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Views</span></p><p class="data"><span class="js-profile-view-count"></span></p></div></span></div><div class="ri-section"><div class="ri-section-header"><span>Interests</span></div><div class="ri-tags-container"><a data-click-track="profile-user-info-expand-research-interests" data-has-card-for-ri-list="42086941" href="https://www.academia.edu/Documents/in/Philosophy_of_Biology"><div id="js-react-on-rails-context" style="display:none" data-rails-context="{"inMailer":false,"i18nLocale":"en","i18nDefaultLocale":"en","href":"https://independent.academia.edu/BarryRichmond3","location":"/BarryRichmond3","scheme":"https","host":"independent.academia.edu","port":null,"pathname":"/BarryRichmond3","search":null,"httpAcceptLanguage":null,"serverSide":false}"></div> <div class="js-react-on-rails-component" style="display:none" data-component-name="Pill" data-props="{"color":"gray","children":["Philosophy of Biology"]}" data-trace="false" 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data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/18825654/Decoding_of_neuronal_signals_in_visual_pattern_recognition"><img alt="Research paper thumbnail of Decoding of neuronal signals in visual pattern recognition" 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" rel="nofollow" href="https://www.academia.edu/18825654/Decoding_of_neuronal_signals_in_visual_pattern_recognition">Decoding of neuronal signals in visual pattern recognition</a></div><div class="wp-workCard_item wp-workCard--coauthors"><span>by </span><span><a class="" data-click-track="profile-work-strip-authors" href="https://independent.academia.edu/EmadEskandar">Emad Eskandar</a> and <a class="" data-click-track="profile-work-strip-authors" href="https://independent.academia.edu/BarryRichmond3">Barry Richmond</a></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="18825654"><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="18825654"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 18825654; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); 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})(["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" } } $('.js-work-strip[data-work-id=18825654]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":18825654,"title":"Decoding of neuronal signals in visual pattern 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href="https://www.academia.edu/20831274/Neuronal_Signals_in_the_Monkey_Ventral_Striatum_Related_to_Progress_through_a_Predictable_Series_of"><img alt="Research paper thumbnail of Neuronal Signals in the Monkey Ventral Striatum Related to Progress through a Predictable Series of" 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/20831274/Neuronal_Signals_in_the_Monkey_Ventral_Striatum_Related_to_Progress_through_a_Predictable_Series_of">Neuronal Signals in the Monkey Ventral Striatum Related to Progress through a Predictable Series of</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Single neurons in the ventral striatum of primates carry signals that are related to reward and m...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Single neurons in the ventral striatum of primates carry signals that are related to reward and motivation. When monkeys performed a task requiring one to three bar release trials to be completed successfully before a reward was given, they seemed more motivated as the rewarded trials approached; they responded more quickly and accurately. When the mon-keys were cued as to</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="20831274"><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="20831274"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 20831274; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=20831274]").text(description); $(".js-view-count[data-work-id=20831274]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 20831274; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='20831274']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 20831274, 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" } } $('.js-work-strip[data-work-id=20831274]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":20831274,"title":"Neuronal Signals in the Monkey Ventral Striatum Related to Progress through a Predictable Series of","translated_title":"","metadata":{"abstract":"Single neurons in the ventral striatum of primates carry signals that are related to reward and motivation. 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J. Neurophysiol. ...</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">key TE and perirhinal cortex: stimulus association related to reward schedules. J. Neurophysiol. 83: 1677-1692, 2000. Anatomic and behavioral evidence shows that TE and perirhinal cortices are two directly connected but distinct inferior temporal areas. Despite this distinctness, physiological properties of neurons in these two areas generally have been similar with neurons in both areas showing selectivity for complex visual</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="9b074880667f2bcfdc2961fa2e9cd971" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":41920795,"asset_id":20831273,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/41920795/download_file?st=MTczMjUyNzMzOSw4LjIyMi4yMDguMTQ2&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="20831273"><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="20831273"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 20831273; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=20831273]").text(description); $(".js-view-count[data-work-id=20831273]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 20831273; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='20831273']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 20831273, 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: "9b074880667f2bcfdc2961fa2e9cd971" } } $('.js-work-strip[data-work-id=20831273]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":20831273,"title":"Response Differences in Monkey TE and Perirhinal Cortex: Stimulus Association Related to Reward Schedules","translated_title":"","metadata":{"abstract":"key TE and perirhinal cortex: stimulus association related to reward schedules. 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When monkeys performed a task requiring one to three bar release trials to be completed successfully before a reward was given, they seemed more motivated as the rewarded trials approached; they responded more quickly and accurately. When the mon- keys were cued as</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="20831272"><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="20831272"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 20831272; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=20831272]").text(description); $(".js-view-count[data-work-id=20831272]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 20831272; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='20831272']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 20831272, 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" } } $('.js-work-strip[data-work-id=20831272]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":20831272,"title":"Neuronal Signals in the Monkey Ventral Striatum Related to Progress through a Predictable Series of Trials","translated_title":"","metadata":{"abstract":"Single neurons in the ventral striatum of primates carry signals that are related to reward and motivation. 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We...