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id="social-redesign-work-container"><div class="upload-header"><h2 class="ds2-5-heading-sans-serif-xs">Uploads</h2></div><div class="documents-container backbone-social-profile-documents" style="width: 100%;"><div class="u-taCenter"></div><div class="profile--tab_content_container js-tab-pane tab-pane active" id="all"><div class="profile--tab_heading_container js-section-heading" data-section="Papers" id="Papers"><h3 class="profile--tab_heading_container">Papers by Jan Antolik</h3></div><div class="js-work-strip profile--work_container" data-work-id="102927319"><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/102927319/The_cortico_thalamic_loop_attunes_competitive_lateral_interactions_across_retinotopic_and_orientation_preference_maps"><img alt="Research paper thumbnail of The cortico-thalamic loop attunes competitive lateral interactions across retinotopic and orientation preference maps" class="work-thumbnail" src="https://attachments.academia-assets.com/103069061/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/102927319/The_cortico_thalamic_loop_attunes_competitive_lateral_interactions_across_retinotopic_and_orientation_preference_maps">The cortico-thalamic loop attunes competitive lateral interactions across retinotopic and orientation preference maps</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">In the early visual system, corticothalamic feedback projections greatly outnumber thalamocortica...</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 early visual system, corticothalamic feedback projections greatly outnumber thalamocortical feedforward projections. Extensive experimental and modeling work has been devoted to the functional impact of the feedforward pathway, but the role of its denser feedback counterpart remains elusive. Here, we propose a novel unifying framework where thalamic recurrent interactions and corticothalamic feedback act in a closed-loop fashion to attune multiple stimulus representations. At each position of the visual field, the loop puts into competition local representations of the stimulus in thalamus and cortex through direct excitation of narrow topologically-aligned portions of the thalamus, accompanied with peri-geniculate nucleus mediated broad inhibition suppressing the topological surround. We built a detailed conductance-based spiking model incorporating retinal input, lateral geniculate nucleus, peri-geniculate nucleus, primary visual cortex, and all the relevant intra-areal and...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="8e6f82f04cd0da65b622d0405cd074a3" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":103069061,"asset_id":102927319,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/103069061/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="102927319"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="102927319"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 102927319; 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="102927318"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/102927318/Developing_maps_of_complex_cells_in_a_computational_model_of_V1"><img alt="Research paper thumbnail of Developing maps of complex cells in a computational model of V1" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" rel="nofollow" href="https://www.academia.edu/102927318/Developing_maps_of_complex_cells_in_a_computational_model_of_V1">Developing maps of complex cells in a computational model of V1</a></div><div class="wp-workCard_item"><span>Society for Neuroscience Annual meeting, 2008</span><span>, Nov 19, 2008</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="102927318"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="102927318"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 102927318; 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dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=102927316]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":102927316,"title":"Developing maps of complex cells in a computational model","internal_url":"https://www.academia.edu/102927316/Developing_maps_of_complex_cells_in_a_computational_model","owner_id":58717480,"coauthors_can_edit":true,"owner":{"id":58717480,"first_name":"Jan","middle_initials":null,"last_name":"Antolik","page_name":"JanAntolik","domain_name":"independent","created_at":"2017-01-10T01:02:44.948-08:00","display_name":"Jan Antolik","url":"https://independent.academia.edu/JanAntolik"},"attachments":[]}, 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="102927315"><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/102927315/Assessment_of_optogenetically_driven_strategies_for_prosthetic_restoration_of_cortical_vision_in_large_scale_neural_simulation_of_V1"><img alt="Research paper thumbnail of Assessment of optogenetically-driven strategies for prosthetic restoration of cortical vision in large-scale neural simulation of V1" class="work-thumbnail" src="https://attachments.academia-assets.com/103068989/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/102927315/Assessment_of_optogenetically_driven_strategies_for_prosthetic_restoration_of_cortical_vision_in_large_scale_neural_simulation_of_V1">Assessment of optogenetically-driven strategies for prosthetic restoration of cortical vision in large-scale neural simulation of V1</a></div><div class="wp-workCard_item"><span>Scientific Reports</span><span>, 2021</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">The neural encoding of visual features in primary visual cortex (V1) is well understood, with str...</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 neural encoding of visual features in primary visual cortex (V1) is well understood, with strong correlates to low-level perception, making V1 a strong candidate for vision restoration through neuroprosthetics. However, the functional relevance of neural dynamics evoked through external stimulation directly imposed at the cortical level is poorly understood. Furthermore, protocols for designing cortical stimulation patterns that would induce a naturalistic perception of the encoded stimuli have not yet been established. Here, we demonstrate a proof of concept by solving these issues through a computational model, combining (1) a large-scale spiking neural network model of cat V1 and (2) a virtual prosthetic system transcoding the visual input into tailored light-stimulation patterns which drive in situ the optogenetically modified cortical tissue. Using such virtual experiments, we design a protocol for translating simple Fourier contrasted stimuli (gratings) into activation pat...