29T00:57:29.076-08:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":246424110,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[],"slug":"Theories_of_rhythmogenesis","translated_slug":"","page_count":null,"language":"en","content_type":"Work","owner":{"id":246424110,"first_name":"Maoz","middle_initials":null,"last_name":"Shamir","page_name":"MaozShamir","domain_name":"bgu","created_at":"2022-11-20T23:39:36.274-08:00","display_name":"Maoz Shamir","url":"https://bgu.academia.edu/MaozShamir"},"attachments":[],"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":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science"},{"id":26327,"name":"Medicine","url":"https://www.academia.edu/Documents/in/Medicine"},{"id":1239755,"name":"Neurosciences","url":"https://www.academia.edu/Documents/in/Neurosciences"}],"urls":[{"id":28486447,"url":"https://api.elsevier.com/content/article/PII:S0959438818302241?httpAccept=text/xml"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="95888226"><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/95888226/Rhythmogenesis_evolves_as_a_consequence_of_long_term_plasticity_of_inhibitory_synapses"><img alt="Research paper thumbnail of Rhythmogenesis evolves as a consequence of long-term plasticity of inhibitory synapses" class="work-thumbnail" src="https://attachments.academia-assets.com/97942839/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/95888226/Rhythmogenesis_evolves_as_a_consequence_of_long_term_plasticity_of_inhibitory_synapses">Rhythmogenesis evolves as a consequence of long-term plasticity of inhibitory synapses</a></div><div class="wp-workCard_item"><span>Scientific Reports</span><span>, 2018</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Brain rhythms are widely believed to reflect numerous cognitive processes. Changes in rhythmicity...</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">Brain rhythms are widely believed to reflect numerous cognitive processes. Changes in rhythmicity have been associated with pathological states. However, the mechanism underlying these rhythms remains unknown. Here, we present a theoretical analysis of the evolvement of rhythm generating capabilities in neuronal circuits. We tested the hypothesis that brain rhythms can be acquired via an intrinsic unsupervised learning process of activity dependent plasticity. Specifically, we focused on spike timing dependent plasticity (STDP) of inhibitory synapses. We detail how rhythmicity can develop via STDP under certain conditions that serve as a natural prediction of the hypothesis. We show how global features of the STDP rule govern and stabilize the resultant rhythmic activity. Finally, we demonstrate how rhythmicity is retained even in the face of synaptic variability. This study suggests a role for inhibitory plasticity that is beyond homeostatic processes.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="fd13c95e0a134bdea4b057cb903796a8" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":97942839,"asset_id":95888226,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/97942839/download_file?st=MTczMjgzMTA2Miw4LjIyMi4yMDguMTQ2&st=MTczMjgzMTA2MSw4LjIyMi4yMDguMTQ2&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="95888226"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="95888226"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 95888226; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=95888226]").text(description); $(".js-view-count[data-work-id=95888226]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 95888226; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='95888226']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 95888226, container: "", }); });</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-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.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: "fd13c95e0a134bdea4b057cb903796a8" } } $('.js-work-strip[data-work-id=95888226]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":95888226,"title":"Rhythmogenesis evolves as a consequence of long-term plasticity of inhibitory synapses","translated_title":"","metadata":{"abstract":"Brain rhythms are widely believed to reflect numerous cognitive processes. 