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">We would like to know whether the statistics of neuronal responses vary across cortical areas. We examined stimulus-elicited spike count response distributions in V1 and IT cortices of awake monkeys. In both areas the distribution of spike counts for each stimulus was well-described by a Gaussian, with the log of the variance in the spike count linearly related to the</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="653637a43f2ad230f72d35cdc07ab729" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":41586620,"asset_id":20831271,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/41586620/download_file?st=MTczMjUyNzMzOSw4LjIyMi4yMDguMTQ2&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="20831271"><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="20831271"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 20831271; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=20831271]").text(description); $(".js-view-count[data-work-id=20831271]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 20831271; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='20831271']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 20831271, 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: "653637a43f2ad230f72d35cdc07ab729" } } $('.js-work-strip[data-work-id=20831271]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":20831271,"title":"Coding Strategies in Monkey V1 and Inferior Temporal Cortices","translated_title":"","metadata":{"abstract":"We would like to know whether the statistics of neuronal responses vary across cortical areas. 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data-click-track="profile-work-strip-title" href="https://www.academia.edu/20831270/Anomalous_response_variability_in_a_balanced_cortical_network_model">Anomalous response variability in a balanced cortical network model</a></div><div class="wp-workCard_item"><span>Neurocomputing</span><span>, 2003</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="d9bb3af5a6c164b0cd83d0a9c52b3771" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":41586656,"asset_id":20831270,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/41586656/download_file?st=MTczMjUyNzMzOSw4LjIyMi4yMDguMTQ2&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 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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="20831269"><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/20831269/Decoding_spike_trains_instant_by_instant_using_order_statistics_and_the_mixture_of_Poissons_model"><img alt="Research paper thumbnail of Decoding spike trains instant by instant using order statistics and the mixture-of-Poissons model" class="work-thumbnail" src="https://attachments.academia-assets.com/41586690/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/20831269/Decoding_spike_trains_instant_by_instant_using_order_statistics_and_the_mixture_of_Poissons_model">Decoding spike trains instant by instant using order statistics and the mixture-of-Poissons model</a></div><div class="wp-workCard_item"><span>The Journal of neuroscience : the official journal of the Society for Neuroscience</span><span>, Jan 15, 2003</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">In the brain, spike trains are generated in time and presumably also interpreted as they unfold i...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">In the brain, spike trains are generated in time and presumably also interpreted as they unfold in time. Recent work (Oram et al., 1999; Baker and Lemon, 2000) suggests that in several areas of the monkey brain, individual spike times carry information because they reflect an underlying rate variation. Constructing a model based on this stochastic structure allows us to apply order statistics to decode spike trains instant by instant as spikes arrive or do not. Order statistics are time-consuming to compute in the general case. We demonstrate that data from neurons in primary visual cortex are well fit by a mixture of Poisson processes; in this special case, our computations are substantially faster. In these data, spike timing contributed information beyond that available from the spike count throughout the trial. At the end of the trial, a decoder based on the mixture-of-Poissons model correctly decoded about three times as many trials as expected by chance, compared with approxim...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="0be4b60bbb82970cdad33b3b7974453d" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":41586690,"asset_id":20831269,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/41586690/download_file?st=MTczMjUyNzMzOSw4LjIyMi4yMDguMTQ2&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="20831269"><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="20831269"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 20831269; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=20831269]").text(description); $(".js-view-count[data-work-id=20831269]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 20831269; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='20831269']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 20831269, 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: "0be4b60bbb82970cdad33b3b7974453d" } } $('.js-work-strip[data-work-id=20831269]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":20831269,"title":"Decoding spike trains instant by instant using order statistics and the mixture-of-Poissons model","translated_title":"","metadata":{"abstract":"In the brain, spike trains are generated in time and presumably also interpreted as they unfold in time. Recent work (Oram et al., 1999; Baker and Lemon, 2000) suggests that in several areas of the monkey brain, individual spike times carry information because they reflect an underlying rate variation. Constructing a model based on this stochastic structure allows us to apply order statistics to decode spike trains instant by instant as spikes arrive or do not. Order statistics are time-consuming to compute in the general case. We demonstrate that data from neurons in primary visual cortex are well fit by a mixture of Poisson processes; in this special case, our computations are substantially faster. In these data, spike timing contributed information beyond that available from the spike count throughout the trial. At the end of the trial, a decoder based on the mixture-of-Poissons model correctly decoded about three times as many trials as expected by chance, compared with approxim...","publication_date":{"day":15,"month":1,"year":2003,"errors":{}},"publication_name":"The Journal of neuroscience : the official journal of the Society for Neuroscience"},"translated_abstract":"In the brain, spike trains are generated in time and presumably also interpreted as they unfold in time. Recent work (Oram et al., 1999; Baker and Lemon, 2000) suggests that in several areas of the monkey brain, individual spike times carry information because they reflect an underlying rate variation. Constructing a model based on this stochastic structure allows us to apply order statistics to decode spike trains instant by instant as spikes arrive or do not. Order statistics are time-consuming to compute in the general case. 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significance of visual cues for reward schedules requires rhinal cortex" 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/20831267/Learning_motivational_significance_of_visual_cues_for_reward_schedules_requires_rhinal_cortex">Learning motivational significance of visual cues for reward schedules requires rhinal cortex</a></div><div class="wp-workCard_item"><span>Nature Neuroscience</span><span>, 2000</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">The limbic system is necessary to associate stimuli with their motivational and emotional signifi...