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="6954b239ba409a0011d32734e754b163" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":103068989,"asset_id":102927315,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/103068989/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="102927315"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="102927315"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 102927315; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=102927315]").text(description); $(".js-view-count[data-work-id=102927315]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 102927315; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='102927315']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); 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$(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="102927314"><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/102927314/An_Anatomically_Constrained_Model_of_V1_Simple_Cells_Predicts_the_Coexistence_of_Push_Pull_and_Broad_Inhibition"><img alt="Research paper thumbnail of An Anatomically Constrained Model of V1 Simple Cells Predicts the Coexistence of Push–Pull and Broad Inhibition" class="work-thumbnail" src="https://attachments.academia-assets.com/103069041/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/102927314/An_Anatomically_Constrained_Model_of_V1_Simple_Cells_Predicts_the_Coexistence_of_Push_Pull_and_Broad_Inhibition">An Anatomically Constrained Model of V1 Simple Cells Predicts the Coexistence of Push–Pull and Broad Inhibition</a></div><div class="wp-workCard_item"><span>The Journal of Neuroscience</span><span>, 2021</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">The spatial organization and dynamic interactions between excitatory and inhibitory synaptic inpu...</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 spatial organization and dynamic interactions between excitatory and inhibitory synaptic inputs that define the receptive field (RF) of simple cells in the cat primary visual cortex (V1) still raise the following paradoxical issues: (1) stimulation of simple cells in V1 with drifting gratings supports a wiring schema of spatially segregated sets of excitatory and inhibitory inputs activated in an opponent way by stimulus contrast polarity and (2) in contrast, intracellular studies using flashed bars suggest that although ON and OFF excitatory inputs are indeed segregated, inhibitory inputs span the entire RF regardless of input contrast polarity. Here, we propose a biologically detailed computational model of simple cells embedded in a V1-like network that resolves this seeming contradiction. We varied parametrically the RF-correlation-based bias for excitatory and inhibitory synapses and found that a moderate bias of excitatory neurons to synapse onto other neurons with correlated receptive fields and a weaker bias of inhibitory neurons to synapse onto other neurons with anticorrelated receptive fields can explain the conductance input, the postsynaptic membrane potential, and the spike train dynamics under both stimulation paradigms. This computational study shows that the same structural model can reproduce the functional diversity of visual processing observed during different visual contexts.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="69ada2114468d555cd6f86ebed1da308" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":103069041,"asset_id":102927314,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/103069041/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="102927314"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="102927314"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 102927314; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=102927314]").text(description); $(".js-view-count[data-work-id=102927314]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 102927314; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='102927314']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "69ada2114468d555cd6f86ebed1da308" } } $('.js-work-strip[data-work-id=102927314]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":102927314,"title":"An Anatomically Constrained Model of V1 Simple Cells Predicts the Coexistence of Push–Pull and Broad Inhibition","internal_url":"https://www.academia.edu/102927314/An_Anatomically_Constrained_Model_of_V1_Simple_Cells_Predicts_the_Coexistence_of_Push_Pull_and_Broad_Inhibition","owner_id":58717480,"coauthors_can_edit":true,"owner":{"id":58717480,"first_name":"Jan","middle_initials":null,"last_name":"Antolik","page_name":"JanAntolik","domain_name":"independent","created_at":"2017-01-10T01:02:44.948-08:00","display_name":"Jan Antolik","url":"https://independent.academia.edu/JanAntolik"},"attachments":[{"id":103069041,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/103069041/thumbnails/1.jpg","file_name":"Broad2021.pdf","download_url":"https://www.academia.edu/attachments/103069041/download_file","bulk_download_file_name":"An_Anatomically_Constrained_Model_of_V1.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/103069041/Broad2021-libre.pdf?1686036719=\u0026response-content-disposition=attachment%3B+filename%3DAn_Anatomically_Constrained_Model_of_V1.pdf\u0026Expires=1740608526\u0026Signature=U3YEvSEePmX7ZFrArq~0kWHCjwmPfbEGnHzykF5sijJvXnFtAwsA2duZWnIWZzTN2ZQqCUFo2J8K60emPcqGokfKa8U0JBroBVeZZU6Jsz1HyahpNBsPhDEyYay2P9rptvT0I3lZIo9ka0TKnDjHiwoQVA6DDskTvpfrSQ~zNa3th9yg5QKz2NTibclm2WOh0fEX2g53v5djJSZLbhf8fTW7Tp8Eat2iSQThkC5e70BeLHB0oE0Vmj7bt9y73mAVUNpLwDcyoMbu4~tvJw~ymygvhUUAb4FYb6j~QRLXTafj1QzS7hbhEIZIiTceo7FBUucQpO9Bu3pdo2cUmFH17A__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}]}, 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="102927313"><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/102927313/Arkheia_Data_Management_and_Communication_for_Open_Computational_Neuroscience"><img alt="Research paper thumbnail of Arkheia: Data Management and Communication for Open Computational Neuroscience" class="work-thumbnail" src="https://attachments.academia-assets.com/103069052/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/102927313/Arkheia_Data_Management_and_Communication_for_Open_Computational_Neuroscience">Arkheia: Data Management and Communication for Open Computational Neuroscience</a></div><div