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In certain cases changes in oscillatory activity has been associated with pathological states. Although the specific role of these oscillations has yet to be determined, it is clear that neuronal oscillations are abundant in the central nervous system. These observations raise the question of the origin of these oscillations; and specifically whether the mechanisms responsible for the generation and stabilization of these oscillations are genetically hard-wired or whether they can be acquired via a learning process.Here we focus on spike timing dependent plasticity (STDP) to investigate whether oscillatory activity can emerge in a neuronal network via an unsupervised learning process of STDP dynamics, and if so, what features of the STDP learning rule govern and stabilize the resultant oscillatory activity?Here, the STDP dynamics of the effective coupling between two competing neurona...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="d6b21efc0264f4dfc6a815dd12f09c7b" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":97942858,"asset_id":95888225,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/97942858/download_file?st=MTczMjgzMTA2Miw4LjIyMi4yMDguMTQ2&st=MTczMjgzMTA2MSw4LjIyMi4yMDguMTQ2&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="95888225"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="95888225"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 95888225; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=95888225]").text(description); $(".js-view-count[data-work-id=95888225]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 95888225; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='95888225']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 95888225, container: "", }); });</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-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.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: "d6b21efc0264f4dfc6a815dd12f09c7b" } } $('.js-work-strip[data-work-id=95888225]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":95888225,"title":"Emergence of oscillations via spike timing dependent plasticity","translated_title":"","metadata":{"abstract":"Neuronal oscillatory activity has been reported in relation to a wide range of cognitive processes. In certain cases changes in oscillatory activity has been associated with pathological states. Although the specific role of these oscillations has yet to be determined, it is clear that neuronal oscillations are abundant in the central nervous system. These observations raise the question of the origin of these oscillations; and specifically whether the mechanisms responsible for the generation and stabilization of these oscillations are genetically hard-wired or whether they can be acquired via a learning process.Here we focus on spike timing dependent plasticity (STDP) to investigate whether oscillatory activity can emerge in a neuronal network via an unsupervised learning process of STDP dynamics, and if so, what features of the STDP learning rule govern and stabilize the resultant oscillatory activity?Here, the STDP dynamics of the effective coupling between two competing neurona...","publisher":"Cold Spring Harbor Laboratory","publication_date":{"day":null,"month":null,"year":2018,"errors":{}}},"translated_abstract":"Neuronal oscillatory activity has been reported in relation to a wide range of cognitive processes. In certain cases changes in oscillatory activity has been associated with pathological states. 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These observations raise the question of the origin of these oscillations; and specifically whether the mechanisms responsible for the generation and stabilization of these oscillations are genetically hard-wired or whether they can be acquired via a learning process.Here we focus on spike timing dependent plasticity (STDP) to investigate whether oscillatory activity can emerge in a neuronal network via an unsupervised learning process of STDP dynamics, and if so, what features of the STDP learning rule govern and stabilize the resultant oscillatory activity?Here, the STDP dynamics of the effective coupling between two competing neurona...","internal_url":"https://www.academia.edu/95888225/Emergence_of_oscillations_via_spike_timing_dependent_plasticity","translated_internal_url":"","created_at":"2023-01-29T00:57:28.706-08:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":246424110,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[{"id":97942858,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/97942858/thumbnails/1.jpg","file_name":"269712.full.pdf","download_url":"https://www.academia.edu/attachments/97942858/download_file?st=MTczMjgzMTA2Miw4LjIyMi4yMDguMTQ2&st=MTczMjgzMTA2MSw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Emergence_of_oscillations_via_spike_timi.