</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">The limbic system is necessary to associate stimuli with their motivational and emotional significance. The perirhinal cortex is directly connected to this system, and neurons in this region carry signals related to a monkey&#39;s progress through visually cued reward schedules. This task manipulates motivation by displaying different visual cues to indicate the amount of work remaining until reward delivery. 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The perirhinal cortex is directly connected to this system, and neurons in this region carry signals related to a monkey\u0026#39;s progress through visually cued reward schedules. This task manipulates motivation by displaying different visual cues to indicate the amount of work remaining until reward delivery. We","publication_date":{"day":null,"month":null,"year":2000,"errors":{}},"publication_name":"Nature Neuroscience"},"translated_abstract":"The limbic system is necessary to associate stimuli with their motivational and emotional significance. The perirhinal cortex is directly connected to this system, and neurons in this region carry signals related to a monkey\u0026#39;s progress through visually cued reward schedules. This task manipulates motivation by displaying different visual cues to indicate the amount of work remaining until reward delivery. 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Liu","title":"Learning motivational significance of visual cues for reward schedules requires rhinal cortex"},{"id":14142782,"work_id":20831267,"tagging_user_id":42086941,"tagged_user_id":53481677,"co_author_invite_id":3250470,"email":"m***e@mail.nih.gov","display_order":4194304,"name":"Elisabeth Murray","title":"Learning motivational significance of visual cues for reward schedules requires rhinal cortex"}],"downloadable_attachments":[],"slug":"Learning_motivational_significance_of_visual_cues_for_reward_schedules_requires_rhinal_cortex","translated_slug":"","page_count":null,"language":"en","content_type":"Work","owner":{"id":42086941,"first_name":"Barry","middle_initials":null,"last_name":"Richmond","page_name":"BarryRichmond3","domain_name":"independent","created_at":"2016-01-25T13:40:57.648-08:00","display_name":"Barry Richmond","url":"https://independent.academia.edu/BarryRichmond3"},"attachments":[],"research_interests":[{"id":237,"name":"Cognitive Science","url":"https://www.academia.edu/Documents/in/Cognitive_Science"},{"id":42162,"name":"Emotions","url":"https://www.academia.edu/Documents/in/Emotions"},{"id":46406,"name":"Motivation","url":"https://www.academia.edu/Documents/in/Motivation"},{"id":49021,"name":"Reward","url":"https://www.academia.edu/Documents/in/Reward"},{"id":72150,"name":"Discrimination Learning","url":"https://www.academia.edu/Documents/in/Discrimination_Learning"},{"id":88325,"name":"Cues","url":"https://www.academia.edu/Documents/in/Cues"},{"id":99234,"name":"Animals","url":"https://www.academia.edu/Documents/in/Animals"},{"id":115437,"name":"Nature Neuroscience","url":"https://www.academia.edu/Documents/in/Nature_Neuroscience"},{"id":193974,"name":"Neurons","url":"https://www.academia.edu/Documents/in/Neurons"},{"id":230879,"name":"Denervation","url":"https://www.academia.edu/Documents/in/Denervation"},{"id":313989,"name":"Visual Cues","url":"https://www.academia.edu/Documents/in/Visual_Cues"},{"id":319071,"name":"Entorhinal Cortex","url":"https://www.academia.edu/Documents/in/Entorhinal_Cortex"},{"id":494840,"name":"Limbic System","url":"https://www.academia.edu/Documents/in/Limbic_System"},{"id":573267,"name":"Macaca Mulatta","url":"https://www.academia.edu/Documents/in/Macaca_Mulatta"},{"id":1239755,"name":"Neurosciences","url":"https://www.academia.edu/Documents/in/Neurosciences"}],"urls":[]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="20831266"><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/20831266/Role_of_perirhinal_cortex_in_object_perception_memory_and_associations"><img alt="Research paper thumbnail of Role of perirhinal cortex in object perception, memory, and associations" 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/20831266/Role_of_perirhinal_cortex_in_object_perception_memory_and_associations">Role of perirhinal cortex in object perception, memory, and associations</a></div><div class="wp-workCard_item"><span>Current Opinion in Neurobiology - CURR OPIN NEUROBIOL</span><span>, 2001</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">The perirhinal cortex plays a key role in acquiring knowledge about objects. It contributes to at...</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">The perirhinal cortex plays a key role in acquiring knowledge about objects. It contributes to at least four cognitive functions, and recent findings provide new insights into how the perirhinal cortex contributes to each: first, it contributes to recognition memory in an automatic fashion; second, it probably contributes to perception as well as memory; third, it helps identify objects by associating together the different sensory features of an object; and fourth, it associates objects with other objects and with abstractions.</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="20831266"><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="20831266"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 20831266; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=20831266]").text(description); $(".js-view-count[data-work-id=20831266]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 20831266; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='20831266']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 20831266, 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" } } $('.js-work-strip[data-work-id=20831266]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":20831266,"title":"Role of perirhinal cortex in object perception, memory, and associations","translated_title":"","metadata":{"abstract":"The perirhinal cortex plays a key role in acquiring knowledge about objects. 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It contributes to at least four cognitive functions, and recent findings provide new insights into how the perirhinal cortex contributes to each: first, it contributes to recognition memory in an automatic fashion; second, it probably contributes to perception as well as memory; third, it helps identify objects by associating together the different sensory features of an object; and fourth, it associates objects with other objects and with abstractions.","internal_url":"https://www.academia.edu/20831266/Role_of_perirhinal_cortex_in_object_perception_memory_and_associations","translated_internal_url":"","created_at":"2016-01-26T05:46:07.037-08:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":42086941,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[{"id":14142783,"work_id":20831266,"tagging_user_id":42086941,"tagged_user_id":53481677,"co_author_invite_id":3250470,"email":"m***e@mail.nih.gov","display_order":0,"name":"Elisabeth Murray","title":"Role of perirhinal cortex in object perception, memory, and associations"}],"downloadable_attachments":[],"slug":"Role_of_perirhinal_cortex_in_object_perception_memory_and_associations","translated_slug":"","page_count":null,"language":"en","content_type":"Work","owner":{"id":42086941,"first_name":"Barry","middle_initials":null,"last_name":"Richmond","page_name":"BarryRichmond3","domain_name":"independent","created_at":"2016-01-25T13:40:57.648-08:00","display_name":"Barry