class="wp-workCard_item"><span>Frontiers in Neuroinformatics</span><span>, 2018</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="f22323f563ab4e3c6d9a795430704ac4" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":103069052,"asset_id":102927313,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/103069052/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="102927313"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="102927313"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 102927313; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=102927313]").text(description); $(".js-view-count[data-work-id=102927313]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 102927313; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='102927313']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "f22323f563ab4e3c6d9a795430704ac4" } } $('.js-work-strip[data-work-id=102927313]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":102927313,"title":"Arkheia: Data Management and Communication for Open Computational Neuroscience","internal_url":"https://www.academia.edu/102927313/Arkheia_Data_Management_and_Communication_for_Open_Computational_Neuroscience","owner_id":58717480,"coauthors_can_edit":true,"owner":{"id":58717480,"first_name":"Jan","middle_initials":null,"last_name":"Antolik","page_name":"JanAntolik","domain_name":"independent","created_at":"2017-01-10T01:02:44.948-08:00","display_name":"Jan Antolik","url":"https://independent.academia.edu/JanAntolik"},"attachments":[{"id":103069052,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/103069052/thumbnails/1.jpg","file_name":"fninf-12-00006.pdf","download_url":"https://www.academia.edu/attachments/103069052/download_file","bulk_download_file_name":"Arkheia_Data_Management_and_Communicatio.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/103069052/fninf-12-00006-libre.pdf?1686036705=\u0026response-content-disposition=attachment%3B+filename%3DArkheia_Data_Management_and_Communicatio.pdf\u0026Expires=1740608526\u0026Signature=UWVMjsC5eFcLHG3H-l1-7o6px0NcEmCfpEVd-0Q5oYNPaSCfmyaYDDUeay0UFnKLADJ0BK1FsD13YDGuGDysB0rD8MZ6zEJH1mcVlzT6fsg9ycvurKJT6yevJX1vFz~~8qp9UCnswzrMLu9yWiy-eC~y5KzYm627rJhFmVHhN0PuTed3miuEHtEOJU0O5IQcJTegJ78k3dkYLABm9XAlI7z3rZzTcIm9TWDuXLf4ZKuS7iged2reuUvmyD06CPLHtFsm1ugD10Ig1Jm5JTPxishFOOSqkPGKA778aTVcnJOxNWI2p66Z2sAWt1zp5qfe3zH9Ldld3y37-od7s0E3hQ__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}]}, 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="102927312"><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/102927312/Cortical_visual_prosthesis_a_detailed_large_scale_simulation_study"><img alt="Research paper thumbnail of Cortical visual prosthesis: a detailed large-scale simulation study" class="work-thumbnail" src="https://attachments.academia-assets.com/103069040/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/102927312/Cortical_visual_prosthesis_a_detailed_large_scale_simulation_study">Cortical visual prosthesis: a detailed large-scale simulation study</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Recent advances in applying optogenetics in primates initiated the development of light based pro...</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">Recent advances in applying optogenetics in primates initiated the development of light based prosthetic implants for sensory restoration. Thanks to being the most well explored cortical area that is readily accessible at the surface of the brain, vision restoration via direct optogenetic activation of primary visual cortex is one of the most promising early targets for a optogenetics based prosthetic program. However, two fundamental elements of the cortical optogenetic prosthesis remain unclear. First, the exact mechanisms of neural dynamics under direct cortical stimulation, especially in the context of living, active and functionally specific intra-cortical neural circuitry, is poorly understood. Second, we lack protocols for transformation of arbitrary visual stimuli into light activation patterns that would induce perception of the said stimulus by the subject. In this study we address these issues using a large-scale spiking neural network modeling strategy of high biological...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="9d7ae930474c41c6be0bd909e3c09b75" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":103069040,"asset_id":102927312,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/103069040/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="102927312"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="102927312"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 102927312; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=102927312]").text(description); $(".js-view-count[data-work-id=102927312]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 102927312; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='102927312']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); 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$(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="102927311"><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/102927311/A_comprehensive_data_driven_model_of_cat_primary_visual_cortex"><img alt="Research paper thumbnail of A comprehensive data-driven model of cat primary visual cortex" class="work-thumbnail" src="https://attachments.academia-assets.com/103069042/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/102927311/A_comprehensive_data_driven_model_of_cat_primary_visual_cortex">A comprehensive data-driven model of cat primary visual cortex</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Knowledge integration based on the relationship between structure and function of the neural subs...</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">Knowledge integration based on the relationship between structure and function of the neural substrate is one of the main targets of neuroinformatics and data-driven computational modeling. However, the multiplicity of data sources, the diversity of benchmarks, the mixing of observables of different natures, and the necessity of a long-term, systematic approach make such a task challenging. Here we present a first snapshot of a long-term integrative modeling program designed to address this issue: a comprehensive spiking model of cat primary visual cortex satisfying an unprecedented range of anatomical, statistical and functional constraints under a wide range of visual input statistics. In the presence of physiological levels of tonic stochastic bombardment by spontaneous thalamic activity, the modeled cortical reverberations self-generate a sparse asynchronous ongoing activity that quantitatively matches a range of experimentally measured statistics. When integrating feed-forward ...