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/97942858/269712.full-libre.pdf?1674994232=\u0026response-content-disposition=attachment%3B+filename%3DEmergence_of_oscillations_via_spike_timi.pdf\u0026Expires=1732834661\u0026Signature=bkOIaeMjvWHA6gUCVu-DjdlfijiFbPLPj87Yh5X5q87B7cTw5oE-fu67Ym5cb1i07QIETDCfjqBNOLFs08BmT6kErSYF843YR2kP8rlCG9UrTqOO-ME5pbbobMstY0hAKhihkp~pX~g6YblHTTw1f-seSgQIUOQKD3sgeQK-9pfQ5CS2RDcSgYDnKMj77yoyyImtd7a2qappJEARjinX3aLSKNuVjivUDH78W2uVpCIioCobSb0Bn-KWMGBsjyxTcqxWRHsm7i2PV878GNmGoyEA16R0dtLuVBeqTZ9yVwCQuocXaoxLWwtFn695cjyU1KgmYhTPZOy1Z86juAv32Q__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Emergence_of_oscillations_via_spike_timing_dependent_plasticity","translated_slug":"","page_count":19,"language":"en","content_type":"Work","owner":{"id":246424110,"first_name":"Maoz","middle_initials":null,"last_name":"Shamir","page_name":"MaozShamir","domain_name":"bgu","created_at":"2022-11-20T23:39:36.274-08:00","display_name":"Maoz Shamir","url":"https://bgu.academia.edu/MaozShamir"},"attachments":[{"id":97942858,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/97942858/thumbnails/1.jpg","file_name":"269712.full.pdf","download_url":"https://www.academia.edu/attachments/97942858/download_file?st=MTczMjgzMTA2Miw4LjIyMi4yMDguMTQ2&st=MTczMjgzMTA2MSw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Emergence_of_oscillations_via_spike_timi.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/97942858/269712.full-libre.pdf?1674994232=\u0026response-content-disposition=attachment%3B+filename%3DEmergence_of_oscillations_via_spike_timi.pdf\u0026Expires=1732834661\u0026Signature=bkOIaeMjvWHA6gUCVu-DjdlfijiFbPLPj87Yh5X5q87B7cTw5oE-fu67Ym5cb1i07QIETDCfjqBNOLFs08BmT6kErSYF843YR2kP8rlCG9UrTqOO-ME5pbbobMstY0hAKhihkp~pX~g6YblHTTw1f-seSgQIUOQKD3sgeQK-9pfQ5CS2RDcSgYDnKMj77yoyyImtd7a2qappJEARjinX3aLSKNuVjivUDH78W2uVpCIioCobSb0Bn-KWMGBsjyxTcqxWRHsm7i2PV878GNmGoyEA16R0dtLuVBeqTZ9yVwCQuocXaoxLWwtFn695cjyU1KgmYhTPZOy1Z86juAv32Q__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":161,"name":"Neuroscience","url":"https://www.academia.edu/Documents/in/Neuroscience"},{"id":498,"name":"Physics","url":"https://www.academia.edu/Documents/in/Physics"},{"id":7710,"name":"Biology","url":"https://www.academia.edu/Documents/in/Biology"}],"urls":[{"id":28486445,"url":"https://syndication.highwire.org/content/doi/10.1101/269712"}]}, dispatcherData: dispatcherData }); 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The accuracy of latency codes was studied in the past using a simplified readout algorithm termed the temporal-winner-take-all (tWTA). The tWTA is a competitive readout algorithm in which populations of neurons with a similar decision preference compete, and the algorithm selects according to the preference of the population that reaches the decision threshold first. It has been shown that this algorithm can account for accurate decisions among a small number of alternatives during short biologically relevant time periods. However, one of the major points of criticism of latency codes has been that it is unclear how can such a readout be implemented by the central nervous system. Here we show that the solution to this long standing puzzle may be rather simple. We suggest a mechanism that is based on reciprocal inhibition architecture, similar to that of the conventional winner-take-all, and show that under a wide range of parameters this mechanism is sufficient to implement the tWTA algorithm. This is done by first analyzing a rate toy model, and demonstrating its ability to discriminate short latency differences between its inputs. We then study the sensitivity of this mechanism to fine-tuning of its initial conditions, and show that it is robust to wide range of noise levels in the initial conditions. These results are then generalized to a Hodgkin-Huxley type of neuron model, using numerical simulations. Latency codes have been criticized for requiring a reliable stimulus-onset detection mechanism as a reference for measuring latency. Here we show that this frequent assumption does not hold, and that, an additional onset estimator is not needed to trigger this simple tWTA mechanism.","publication_date":{"day":null,"month":null,"year":2016,"errors":{}},"publication_name":"Frontiers in Computational Neuroscience","grobid_abstract_attachment_id":97942856},"translated_abstract":null,"internal_url":"https://www.academia.edu/95888224/A_Readout_Mechanism_for_Latency_Codes","translated_internal_url":"","created_at":"2023-01-29T00:57:28.540-08:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":246424110,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[{"id":97942856,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/97942856/thumbnails/1.jpg","file_name":"3ec5e6a27d02cc3178373cccaac1a15ab085.pdf","download_url":"https://www.academia.edu/attachments/97942856/download_file?st=MTczMjgzMTA2Miw4LjIyMi4yMDguMTQ2&st=MTczMjgzMTA2MSw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"A_Readout_Mechanism_for_Latency_Codes.