Richmond","url":"https://independent.academia.edu/BarryRichmond3"},"attachments":[],"research_interests":[{"id":237,"name":"Cognitive Science","url":"https://www.academia.edu/Documents/in/Cognitive_Science"},{"id":867,"name":"Perception","url":"https://www.academia.edu/Documents/in/Perception"},{"id":17113,"name":"Face recognition (Psychology)","url":"https://www.academia.edu/Documents/in/Face_recognition_Psychology_"},{"id":24782,"name":"Recognition memory","url":"https://www.academia.edu/Documents/in/Recognition_memory"},{"id":46858,"name":"Memory","url":"https://www.academia.edu/Documents/in/Memory"},{"id":57557,"name":"Temporal Lobe","url":"https://www.academia.edu/Documents/in/Temporal_Lobe"},{"id":64568,"name":"Humans","url":"https://www.academia.edu/Documents/in/Humans"},{"id":72150,"name":"Discrimination Learning","url":"https://www.academia.edu/Documents/in/Discrimination_Learning"},{"id":99234,"name":"Animals","url":"https://www.academia.edu/Documents/in/Animals"},{"id":118256,"name":"Object Perception","url":"https://www.academia.edu/Documents/in/Object_Perception"},{"id":441653,"name":"Cognitive Function","url":"https://www.academia.edu/Documents/in/Cognitive_Function"},{"id":1239755,"name":"Neurosciences","url":"https://www.academia.edu/Documents/in/Neurosciences"}],"urls":[{"id":6239514,"url":"http://www.sciencedirect.com/science/article/pii/S0959438800001951"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="20831265"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/20831265/Role_of_inferior_ROLLS"><img alt="Research paper thumbnail of Role of inferior ROLLS" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" rel="nofollow" href="https://www.academia.edu/20831265/Role_of_inferior_ROLLS">Role of inferior ROLLS</a></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="20831265"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="20831265"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 20831265; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=20831265]").text(description); $(".js-view-count[data-work-id=20831265]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 20831265; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='20831265']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 20831265, 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" } } $('.js-work-strip[data-work-id=20831265]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":20831265,"title":"Role of inferior ROLLS","translated_title":"","metadata":{"publication_date":{"day":null,"month":null,"year":1984,"errors":{}}},"translated_abstract":null,"internal_url":"https://www.academia.edu/20831265/Role_of_inferior_ROLLS","translated_internal_url":"","created_at":"2016-01-26T05:46:06.912-08:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":42086941,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[{"id":13795212,"work_id":20831265,"tagging_user_id":42086941,"tagged_user_id":null,"co_author_invite_id":215356,"email":"d***n@yahoo.fr","display_order":0,"name":"L. Optican","title":"Role of inferior ROLLS"},{"id":14142772,"work_id":20831265,"tagging_user_id":42086941,"tagged_user_id":38914104,"co_author_invite_id":null,"email":"e***r@partners.org","display_order":4194304,"name":"Emad Eskandar","title":"Role of inferior ROLLS"}],"downloadable_attachments":[],"slug":"Role_of_inferior_ROLLS","translated_slug":"","page_count":null,"language":"en","content_type":"Work","owner":{"id":42086941,"first_name":"Barry","middle_initials":null,"last_name":"Richmond","page_name":"BarryRichmond3","domain_name":"independent","created_at":"2016-01-25T13:40:57.648-08:00","display_name":"Barry Richmond","url":"https://independent.academia.edu/BarryRichmond3"},"attachments":[],"research_interests":[],"urls":[]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="20831264"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/20831264/First_spike_timing_precision_of_monkey_V1_neurons"><img alt="Research paper thumbnail of First-spike timing precision of monkey V1 neurons" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" rel="nofollow" href="https://www.academia.edu/20831264/First_spike_timing_precision_of_monkey_V1_neurons">First-spike timing precision of monkey V1 neurons</a></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="20831264"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="20831264"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 20831264; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=20831264]").text(description); $(".js-view-count[data-work-id=20831264]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 20831264; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='20831264']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 20831264, 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" } } $('.js-work-strip[data-work-id=20831264]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":20831264,"title":"First-spike timing precision of monkey V1 neurons","translated_title":"","metadata":{"publication_date":{"day":null,"month":null,"year":1997,"errors":{}}},"translated_abstract":null,"internal_url":"https://www.academia.edu/20831264/First_spike_timing_precision_of_monkey_V1_neurons","translated_internal_url":"","created_at":"2016-01-26T05:46:06.785-08:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":42086941,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[{"id":13795206,"work_id":20831264,"tagging_user_id":42086941,"tagged_user_id":null,"co_author_invite_id":322394,"email":"h***z@nordita.dk","display_order":0,"name":"J. Hertz","title":"First-spike timing precision of monkey V1 neurons"}],"downloadable_attachments":[],"slug":"First_spike_timing_precision_of_monkey_V1_neurons","translated_slug":"","page_count":null,"language":"en","content_type":"Work","owner":{"id":42086941,"first_name":"Barry","middle_initials":null,"last_name":"Richmond","page_name":"BarryRichmond3","domain_name":"independent","created_at":"2016-01-25T13:40:57.648-08:00","display_name":"Barry Richmond","url":"https://independent.academia.edu/BarryRichmond3"},"attachments":[],"research_interests":[],"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="20831263"><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/20831263/DNA_targeting_of_rhinal_cortex_D2_receptor_protein_reversibly_blocks_learning_of_cues_that_predict_reward"><img alt="Research paper thumbnail of DNA targeting of rhinal cortex D2 receptor protein reversibly blocks learning of cues that predict reward" class="work-thumbnail" src="https://attachments.academia-assets.com/41920896/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/20831263/DNA_targeting_of_rhinal_cortex_D2_receptor_protein_reversibly_blocks_learning_of_cues_that_predict_reward">DNA targeting of rhinal cortex D2 receptor protein reversibly blocks learning of cues that predict reward</a></div><div class="wp-workCard_item"><span>Proceedings of the National Academy of Sciences</span><span>, 2004</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">When schedules of several operant trials must be successfully completed to obtain a reward, monke...