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="82df6256e55e0c739a53f0b546a0319e" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":103069042,"asset_id":102927311,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/103069042/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="102927311"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="102927311"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 102927311; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=102927311]").text(description); $(".js-view-count[data-work-id=102927311]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 102927311; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='102927311']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "82df6256e55e0c739a53f0b546a0319e" } } $('.js-work-strip[data-work-id=102927311]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":102927311,"title":"A comprehensive data-driven model of cat primary visual cortex","internal_url":"https://www.academia.edu/102927311/A_comprehensive_data_driven_model_of_cat_primary_visual_cortex","owner_id":58717480,"coauthors_can_edit":true,"owner":{"id":58717480,"first_name":"Jan","middle_initials":null,"last_name":"Antolik","page_name":"JanAntolik","domain_name":"independent","created_at":"2017-01-10T01:02:44.948-08:00","display_name":"Jan Antolik","url":"https://independent.academia.edu/JanAntolik"},"attachments":[{"id":103069042,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/103069042/thumbnails/1.jpg","file_name":"416156.full.pdf","download_url":"https://www.academia.edu/attachments/103069042/download_file","bulk_download_file_name":"A_comprehensive_data_driven_model_of_cat.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/103069042/416156.full-libre.pdf?1686036746=\u0026response-content-disposition=attachment%3B+filename%3DA_comprehensive_data_driven_model_of_cat.pdf\u0026Expires=1740608527\u0026Signature=M-kXTGZeZEjUqwgBJYFbCzEWHUNEPhWjdGPfNs0fA6d9bq4x614sXJ8r2bMJ1FIonfKF--kpr4cZqLzbsi5bWmqDXcoK4Ib17-Rxducht4dgHst5n2eZaqsQo5JYUj1pq9LyA0eP-RSYuwmyNBPjUmvyV~dUnMj7LrXKUGkm7Z8t~y5NjMcXu6mZ8GIbSep7-KgJH7TAE-oUCQT~eaohTLFWvSYcocdvEXbSS5Ge3LJTGD~9F1MF46-vDQmetBJMdygKTRF3mDNMzxKNDLN53FJWi5fcOnxejyVJiTY~Z6GlloYhfKCBqi1iNbq74alycnNTzLL9I3U4UbnVx2cOMg__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}]}, 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="102927310"><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/102927310/Rapid_Long_Range_Disynaptic_Inhibition_Explains_the_Formation_of_Cortical_Orientation_Maps"><img alt="Research paper thumbnail of Rapid Long-Range Disynaptic Inhibition Explains the Formation of Cortical Orientation Maps" class="work-thumbnail" src="https://attachments.academia-assets.com/103069035/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/102927310/Rapid_Long_Range_Disynaptic_Inhibition_Explains_the_Formation_of_Cortical_Orientation_Maps">Rapid Long-Range Disynaptic Inhibition Explains the Formation of Cortical Orientation Maps</a></div><div class="wp-workCard_item"><span>Frontiers in Neural Circuits</span><span>, 2017</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Competitive interactions are believed to underlie many types of cortical processing, ranging from...</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">Competitive interactions are believed to underlie many types of cortical processing, ranging from memory formation, attention and development of cortical functional organization (e.g., development of orientation maps in primary visual cortex). In the latter case, the competitive interactions happen along the cortical surface, with local populations of neurons reinforcing each other, while competing with those displaced more distally. This specific configuration of lateral interactions is however in stark contrast with the known properties of the anatomical substrate, i.e., excitatory connections (mediating reinforcement) having longer reach than inhibitory ones (mediating competition). No satisfactory biologically plausible resolution of this conflict between anatomical measures, and assumed cortical function has been proposed. Recently a specific pattern of delays between different types of neurons in cat cortex has been discovered, where direct mono-synaptic excitation has approximately the same delay, as the combined delays of the disynaptic inhibitory interactions between excitatory neurons (i.e., the sum of delays from excitatory to inhibitory and from inhibitory to excitatory neurons). Here we show that this specific pattern of delays represents a biologically plausible explanation for how short-range inhibition can support competitive interactions that underlie the development of orientation maps in primary visual cortex. We demonstrate this statement analytically under simplifying conditions, and subsequently show using network simulations that development of orientation maps is preserved when long-range excitation, direct inhibitory to inhibitory interactions, and moderate inequality in the delays between excitatory and inhibitory pathways is added.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="44ba747b60e1e315c377df43f23f9a61" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":103069035,"asset_id":102927310,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/103069035/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="102927310"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="102927310"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 102927310; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=102927310]").text(description); $(".js-view-count[data-work-id=102927310]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 102927310; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='102927310']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "44ba747b60e1e315c377df43f23f9a61" } } $('.js-work-strip[data-work-id=102927310]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":102927310,"title":"Rapid Long-Range Disynaptic Inhibition Explains the Formation of Cortical Orientation Maps","internal_url":"https://www.academia.edu/102927310/Rapid_Long_Range_Disynaptic_Inhibition_Explains_the_Formation_of_Cortical_Orientation_Maps","owner_id":58717480,"coauthors_can_edit":true,"owner":{"id":58717480,"first_name":"Jan","middle_initials":null,"last_name":"Antolik","page_name":"JanAntolik","domain_name":"independent","created_at":"2017-01-10T01:02:44.948-08:00","display_name":"Jan Antolik","url":"https://independent.academia.edu/JanAntolik"},"attachments":[{"id":103069035,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/103069035/thumbnails/1.jpg","file_name":"52bf5ebf8cb5db2f87202ca75892f4919a59.pdf","download_url":"https://www.academia.edu/attachments/103069035/download_file","bulk_download_file_name":"Rapid_Long_Range_Disynaptic_Inhibition_E.