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/97942856/3ec5e6a27d02cc3178373cccaac1a15ab085-libre.pdf?1674994216=\u0026response-content-disposition=attachment%3B+filename%3DA_Readout_Mechanism_for_Latency_Codes.pdf\u0026Expires=1732834661\u0026Signature=SBKJdV5FV8zYncr8S7eTtvTGtKN~xsE3YR6bbbJnlqdeisS6-1VAxCWFyY0l7egUqTzpWVRHyGXnuykbUxxH-Z-AGsQ28-ArBV0~hSLBhioTnOj~aYctUWUiIdl4v8djyIbEC4esWpOYLY~P7joa~J2h-HAVlxa88t66SmO-WcNu8jypg59WXjbfps8nvnjK0749iVJjaX~esDXqALi0u9gMPWf41OB0LeZlJCetqtiq-RmtxaTwr2MjIaMvo4ZGiX0Y2LFwW~9dy7GcvHB-dCjhFanCHETuvPY9EGyVo5W4TSiMe0OFo3w3GST5-BIWZivVsENx6y~MzWQea9qbyA__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"A_Readout_Mechanism_for_Latency_Codes","translated_slug":"","page_count":9,"language":"en","content_type":"Work","owner":{"id":246424110,"first_name":"Maoz","middle_initials":null,"last_name":"Shamir","page_name":"MaozShamir","domain_name":"bgu","created_at":"2022-11-20T23:39:36.274-08:00","display_name":"Maoz Shamir","url":"https://bgu.academia.edu/MaozShamir"},"attachments":[{"id":97942856,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/97942856/thumbnails/1.jpg","file_name":"3ec5e6a27d02cc3178373cccaac1a15ab085.pdf","download_url":"https://www.academia.edu/attachments/97942856/download_file?st=MTczMjgzMTA2Miw4LjIyMi4yMDguMTQ2&st=MTczMjgzMTA2MSw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"A_Readout_Mechanism_for_Latency_Codes.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/97942856/3ec5e6a27d02cc3178373cccaac1a15ab085-libre.pdf?1674994216=\u0026response-content-disposition=attachment%3B+filename%3DA_Readout_Mechanism_for_Latency_Codes.pdf\u0026Expires=1732834661\u0026Signature=SBKJdV5FV8zYncr8S7eTtvTGtKN~xsE3YR6bbbJnlqdeisS6-1VAxCWFyY0l7egUqTzpWVRHyGXnuykbUxxH-Z-AGsQ28-ArBV0~hSLBhioTnOj~aYctUWUiIdl4v8djyIbEC4esWpOYLY~P7joa~J2h-HAVlxa88t66SmO-WcNu8jypg59WXjbfps8nvnjK0749iVJjaX~esDXqALi0u9gMPWf41OB0LeZlJCetqtiq-RmtxaTwr2MjIaMvo4ZGiX0Y2LFwW~9dy7GcvHB-dCjhFanCHETuvPY9EGyVo5W4TSiMe0OFo3w3GST5-BIWZivVsENx6y~MzWQea9qbyA__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science"},{"id":26327,"name":"Medicine","url":"https://www.academia.edu/Documents/in/Medicine"},{"id":244814,"name":"Clinical Sciences","url":"https://www.academia.edu/Documents/in/Clinical_Sciences"},{"id":810972,"name":"Mechanism in Biology","url":"https://www.academia.edu/Documents/in/Mechanism_in_Biology"},{"id":1239755,"name":"Neurosciences","url":"https://www.academia.edu/Documents/in/Neurosciences"}],"urls":[{"id":28486444,"url":"http://journal.frontiersin.org/article/10.3389/fncom.2016.00107/full"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="95888223"><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/95888223/Oscillations_via_Spike_Timing_Dependent_Plasticity_in_a_Feed_Forward_Model"><img alt="Research paper thumbnail of Oscillations via Spike-Timing Dependent Plasticity in a Feed-Forward Model" class="work-thumbnail" src="https://attachments.academia-assets.com/97942860/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/95888223/Oscillations_via_Spike_Timing_Dependent_Plasticity_in_a_Feed_Forward_Model">Oscillations via Spike-Timing Dependent Plasticity in a Feed-Forward Model</a></div><div class="wp-workCard_item"><span>PLoS computational biology</span><span>, 2016</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Neuronal oscillatory activity has been reported in relation to a wide range of cognitive processe...</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">Neuronal oscillatory activity has been reported in relation to a wide range of cognitive processes including the encoding of external stimuli, attention, and learning. Although the specific role of these oscillations has yet to be determined, it is clear that neuronal oscillations are abundant in the central nervous system. This raises the question of the origin of these oscillations: are the mechanisms for generating these oscillations genetically hard-wired or can they be acquired via a learning process? Here, we study the conditions under which oscillatory activity emerges through a process of spike timing dependent plasticity (STDP) in a feed-forward architecture. First, we analyze the effect of oscillations on STDP-driven synaptic dynamics of a single synapse, and study how the parameters that characterize the STDP rule and the oscillations affect the resultant synaptic weight. Next, we analyze STDP-driven synaptic dynamics of a pre-synaptic population of neurons onto a single ...