</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">When schedules of several operant trials must be successfully completed to obtain a reward, monkeys quickly learn to adjust their behavioral performance by using visual cues that signal how many trials have been completed and how many remain in the current schedule. Bilateral rhinal (perirhinal and entorhinal) cortex ablations irreversibly prevent this learning. Here, we apply a recombinant DNA technique to investigate the role of dopamine D2 receptor in rhinal cortex for this type of learning. Rhinal cortex was injected with a DNA construct that significantly decreased D2 receptor ligand binding and temporarily produced the same profound learning deficit seen after ablation. However, unlike after ablation, the D2 receptor-targeted, DNA-treated monkeys recovered cue-related learning after 11-19 weeks. Injecting a DNA construct that decreased N-methyl-d-aspartate but not D2 receptor ligand binding did not interfere with learning associations between the cues and the schedules. A second D2 receptor-targeted DNA treatment administered after either recovery from a first D2 receptor-targeted DNA treatment (one monkey), after N-methyl-d-aspartate receptor-targeted DNA treatment (two monkeys), or after a vector control treatment (one monkey) also induced a learning deficit of similar duration. These results suggest that the D2 receptor in primate rhinal cortex is essential for learning to relate the visual cues to the schedules. The specificity of the receptor manipulation reported here suggests that this approach could be generalized in this or other brain pathways to relate molecular mechanisms to cognitive functions.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="1a705a2e14d745984f62007a8f623436" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":41920896,"asset_id":20831263,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/41920896/download_file?st=MTczMjUyNzMzOSw4LjIyMi4yMDguMTQ2&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="20831263"><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="20831263"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 20831263; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=20831263]").text(description); $(".js-view-count[data-work-id=20831263]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 20831263; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='20831263']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 20831263, 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: "1a705a2e14d745984f62007a8f623436" } } $('.js-work-strip[data-work-id=20831263]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":20831263,"title":"DNA targeting of rhinal cortex D2 receptor protein reversibly blocks learning of cues that predict reward","translated_title":"","metadata":{"abstract":"When schedules of several operant trials must be successfully completed to obtain a reward, monkeys quickly learn to adjust their behavioral performance by using visual cues that signal how many trials have been completed and how many remain in the current schedule. 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class="js-work-more-abstract-truncated">... References. [1] JL Gallant, CE Connor and DC Van Essen, Neural activity in V1, V2 and V4 ... ...</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">... References. [1] JL Gallant, CE Connor and DC Van Essen, Neural activity in V1, V2 and V4 ... Tim A. 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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="20831259"><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/20831259/Decoding_cortical_neuronal_signals_Network_models_information_estimation_and_spatial_tuning"><img alt="Research paper thumbnail of Decoding cortical neuronal signals: Network models, information estimation and spatial tuning" 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/20831259/Decoding_cortical_neuronal_signals_Network_models_information_estimation_and_spatial_tuning">Decoding cortical neuronal signals: Network models, information estimation and spatial tuning</a></div><div class="wp-workCard_item"><span>Journal of Computational Neuroscience</span><span>, 1994</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">We have studied the encoding of spatial pattern information by complex cells in the primary visua...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">We have studied the encoding of spatial pattern information by complex cells in the primary visual cortex of awake monkeys. Three models for the conditional probabilities of different stimuli, given the neuronal response, were fit and compared using cross-validation. For our data, a feed-forward neural network proved to be the best of these models. The information carried by a cell about a stimulus set can be calculated from the estimated conditional probabilities. We performed a spatial spectroscopy of the encoding, examining how the transmitted information varies with both the average coarseness of the stimulus set and the coarseness differences within it. We find that each neuron encodes information about many features at multiple scales. 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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="18825667"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/18825667/Measuring_Natural_Neural_Processing_with_Artificial_Neural_Networks"><img alt="Research paper thumbnail of Measuring Natural Neural Processing with Artificial Neural Networks" 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" rel="nofollow" href="https://www.academia.edu/18825667/Measuring_Natural_Neural_Processing_with_Artificial_Neural_Networks">Measuring Natural Neural Processing with Artificial Neural Networks</a></div><div class="wp-workCard_item wp-workCard--coauthors"><span>by </span><span><a class="" data-click-track="profile-work-strip-authors" href="https://independent.academia.edu/EmadEskandar">Emad Eskandar</a> and <a class="" data-click-track="profile-work-strip-authors" href="https://independent.academia.edu/BarryRichmond3">Barry Richmond</a></span></div><div class="wp-workCard_item"><span>International Journal of Neural Systems</span><span>, 1992</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">ABSTRACT We show how to use artificial neural networks as a quantitative tool in studying real ne...</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">ABSTRACT We show how to use artificial neural networks as a quantitative tool in studying real neuronal processing in the monkey visual system. Training a network to classify neuronal signals according to the stimulus that elicited them permits us to calculate the information transmitted by these signals. We illustrate this for neurons in the primary visual cortex with measurements of the information transmitted about visual stimuli and for cells in inferior temporal cortex with measurements of information about behavioral context. 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When monkeys performed a task requiring one to three bar release trials to be completed successfully before a reward was given, they seemed more motivated as the rewarded trials approached; they responded more quickly and accurately. When the mon-keys were cued as to</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="20831274"><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="20831274"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 20831274; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=20831274]").text(description); $(".js-view-count[data-work-id=20831274]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 20831274; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='20831274']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 20831274, 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" } } $('.js-work-strip[data-work-id=20831274]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":20831274,"title":"Neuronal Signals in the Monkey Ventral Striatum Related to Progress through a Predictable Series of","translated_title":"","metadata":{"abstract":"Single neurons in the ventral striatum of primates carry signals that are related to reward and motivation. 