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/103069035/52bf5ebf8cb5db2f87202ca75892f4919a59-libre.pdf?1686036705=\u0026response-content-disposition=attachment%3B+filename%3DRapid_Long_Range_Disynaptic_Inhibition_E.pdf\u0026Expires=1740608527\u0026Signature=P6DnCPaW84iqysDunz4CdYezpyOtw9eMBwm5ZgV5q-wVsUXjMR~ZNQ416LO21hDJ-cWr6xONU-A1M8cSDmrclmKsC2KbHkplqcmlgGjgrsZ29SojqVrz~xXUkUyulGX3IZVaXp8V734e92UuUf4T-cV-JDur4GLEmBbXEWbhzGdTYTr0zW-qPvv3~as9NmX2YGA2iU~QNs-IF5qLOFUBBuvKnO5BP-BFDdesbnJXecdByfmjr~m8rtR-PP9UeC29p3kbPoor~iL5dg~me5RD1VGyaLSApg4MkjKf0-C6qLDgiUtgx1lvGbYBNtU6bE2T54W1YpUnASnnV4nEYbL83A__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}]}, dispatcherData: dispatcherData }); 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="102927274"><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/102927274/Mechanisms_for_Stable_Robust_and_Adaptive_Development_of_Orientation_Maps_in_the_Primary_Visual_Cortex"><img alt="Research paper thumbnail of Mechanisms for Stable, Robust, and Adaptive Development of Orientation Maps in the Primary Visual Cortex" class="work-thumbnail" src="https://attachments.academia-assets.com/103068991/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/102927274/Mechanisms_for_Stable_Robust_and_Adaptive_Development_of_Orientation_Maps_in_the_Primary_Visual_Cortex">Mechanisms for Stable, Robust, and Adaptive Development of Orientation Maps in the Primary Visual Cortex</a></div><div class="wp-workCard_item"><span>The Journal of Neuroscience</span><span>, 2013</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Development of orientation maps in ferret and cat primary visual cortex (V1) has been shown to be...</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">Development of orientation maps in ferret and cat primary visual cortex (V1) has been shown to be stable, in that the earliest measurable maps are similar in form to the eventual adult map, robust, in that similar maps develop in both dark rearing and in a variety of normal visual environments, and yet adaptive, in that the final map pattern reflects the statistics of the specific visual environment. How can these three properties be reconciled? Using mechanistic models of the development of neural connectivity in V1, we show for the first time that realistic stable, robust, and adaptive map development can be achieved by including two low-level mechanisms originally motivated from single-neuron results. Specifically, contrast-gain control in the retinal ganglion cells and the lateral geniculate nucleus reduces variation in the presynaptic drive due to differences in input patterns, while homeostatic plasticity of V1 neuron excitability reduces the postsynaptic variability in firing...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="c09e066c023efa7e15fbe415c4d69504" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":103068991,"asset_id":102927274,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/103068991/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="102927274"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="102927274"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 102927274; 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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="30887560"><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/30887560/Automatic_annotation_of_medical_records"><img alt="Research paper thumbnail of Automatic annotation of medical records" 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/30887560/Automatic_annotation_of_medical_records">Automatic annotation of medical records</a></div><div class="wp-workCard_item"><span>Studies in health technology and informatics</span><span>, 2005</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">One of the research projects running at the medical informatics department of the Institute of Co...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">One of the research projects running at the medical informatics department of the Institute of Computer Science AS CR explores the problem of medical information representation and development of electronic health record (EHR). With respect to this effort an interesting problem arises: how to transfer knowledge from a medical record written in a free text form into a structured electronic format represented by the EHR. Currently, this task was solved by writing extraction rules (regular expressions) for every element of information that is to be extracted from the medical record. However, such approach is very time consuming and requires supervision of a skilled programmer whenever the target area of medicine is changed. In this article we explore the possibility to mechanize this process by automatically generating the extraction rules from a pre-annotated corpus of medical records. Since we are currently in the phase of data acquisition and preliminary tests we will not present an...