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="cdeafe1a80cb399730d60245ccaaeb53" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":97942860,"asset_id":95888223,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/97942860/download_file?st=MTczMjgzMTA2Miw4LjIyMi4yMDguMTQ2&st=MTczMjgzMTA2MSw4LjIyMi4yMDguMTQ2&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="95888223"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="95888223"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 95888223; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=95888223]").text(description); $(".js-view-count[data-work-id=95888223]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 95888223; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='95888223']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 95888223, container: "", }); });</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-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.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: "cdeafe1a80cb399730d60245ccaaeb53" } } $('.js-work-strip[data-work-id=95888223]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":95888223,"title":"Oscillations via Spike-Timing Dependent Plasticity in a Feed-Forward Model","translated_title":"","metadata":{"abstract":"Neuronal oscillatory activity has been reported in relation to a wide range of cognitive processes including the encoding of external stimuli, attention, and learning. Although the specific role of these oscillations has yet to be determined, it is clear that neuronal oscillations are abundant in the central nervous system. This raises the question of the origin of these oscillations: are the mechanisms for generating these oscillations genetically hard-wired or can they be acquired via a learning process? Here, we study the conditions under which oscillatory activity emerges through a process of spike timing dependent plasticity (STDP) in a feed-forward architecture. First, we analyze the effect of oscillations on STDP-driven synaptic dynamics of a single synapse, and study how the parameters that characterize the STDP rule and the oscillations affect the resultant synaptic weight. Next, we analyze STDP-driven synaptic dynamics of a pre-synaptic population of neurons onto a single ...","publication_date":{"day":null,"month":null,"year":2016,"errors":{}},"publication_name":"PLoS computational biology"},"translated_abstract":"Neuronal oscillatory activity has been reported in relation to a wide range of cognitive processes including the encoding of external stimuli, attention, and learning. Although the specific role of these oscillations has yet to be determined, it is clear that neuronal oscillations are abundant in the central nervous system. This raises the question of the origin of these oscillations: are the mechanisms for generating these oscillations genetically hard-wired or can they be acquired via a learning process? Here, we study the conditions under which oscillatory activity emerges through a process of spike timing dependent plasticity (STDP) in a feed-forward architecture. First, we analyze the effect of oscillations on STDP-driven synaptic dynamics of a single synapse, and study how the parameters that characterize the STDP rule and the oscillations affect the resultant synaptic weight. 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These equations are consistent with the results of the replica theory of these models.","publication_date":{"day":null,"month":null,"year":2000,"errors":{}},"publication_name":"Physical Review E","grobid_abstract_attachment_id":97942854},"translated_abstract":null,"internal_url":"https://www.academia.edu/95888221/Thouless_Anderson_Palmer_equations_for_neural_networks","translated_internal_url":"","created_at":"2023-01-29T00:57:28.151-08:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":246424110,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[{"id":97942854,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/97942854/thumbnails/1.jpg","file_name":"bd93e5c27165b4a6c98142782a750172ff44.pdf","download_url":"https://www.academia.edu/attachments/97942854/download_file?st=MTczMjgzMTA2Miw4LjIyMi4yMDguMTQ2&st=MTczMjgzMTA2MSw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Thouless_Anderson_Palmer_equations_for_n.