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J. Neurophysiol. ...</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">key TE and perirhinal cortex: stimulus association related to reward schedules. J. Neurophysiol. 83: 1677-1692, 2000. Anatomic and behavioral evidence shows that TE and perirhinal cortices are two directly connected but distinct inferior temporal areas. Despite this distinctness, physiological properties of neurons in these two areas generally have been similar with neurons in both areas showing selectivity for complex visual</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="9b074880667f2bcfdc2961fa2e9cd971" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":41920795,"asset_id":20831273,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/41920795/download_file?st=MTczMjUyNzMzOSw4LjIyMi4yMDguMTQ2&st=MTczMjUyNzMzOSw4LjIyMi4yMDguMTQ2&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="20831273"><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="20831273"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 20831273; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=20831273]").text(description); $(".js-view-count[data-work-id=20831273]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 20831273; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='20831273']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 20831273, 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: "9b074880667f2bcfdc2961fa2e9cd971" } } $('.js-work-strip[data-work-id=20831273]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":20831273,"title":"Response Differences in Monkey TE and Perirhinal Cortex: Stimulus Association Related to Reward Schedules","translated_title":"","metadata":{"abstract":"key TE and perirhinal cortex: stimulus association related to reward schedules. 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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="20831272"><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/20831272/Neuronal_Signals_in_the_Monkey_Ventral_Striatum_Related_to_Progress_through_a_Predictable_Series_of_Trials"><img alt="Research paper thumbnail of Neuronal Signals in the Monkey Ventral Striatum Related to Progress through a Predictable Series of Trials" 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/20831272/Neuronal_Signals_in_the_Monkey_Ventral_Striatum_Related_to_Progress_through_a_Predictable_Series_of_Trials">Neuronal Signals in the Monkey Ventral Striatum Related to Progress through a Predictable Series of Trials</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Single neurons in the ventral striatum of primates carry signals that are related to reward and m...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Single neurons in the ventral striatum of primates carry signals that are related to reward and motivation. When monkeys performed a task requiring one to three bar release trials to be completed successfully before a reward was given, they seemed more motivated as the rewarded trials approached; they responded more quickly and accurately. 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We...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">We would like to know whether the statistics of neuronal responses vary across cortical areas. We examined stimulus-elicited spike count response distributions in V1 and IT cortices of awake monkeys. In both areas the distribution of spike counts for each stimulus was well-described by a Gaussian, with the log of the variance in the spike count linearly related to the</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="653637a43f2ad230f72d35cdc07ab729" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":41586620,"asset_id":20831271,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/41586620/download_file?st=MTczMjUyNzMzOSw4LjIyMi4yMDguMTQ2&st=MTczMjUyNzMzOSw4LjIyMi4yMDguMTQ2&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="20831271"><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="20831271"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 20831271; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=20831271]").text(description); $(".js-view-count[data-work-id=20831271]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 20831271; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='20831271']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 20831271, 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: "653637a43f2ad230f72d35cdc07ab729" } } $('.js-work-strip[data-work-id=20831271]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":20831271,"title":"Coding Strategies in Monkey V1 and Inferior Temporal Cortices","translated_title":"","metadata":{"abstract":"We would like to know whether the statistics of neuronal responses vary across cortical areas. 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data-click-track="profile-work-strip-title" href="https://www.academia.edu/20831270/Anomalous_response_variability_in_a_balanced_cortical_network_model">Anomalous response variability in a balanced cortical network model</a></div><div class="wp-workCard_item"><span>Neurocomputing</span><span>, 2003</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="d9bb3af5a6c164b0cd83d0a9c52b3771" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":41586656,"asset_id":20831270,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/41586656/download_file?st=MTczMjUyNzMzOSw4LjIyMi4yMDguMTQ2&st=MTczMjUyNzMzOSw4LjIyMi4yMDguMTQ2&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 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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="20831269"><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/20831269/Decoding_spike_trains_instant_by_instant_using_order_statistics_and_the_mixture_of_Poissons_model"><img alt="Research paper thumbnail of Decoding spike trains instant by instant using order statistics and the mixture-of-Poissons model" class="work-thumbnail" src="https://attachments.academia-assets.com/41586690/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/20831269/Decoding_spike_trains_instant_by_instant_using_order_statistics_and_the_mixture_of_Poissons_model">Decoding spike trains instant by instant using order statistics and the mixture-of-Poissons model</a></div><div class="wp-workCard_item"><span>The Journal of neuroscience : the official journal of the Society for Neuroscience</span><span>, Jan 15, 2003</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">In the brain, spike trains are generated in time and presumably also interpreted as they unfold i...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">In the brain, spike trains are generated in time and presumably also interpreted as they unfold in time. Recent work (Oram et al., 1999; Baker and Lemon, 2000) suggests that in several areas of the monkey brain, individual spike times carry information because they reflect an underlying rate variation. Constructing a model based on this stochastic structure allows us to apply order statistics to decode spike trains instant by instant as spikes arrive or do not. Order statistics are time-consuming to compute in the general case. We demonstrate that data from neurons in primary visual cortex are well fit by a mixture of Poisson processes; in this special case, our computations are substantially faster. In these data, spike timing contributed information beyond that available from the spike count throughout the trial. At the end of the trial, a decoder based on the mixture-of-Poissons model correctly decoded about three times as many trials as expected by chance, compared with approxim...