</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="30887560"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="30887560"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 30887560; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=30887560]").text(description); 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dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=30887560]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":30887560,"title":"Automatic annotation of medical records","internal_url":"https://www.academia.edu/30887560/Automatic_annotation_of_medical_records","owner_id":58717480,"coauthors_can_edit":true,"owner":{"id":58717480,"first_name":"Jan","middle_initials":null,"last_name":"Antolik","page_name":"JanAntolik","domain_name":"independent","created_at":"2017-01-10T01:02:44.948-08:00","display_name":"Jan Antolik","url":"https://independent.academia.edu/JanAntolik"},"attachments":[]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="30887559"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/30887559/Unified_developmental_model_of_maps_complex_cells_and_surround_modulation_in_the_primary_visual_cortex"><img alt="Research paper thumbnail of Unified developmental model of maps, complex cells and surround modulation in the primary visual cortex" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" rel="nofollow" href="https://www.academia.edu/30887559/Unified_developmental_model_of_maps_complex_cells_and_surround_modulation_in_the_primary_visual_cortex">Unified developmental model of maps, complex cells and surround modulation in the primary visual cortex</a></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="30887559"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="30887559"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 30887559; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=30887559]").text(description); $(".js-view-count[data-work-id=30887559]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 30887559; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='30887559']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=30887559]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":30887559,"title":"Unified developmental model of maps, complex cells and surround modulation in the primary visual cortex","internal_url":"https://www.academia.edu/30887559/Unified_developmental_model_of_maps_complex_cells_and_surround_modulation_in_the_primary_visual_cortex","owner_id":58717480,"coauthors_can_edit":true,"owner":{"id":58717480,"first_name":"Jan","middle_initials":null,"last_name":"Antolik","page_name":"JanAntolik","domain_name":"independent","created_at":"2017-01-10T01:02:44.948-08:00","display_name":"Jan Antolik","url":"https://independent.academia.edu/JanAntolik"},"attachments":[]}, 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="30849537"><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/30849537/Model_Constrained_by_Visual_Hierarchy_Improves_Prediction_of_Neural_Responses_to_Natural_Scenes"><img alt="Research paper thumbnail of Model Constrained by Visual Hierarchy Improves Prediction of Neural Responses to Natural Scenes" 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/30849537/Model_Constrained_by_Visual_Hierarchy_Improves_Prediction_of_Neural_Responses_to_Natural_Scenes">Model Constrained by Visual Hierarchy Improves Prediction of Neural Responses to Natural Scenes</a></div><div class="wp-workCard_item"><span>PLOS Computational Biology</span><span>, 2016</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Accurate estimation of neuronal receptive fields is essential for understanding sensory processin...</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">Accurate estimation of neuronal receptive fields is essential for understanding sensory processing in the early visual system. Yet a full characterization of receptive fields is still incomplete, especially with regard to natural visual stimuli and in complete populations of cortical neurons. While previous work has incorporated known structural properties of the early visual system, such as lateral connectivity, or imposing simple-cell-like receptive field structure, no study has exploited the fact that nearby V1 neurons share common feed-forward input from thalamus and other upstream cortical neurons. We introduce a new method for estimating receptive fields simultaneously for a population of V1 neurons, using a model-based analysis incorporating knowledge of the feed-forward visual hierarchy. We assume that a population of V1 neurons shares a common pool of thalamic inputs, and consists of two layers of simple and complex-like V1 neurons. When fit to recordings of a local population of mouse layer 2/3 V1 neurons, our model offers an accurate description of their response to natural images and significant improvement of prediction power over the current state-of-the-art methods. We show that the responses of a large local population of V1 neurons with locally diverse receptive fields can be described with surprisingly limited number of thalamic inputs, consistent with recent experimental findings. Our structural model not only offers an improved functional characterization of V1 neurons, but also provides a framework for studying the relationship between connectivity and function in visual cortical areas.</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="30849537"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="30849537"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 30849537; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=30849537]").text(description); $(".js-view-count[data-work-id=30849537]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 30849537; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='30849537']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=30849537]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":30849537,"title":"Model Constrained by Visual Hierarchy Improves Prediction of Neural Responses to Natural Scenes","internal_url":"https://www.academia.edu/30849537/Model_Constrained_by_Visual_Hierarchy_Improves_Prediction_of_Neural_Responses_to_Natural_Scenes","owner_id":58717480,"coauthors_can_edit":true,"owner":{"id":58717480,"first_name":"Jan","middle_initials":null,"last_name":"Antolik","page_name":"JanAntolik","domain_name":"independent","created_at":"2017-01-10T01:02:44.948-08:00","display_name":"Jan Antolik","url":"https://independent.academia.edu/JanAntolik"},"attachments":[]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="30849536"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/30849536/Evolutionary_tree_genetic_programming"><img alt="Research paper thumbnail of Evolutionary tree genetic programming" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" rel="nofollow" href="https://www.academia.edu/30849536/Evolutionary_tree_genetic_programming">Evolutionary tree genetic programming</a></div><div class="wp-workCard_item"><span>Proceedings of the 2005 Conference on Genetic and Evolutionary Computation</span><span>, 2005</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="30849536"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="30849536"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 30849536; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=30849536]").text(description); $(".js-view-count[data-work-id=30849536]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 