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/97942854/bd93e5c27165b4a6c98142782a750172ff44-libre.pdf?1674994218=\u0026response-content-disposition=attachment%3B+filename%3DThouless_Anderson_Palmer_equations_for_n.pdf\u0026Expires=1732834661\u0026Signature=CRpddNOFbsLVIt5LfT0yg-6smokmlQTjF~YPOJpELagoIhj561-DHggQvVikaDfiyOzlHYynBIaRIBn~oX7dHM434t4O6H4b5AcsHcXI-Q7IQGGfJEhSCEs9PByptFFLcGqNqY10LqSrkruU8b4SJlUzUBGpHkANlcrHnyh7ksM~F0caV0QWbdTesQEeOs8qxPpSfvhRjMgLA9jyv-30StAH7ozoCNykRd6xrQkS~SUcTxaJToy4R2psLK2iUELp1HNWRSXWK4QxoEG2-cepmq4gkzUWfihllah0LZH~FgadxPf1AWG91cKWPBLlkebsWGPFFDVsFpx2csSd~2mIww__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Thouless_Anderson_Palmer_equations_for_neural_networks","translated_slug":"","page_count":6,"language":"en","content_type":"Work","owner":{"id":246424110,"first_name":"Maoz","middle_initials":null,"last_name":"Shamir","page_name":"MaozShamir","domain_name":"bgu","created_at":"2022-11-20T23:39:36.274-08:00","display_name":"Maoz Shamir","url":"https://bgu.academia.edu/MaozShamir"},"attachments":[{"id":97942854,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/97942854/thumbnails/1.jpg","file_name":"bd93e5c27165b4a6c98142782a750172ff44.pdf","download_url":"https://www.academia.edu/attachments/97942854/download_file?st=MTczMjgzMTA2Miw4LjIyMi4yMDguMTQ2&st=MTczMjgzMTA2MSw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Thouless_Anderson_Palmer_equations_for_n.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/97942854/bd93e5c27165b4a6c98142782a750172ff44-libre.pdf?1674994218=\u0026response-content-disposition=attachment%3B+filename%3DThouless_Anderson_Palmer_equations_for_n.pdf\u0026Expires=1732834661\u0026Signature=CRpddNOFbsLVIt5LfT0yg-6smokmlQTjF~YPOJpELagoIhj561-DHggQvVikaDfiyOzlHYynBIaRIBn~oX7dHM434t4O6H4b5AcsHcXI-Q7IQGGfJEhSCEs9PByptFFLcGqNqY10LqSrkruU8b4SJlUzUBGpHkANlcrHnyh7ksM~F0caV0QWbdTesQEeOs8qxPpSfvhRjMgLA9jyv-30StAH7ozoCNykRd6xrQkS~SUcTxaJToy4R2psLK2iUELp1HNWRSXWK4QxoEG2-cepmq4gkzUWfihllah0LZH~FgadxPf1AWG91cKWPBLlkebsWGPFFDVsFpx2csSd~2mIww__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"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":192257,"name":"Physical","url":"https://www.academia.edu/Documents/in/Physical"},{"id":639432,"name":"Replica","url":"https://www.academia.edu/Documents/in/Replica"},{"id":1211304,"name":"Artificial Neural Network","url":"https://www.academia.edu/Documents/in/Artificial_Neural_Network"}],"urls":[{"id":28486443,"url":"http://link.aps.org/article/10.1103/PhysRevE.61.1839"}]}, dispatcherData: dispatcherData }); 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However, the accuracy of such a mechanism has not been analyzed rigorously. Here, we investigate the utility of first spike latency for encoding information about the location of a sound source, based on the responses of inferior colliculus (IC) neurons in the guinea pig to interaural phase differences (IPDs). First spike latencies of many cells in the guinea pig IC show unimodal tuning to stimulus IPD. We investigated the discrimination accuracy of a simple latency code that estimates stimulus IPD from the preferred IPD of the single cell that fired first. Surprisingly, despite being based on only a single spike, the accuracy of the latency code is comparable to that of a conventional rate code computed over the entire response. We show that spontaneous firing limits the capacity of the latency code to accumulate information from large neural populations. This detrimental effect can be overcome by generalizing the latency code to estimate the stimulus IPD from the preferred IPDs of the population of cells that fired the first n spikes. In addition, we show that a good estimate of the neural response time to the stimulus, which can be obtained from the responses of the cells whose response latency is invariant to stimulus identity, limits the detrimental effect of spontaneous firing. Thus, a latency code may provide great improvement in response speed at a small cost to the accuracy of the decision.","publication_date":{"day":null,"month":null,"year":2011,"errors":{}},"publication_name":"Journal of Neuroscience","grobid_abstract_attachment_id":97942862},"translated_abstract":null,"internal_url":"https://www.academia.edu/95888220/First_Spike_Latency_Code_for_Interaural_Phase_Difference_Discrimination_in_the_Guinea_Pig_Inferior_Colliculus","translated_internal_url":"","created_at":"2023-01-29T00:57:27.987-08:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":246424110,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[{"id":97942862,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/97942862/thumbnails/1.jpg","file_name":"9192.full.pdf","download_url":"https://www.academia.edu/attachments/97942862/download_file?st=MTczMjgzMTA2Miw4LjIyMi4yMDguMTQ2&st=MTczMjgzMTA2MSw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"First_Spike_Latency_Code_for_Interaural.