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="0be4b60bbb82970cdad33b3b7974453d" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":41586690,"asset_id":20831269,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/41586690/download_file?st=MTczMjUyNzMzOSw4LjIyMi4yMDguMTQ2&st=MTczMjUyNzMzOSw4LjIyMi4yMDguMTQ2&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="20831269"><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="20831269"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 20831269; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=20831269]").text(description); $(".js-view-count[data-work-id=20831269]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 20831269; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='20831269']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 20831269, 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: "0be4b60bbb82970cdad33b3b7974453d" } } $('.js-work-strip[data-work-id=20831269]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":20831269,"title":"Decoding spike trains instant by instant using order statistics and the mixture-of-Poissons model","translated_title":"","metadata":{"abstract":"In the brain, spike trains are generated in time and presumably also interpreted as they unfold in time. 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significance of visual cues for reward schedules requires rhinal cortex" 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/20831267/Learning_motivational_significance_of_visual_cues_for_reward_schedules_requires_rhinal_cortex">Learning motivational significance of visual cues for reward schedules requires rhinal cortex</a></div><div class="wp-workCard_item"><span>Nature Neuroscience</span><span>, 2000</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">The limbic system is necessary to associate stimuli with their motivational and emotional signifi...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" 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Bilateral rhinal (perirhinal and entorhinal) cortex ablations irreversibly prevent this learning. Here, we apply a recombinant DNA technique to investigate the role of dopamine D2 receptor in rhinal cortex for this type of learning. Rhinal cortex was injected with a DNA construct that significantly decreased D2 receptor ligand binding and temporarily produced the same profound learning deficit seen after ablation. However, unlike after ablation, the D2 receptor-targeted, DNA-treated monkeys recovered cue-related learning after 11-19 weeks. Injecting a DNA construct that decreased N-methyl-d-aspartate but not D2 receptor ligand binding did not interfere with learning associations between the cues and the schedules. A second D2 receptor-targeted DNA treatment administered after either recovery from a first D2 receptor-targeted DNA treatment (one monkey), after N-methyl-d-aspartate receptor-targeted DNA treatment (two monkeys), or after a vector control treatment (one monkey) also induced a learning deficit of similar duration. These results suggest that the D2 receptor in primate rhinal cortex is essential for learning to relate the visual cues to the schedules. The specificity of the receptor manipulation reported here suggests that this approach could be generalized in this or other brain pathways to relate molecular mechanisms to cognitive functions.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="1a705a2e14d745984f62007a8f623436" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":41920896,"asset_id":20831263,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/41920896/download_file?st=MTczMjUyNzMzOSw4LjIyMi4yMDguMTQ2&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="20831263"><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="20831263"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 20831263; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=20831263]").text(description); $(".js-view-count[data-work-id=20831263]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 20831263; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='20831263']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 20831263, 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: "1a705a2e14d745984f62007a8f623436" } } $('.js-work-strip[data-work-id=20831263]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":20831263,"title":"DNA targeting of rhinal cortex D2 receptor protein reversibly blocks learning of cues that predict reward","translated_title":"","metadata":{"abstract":"When schedules of several operant trials must be successfully completed to obtain a reward, monkeys quickly learn to adjust their behavioral performance by using visual cues that signal how many trials have been completed and how many remain in the current schedule. 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class="js-work-more-abstract-truncated">... 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Recently two improved procedures have been developed to calculate information from experimental results: a binning-and-correcting procedure and a neural network procedure. We have used data produced from a model of the spatiotemporal receptive fields of parvocellular and magnocellular lateral geniculate neurons to study the performance of these methods as a function of the number of trials used. Both procedures yield accurate results for one-dimensional neuronal codes. They can also be used to produce a reasonable estimate of the extra information in a three-dimensional code, in this instance, within 0.05-0.1 bit of the asymptotically calculated value-about 10% of the total transmitted information. 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The different models often seem quite similar, but because they are cast in different formalisms, it is often difficult to compare their predictions. Here we use the information-geometric measure, an orthogonal coordinate representation of point processes, to express different models of stochastic point processes in a common coordinate system. Within such a framework, it becomes straightforward to visualize higher-order correlations of different models and thereby assess the differences between models. We apply the information-geometric measure to compare two similar but not identical models of neuronal spike trains: the inhomogeneous Markov and the mixture of Poisson models. It is shown that they differ in the second-and higher-order interaction terms. In the mixture of Poisson model, the second-and higher-order interactions are of comparable magnitude within each order, whereas in the inhomogeneous Markov model, they have alternating signs over different orders. This provides guidance about what measurements would effectively separate the two models. As newer models are proposed, they also can be compared to these models using information geometry.","publication_date":{"day":null,"month":null,"year":2006,"errors":{}},"publication_name":"Neural Computation","grobid_abstract_attachment_id":41586667},"translated_abstract":null,"internal_url":"https://www.academia.edu/20831261/A_Comparison_of_Descriptive_Models_of_a_Single_Spike_Train_by_Information_Geometric_Measure","translated_internal_url":"","created_at":"2016-01-26T05:46:06.170-08:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":42086941,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[{"id":13795217,"work_id":20831261,"tagging_user_id":42086941,"tagged_user_id":null,"co_author_invite_id":2107164,"email":"h***n@brain.riken.jp","display_order":0,"name":"Hiroyuki Nakahara","title":"A Comparison of Descriptive Models of a Single Spike Train by Information-Geometric 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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="20831259"><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/20831259/Decoding_cortical_neuronal_signals_Network_models_information_estimation_and_spatial_tuning"><img alt="Research paper thumbnail of Decoding cortical neuronal signals: Network models, information estimation and spatial tuning" 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/20831259/Decoding_cortical_neuronal_signals_Network_models_information_estimation_and_spatial_tuning">Decoding cortical neuronal signals: Network models, information estimation and spatial tuning</a></div><div class="wp-workCard_item"><span>Journal of Computational Neuroscience</span><span>, 1994</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">We have studied the encoding of spatial pattern information by complex cells in the primary visua...