30849536; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='30849536']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); 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window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=30849535]").text(description); $(".js-view-count[data-work-id=30849535]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 30849535; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='30849535']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=30849535]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":30849535,"title":"Evolutionary tree genetic programming","internal_url":"https://www.academia.edu/30849535/Evolutionary_tree_genetic_programming","owner_id":58717480,"coauthors_can_edit":true,"owner":{"id":58717480,"first_name":"Jan","middle_initials":null,"last_name":"Antolik","page_name":"JanAntolik","domain_name":"independent","created_at":"2017-01-10T01:02:44.948-08:00","display_name":"Jan Antolik","url":"https://independent.academia.edu/JanAntolik"},"attachments":[]}, 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="30849534"><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/30849534/Integrated_workflows_for_spiking_neuronal_network_simulations"><img alt="Research paper thumbnail of Integrated workflows for spiking neuronal network simulations" class="work-thumbnail" src="https://attachments.academia-assets.com/51277542/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/30849534/Integrated_workflows_for_spiking_neuronal_network_simulations">Integrated workflows for spiking neuronal network simulations</a></div><div class="wp-workCard_item"><span>Frontiers in neuroinformatics</span><span>, 2013</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">The increasing availability of computational resources is enabling more detailed, realistic model...</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 increasing availability of computational resources is enabling more detailed, realistic modeling in computational neuroscience, resulting in a shift toward more heterogeneous models of neuronal circuits, and employment of complex experimental protocols. This poses a challenge for existing tool chains, as the set of tools involved in a typical modeler&#39;s workflow is expanding concomitantly, with growing complexity in the metadata flowing between them. For many parts of the workflow, a range of tools is available; however, numerous areas lack dedicated tools, while integration of existing tools is limited. This forces modelers to either handle the workflow manually, leading to errors, or to write substantial amounts of code to automate parts of the workflow, in both cases reducing their productivity. To address these issues, we have developed Mozaik: a workflow system for spiking neuronal network simulations written in Python. Mozaik integrates model, experiment and stimulation...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="c8edbec658b5df075c0a411111da8fd0" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":51277542,"asset_id":30849534,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/51277542/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="30849534"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="30849534"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 30849534; 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> </div><div class="profile--tab_content_container js-tab-pane tab-pane" data-section-id="6380578" id="papers"><div class="js-work-strip profile--work_container" data-work-id="102927319"><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/102927319/The_cortico_thalamic_loop_attunes_competitive_lateral_interactions_across_retinotopic_and_orientation_preference_maps"><img alt="Research paper thumbnail of The cortico-thalamic loop attunes competitive lateral interactions across retinotopic and orientation preference maps" class="work-thumbnail" src="https://attachments.academia-assets.com/103069061/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/102927319/The_cortico_thalamic_loop_attunes_competitive_lateral_interactions_across_retinotopic_and_orientation_preference_maps">The cortico-thalamic loop attunes competitive lateral interactions across retinotopic and orientation preference maps</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">In the early visual system, corticothalamic feedback projections greatly outnumber thalamocortica...</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 early visual system, corticothalamic feedback projections greatly outnumber thalamocortical feedforward projections. Extensive experimental and modeling work has been devoted to the functional impact of the feedforward pathway, but the role of its denser feedback counterpart remains elusive. Here, we propose a novel unifying framework where thalamic recurrent interactions and corticothalamic feedback act in a closed-loop fashion to attune multiple stimulus representations. At each position of the visual field, the loop puts into competition local representations of the stimulus in thalamus and cortex through direct excitation of narrow topologically-aligned portions of the thalamus, accompanied with peri-geniculate nucleus mediated broad inhibition suppressing the topological surround. We built a detailed conductance-based spiking model incorporating retinal input, lateral geniculate nucleus, peri-geniculate nucleus, primary visual cortex, and all the relevant intra-areal and...