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/97942862/9192.full-libre.pdf?1674994218=\u0026response-content-disposition=attachment%3B+filename%3DFirst_Spike_Latency_Code_for_Interaural.pdf\u0026Expires=1732834661\u0026Signature=B5lZSwJ5n2o2paRACUAeJCytfffWX0XOzdxeaXGaUIPRVOg7ac~4kCyHgkMALw1DUg3npMURWhwmb084TzxtXKYE~EFxSaQAiOx6PoM9hBXVMlDH7nWganGitYyF53yVhTGDhs3zIDvcfzW7qPMSzH2dxnlAVAOgCnNO6X4KGyo3MAsSj9w7xwPqZnr3xLU1Gs5ddFWJoQdnBbA6Jl4vizH1Ep6tTFToFXU333O~a8~PmlKVdWYyzzDV12p4wK68vb-~YtMFEM8dExlPhvxxgbWzeRlFr3zUf9giOD4EMq0w04zZB3zI0GJGGBN4JVtCPBjvcgDfj-jAKHn29AUKpw__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"First_Spike_Latency_Code_for_Interaural_Phase_Difference_Discrimination_in_the_Guinea_Pig_Inferior_Colliculus","translated_slug":"","page_count":13,"language":"en","content_type":"Work","owner":{"id":246424110,"first_name":"Maoz","middle_initials":null,"last_name":"Shamir","page_name":"MaozShamir","domain_name":"bgu","created_at":"2022-11-20T23:39:36.274-08:00","display_name":"Maoz Shamir","url":"https://bgu.academia.edu/MaozShamir"},"attachments":[{"id":97942862,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/97942862/thumbnails/1.jpg","file_name":"9192.full.pdf","download_url":"https://www.academia.edu/attachments/97942862/download_file?st=MTczMjgzMTA2Miw4LjIyMi4yMDguMTQ2&st=MTczMjgzMTA2MSw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"First_Spike_Latency_Code_for_Interaural.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/97942862/9192.full-libre.pdf?1674994218=\u0026response-content-disposition=attachment%3B+filename%3DFirst_Spike_Latency_Code_for_Interaural.pdf\u0026Expires=1732834661\u0026Signature=B5lZSwJ5n2o2paRACUAeJCytfffWX0XOzdxeaXGaUIPRVOg7ac~4kCyHgkMALw1DUg3npMURWhwmb084TzxtXKYE~EFxSaQAiOx6PoM9hBXVMlDH7nWganGitYyF53yVhTGDhs3zIDvcfzW7qPMSzH2dxnlAVAOgCnNO6X4KGyo3MAsSj9w7xwPqZnr3xLU1Gs5ddFWJoQdnBbA6Jl4vizH1Ep6tTFToFXU333O~a8~PmlKVdWYyzzDV12p4wK68vb-~YtMFEM8dExlPhvxxgbWzeRlFr3zUf9giOD4EMq0w04zZB3zI0GJGGBN4JVtCPBjvcgDfj-jAKHn29AUKpw__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":161,"name":"Neuroscience","url":"https://www.academia.edu/Documents/in/Neuroscience"},{"id":300,"name":"Mathematics","url":"https://www.academia.edu/Documents/in/Mathematics"},{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science"},{"id":26327,"name":"Medicine","url":"https://www.academia.edu/Documents/in/Medicine"},{"id":176503,"name":"Synaptic Transmission","url":"https://www.academia.edu/Documents/in/Synaptic_Transmission"},{"id":193974,"name":"Neurons","url":"https://www.academia.edu/Documents/in/Neurons"},{"id":955727,"name":"Action Potentials","url":"https://www.academia.edu/Documents/in/Action_Potentials"},{"id":1505827,"name":"Inferior Colliculus","url":"https://www.academia.edu/Documents/in/Inferior_Colliculus"},{"id":2217019,"name":"Sound Localization","url":"https://www.academia.edu/Documents/in/Sound_Localization"},{"id":2922956,"name":"Psychology and Cognitive Sciences","url":"https://www.academia.edu/Documents/in/Psychology_and_Cognitive_Sciences"},{"id":3763225,"name":"Medical and Health Sciences","url":"https://www.academia.edu/Documents/in/Medical_and_Health_Sciences"},{"id":3769209,"name":"Guinea pigs","url":"https://www.academia.edu/Documents/in/Guinea_pigs"}],"urls":[{"id":28486442,"url":"https://syndication.highwire.org/content/doi/10.1523/JNEUROSCI.6193-10.2011"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="95888219"><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/95888219/Balancing_Feed_Forward_Excitation_and_Inhibition_via_Hebbian_Inhibitory_Synaptic_Plasticity"><img alt="Research paper thumbnail of Balancing Feed-Forward Excitation and Inhibition via Hebbian Inhibitory Synaptic Plasticity" class="work-thumbnail" src="https://attachments.academia-assets.com/97942857/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/95888219/Balancing_Feed_Forward_Excitation_and_Inhibition_via_Hebbian_Inhibitory_Synaptic_Plasticity">Balancing Feed-Forward Excitation and Inhibition via Hebbian Inhibitory Synaptic Plasticity</a></div><div class="wp-workCard_item"><span>PLoS Computational Biology</span><span>, 