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">We have studied the encoding of spatial pattern information by complex cells in the primary visual cortex of awake monkeys. Three models for the conditional probabilities of different stimuli, given the neuronal response, were fit and compared using cross-validation. For our data, a feed-forward neural network proved to be the best of these models. The information carried by a cell about a stimulus set can be calculated from the estimated conditional probabilities. We performed a spatial spectroscopy of the encoding, examining how the transmitted information varies with both the average coarseness of the stimulus set and the coarseness differences within it. We find that each neuron encodes information about many features at multiple scales. Our data do not appear to allow a characterization of these variations in terms of the detection of simple single features such as oriented bars.</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="20831259"><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="20831259"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 20831259; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=20831259]").text(description); $(".js-view-count[data-work-id=20831259]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 20831259; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='20831259']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 20831259, 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" } } $('.js-work-strip[data-work-id=20831259]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":20831259,"title":"Decoding cortical neuronal signals: Network models, information estimation and spatial tuning","translated_title":"","metadata":{"abstract":"We have studied the encoding of spatial pattern information by complex cells in the primary visual cortex of awake monkeys. 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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="20831258"><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/20831258/Information_flow_and_temporal_coding_in_primate_pattern_vision"><img alt="Research paper thumbnail of Information flow and temporal coding in primate pattern vision" class="work-thumbnail" src="https://attachments.academia-assets.com/41586661/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/20831258/Information_flow_and_temporal_coding_in_primate_pattern_vision">Information flow and temporal coding in primate pattern vision</a></div><div class="wp-workCard_item"><span>Journal of Computational Neuroscience</span><span>, 1995</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="2454eec1bc0ce9642ee7eddbda2d9a6f" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":41586661,"asset_id":20831258,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/41586661/download_file?st=MTczMjUyNzMzOSw4LjIyMi4yMDguMTQ2&st=MTczMjUyNzMzOSw4LjIyMi4yMDguMTQ2&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="20831258"><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="20831258"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 20831258; 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All measurable information is carried in an e ective timevarying ring rate, obtained by averaging the neuronal response with a resolution no ner than about 25 ms in primary visual cortex and around twice that in inferior temporal cortex. We found no better way for a neuron receiving these messages to decode them than simply to count spikes for this long. Most of the information tends to be concentrated in one or, more often, two brief packets, one at the very beginning of the response and the other typically 100 ms later. The rst packet is the most informative part of the message, but the second one generally contains new information. A small but signi cant part of the total information in the message accumulates gradually over the entire course of the response. 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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="18825667"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/18825667/Measuring_Natural_Neural_Processing_with_Artificial_Neural_Networks"><img alt="Research paper thumbnail of Measuring Natural Neural Processing with Artificial Neural Networks" 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" rel="nofollow" href="https://www.academia.edu/18825667/Measuring_Natural_Neural_Processing_with_Artificial_Neural_Networks">Measuring Natural Neural Processing with Artificial Neural Networks</a></div><div class="wp-workCard_item wp-workCard--coauthors"><span>by </span><span><a class="" data-click-track="profile-work-strip-authors" href="https://independent.academia.edu/EmadEskandar">Emad Eskandar</a> and <a class="" data-click-track="profile-work-strip-authors" href="https://independent.academia.edu/BarryRichmond3">Barry Richmond</a></span></div><div class="wp-workCard_item"><span>International Journal of Neural Systems</span><span>, 1992</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">ABSTRACT We show how to use artificial neural networks as a quantitative tool in studying real ne...</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">ABSTRACT We show how to use artificial neural networks as a quantitative tool in studying real neuronal processing in the monkey visual system. Training a network to classify neuronal signals according to the stimulus that elicited them permits us to calculate the information transmitted by these signals. We illustrate this for neurons in the primary visual cortex with measurements of the information transmitted about visual stimuli and for cells in inferior temporal cortex with measurements of information about behavioral context. For the latter neurons we also illustrate how artificial neural networks can be used to model the computation they do.</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="18825667"><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="18825667"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 18825667; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=18825667]").text(description); $(".js-view-count[data-work-id=18825667]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 18825667; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='18825667']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 18825667, 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" } } $('.js-work-strip[data-work-id=18825667]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":18825667,"title":"Measuring Natural Neural Processing with Artificial Neural Networks","translated_title":"","metadata":{"abstract":"ABSTRACT We show how to use artificial neural networks as a quantitative tool in studying real neuronal processing in the monkey visual system. 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