2012</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="3dd3a8b160db71d0dfcaf6bf26396306" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":97942857,"asset_id":95888219,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/97942857/download_file?st=MTczMjgzMTA2Miw4LjIyMi4yMDguMTQ2&st=MTczMjgzMTA2MSw4LjIyMi4yMDguMTQ2&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="95888219"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="95888219"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 95888219; 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There are two hypotheses as to the origin of this balance. One assumes that it results from a stable solution of the recurrent neuronal dynamics. This model can account for a balance of steady state excitation and inhibition without fine tuning of parameters, but not for transient inputs. The second hypothesis suggests that the feed forward excitatory and inhibitory inputs to a postsynaptic cell are already balanced. This latter hypothesis thus does account for the balance of transient inputs. However, it remains unclear what mechanism underlies the fine tuning required for balancing feed forward excitatory and inhibitory inputs. Here we investigated whether inhibitory synaptic plasticity is responsible for the balance of transient feed forward excitation and inhibition. We address this issue in the framework of a model characterizing the stochastic dynamics of temporally anti-symmetric Hebbian spike timing dependent plasticity of feed forward excitatory and inhibitory synaptic inputs to a single post-synaptic cell. Our analysis shows that inhibitory Hebbian plasticity generates 'negative feedback' that balances excitation and inhibition, which contrasts with the 'positive feedback' of excitatory Hebbian synaptic plasticity. 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="95888217"><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/95888217/Representation_of_Time_Varying_Stimuli_by_a_Network_Exhibiting_Oscillations_on_a_Faster_Time_Scale"><img alt="Research paper thumbnail of Representation of Time-Varying Stimuli by a Network Exhibiting Oscillations on a Faster Time Scale" class="work-thumbnail" src="https://attachments.academia-assets.com/97942868/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/95888217/Representation_of_Time_Varying_Stimuli_by_a_Network_Exhibiting_Oscillations_on_a_Faster_Time_Scale">Representation of Time-Varying Stimuli by a Network Exhibiting Oscillations on a Faster Time Scale</a></div><div class="wp-workCard_item"><span>PLoS Computational Biology</span><span>, 2009</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="b85b82c859054ff39763926fb182eab8" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":97942868,"asset_id":95888217,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/97942868/download_file?st=MTczMjgzMTA2Miw4LjIyMi4yMDguMTQ2&st=MTczMjgzMTA2Miw4LjIyMi4yMDguMTQ2&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="95888217"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="95888217"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 95888217; 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This raises the question whether gamma oscillations can be directly involved in the representation of time-varying stimuli, including stimuli whose time scale is longer than a gamma cycle. We are interested in the ability of the system to reliably distinguish different stimuli while being robust to stimulus variations such as uniform time-warp. We address this issue with a dynamical model of spiking neurons and study the response to an asymmetric sawtooth input current over a range of shape parameters. These parameters describe how fast the input current rises and falls in time. Our network consists of inhibitory and excitatory populations that are sufficient for generating oscillations in the gamma range. The oscillations period is about one-third of the stimulus duration. Embedded in this network is a subpopulation of excitatory cells that respond to the sawtooth stimulus and a subpopulation of cells that respond to an onset cue. The intrinsic gamma oscillations generate a temporally sparse code for the external stimuli. In this code, an excitatory cell may fire a single spike during a gamma cycle, depending on its tuning properties and on the temporal structure of the specific input; the identity of the stimulus is coded by the list of excitatory cells that fire during each cycle. We quantify the properties of this representation in a series of simulations and show that the sparseness of the code makes it robust to uniform warping of the time scale. 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