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="8e6f82f04cd0da65b622d0405cd074a3" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":103069061,"asset_id":102927319,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/103069061/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="102927319"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="102927319"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 102927319; 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="102927318"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/102927318/Developing_maps_of_complex_cells_in_a_computational_model_of_V1"><img alt="Research paper thumbnail of Developing maps of complex cells in a computational model of V1" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" rel="nofollow" href="https://www.academia.edu/102927318/Developing_maps_of_complex_cells_in_a_computational_model_of_V1">Developing maps of complex cells in a computational model of V1</a></div><div class="wp-workCard_item"><span>Society for Neuroscience Annual meeting, 2008</span><span>, Nov 19, 2008</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="102927318"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="102927318"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 102927318; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=102927318]").text(description); $(".js-view-count[data-work-id=102927318]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 102927318; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='102927318']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=102927318]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":102927318,"title":"Developing maps of complex cells in a computational model of V1","internal_url":"https://www.academia.edu/102927318/Developing_maps_of_complex_cells_in_a_computational_model_of_V1","owner_id":58717480,"coauthors_can_edit":true,"owner":{"id":58717480,"first_name":"Jan","middle_initials":null,"last_name":"Antolik","page_name":"JanAntolik","domain_name":"independent","created_at":"2017-01-10T01:02:44.948-08:00","display_name":"Jan Antolik","url":"https://independent.academia.edu/JanAntolik"},"attachments":[]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="102927317"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/102927317/Reconciling_models_of_V1_development_and_adult_function"><img alt="Research paper thumbnail of Reconciling models of V1 development and adult function" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" rel="nofollow" href="https://www.academia.edu/102927317/Reconciling_models_of_V1_development_and_adult_function">Reconciling models of V1 development and adult function</a></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="102927317"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="102927317"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 102927317; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=102927317]").text(description); $(".js-view-count[data-work-id=102927317]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 102927317; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='102927317']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=102927317]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":102927317,"title":"Reconciling models of V1 development and adult function","internal_url":"https://www.academia.edu/102927317/Reconciling_models_of_V1_development_and_adult_function","owner_id":58717480,"coauthors_can_edit":true,"owner":{"id":58717480,"first_name":"Jan","middle_initials":null,"last_name":"Antolik","page_name":"JanAntolik","domain_name":"independent","created_at":"2017-01-10T01:02:44.948-08:00","display_name":"Jan Antolik","url":"https://independent.academia.edu/JanAntolik"},"attachments":[]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="102927316"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/102927316/Developing_maps_of_complex_cells_in_a_computational_model"><img alt="Research paper thumbnail of Developing maps of complex cells in a computational model" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" rel="nofollow" href="https://www.academia.edu/102927316/Developing_maps_of_complex_cells_in_a_computational_model">Developing maps of complex cells in a computational model</a></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="102927316"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="102927316"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 102927316; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=102927316]").text(description); $(".js-view-count[data-work-id=102927316]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 102927316; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='102927316']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=102927316]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":102927316,"title":"Developing maps of complex cells in a computational model","internal_url":"https://www.academia.edu/102927316/Developing_maps_of_complex_cells_in_a_computational_model","owner_id":58717480,"coauthors_can_edit":true,"owner":{"id":58717480,"first_name":"Jan","middle_initials":null,"last_name":"Antolik","page_name":"JanAntolik","domain_name":"independent","created_at":"2017-01-10T01:02:44.948-08:00","display_name":"Jan Antolik","url":"https://independent.academia.edu/JanAntolik"},"attachments":[]}, 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="102927315"><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/102927315/Assessment_of_optogenetically_driven_strategies_for_prosthetic_restoration_of_cortical_vision_in_large_scale_neural_simulation_of_V1"><img alt="Research paper thumbnail of Assessment of optogenetically-driven strategies for prosthetic restoration of cortical vision in large-scale neural simulation of V1" class="work-thumbnail" src="https://attachments.academia-assets.com/103068989/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/102927315/Assessment_of_optogenetically_driven_strategies_for_prosthetic_restoration_of_cortical_vision_in_large_scale_neural_simulation_of_V1">Assessment of optogenetically-driven strategies for prosthetic restoration of cortical vision in large-scale neural simulation of V1</a></div><div class="wp-workCard_item"><span>Scientific Reports</span><span>, 2021</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">The neural encoding of visual features in primary visual cortex (V1) is well understood, with str...</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 neural encoding of visual features in primary visual cortex (V1) is well understood, with strong correlates to low-level perception, making V1 a strong candidate for vision restoration through neuroprosthetics. However, the functional relevance of neural dynamics evoked through external stimulation directly imposed at the cortical level is poorly understood. Furthermore, protocols for designing cortical stimulation patterns that would induce a naturalistic perception of the encoded stimuli have not yet been established. Here, we demonstrate a proof of concept by solving these issues through a computational model, combining (1) a large-scale spiking neural network model of cat V1 and (2) a virtual prosthetic system transcoding the visual input into tailored light-stimulation patterns which drive in situ the optogenetically modified cortical tissue. Using such virtual experiments, we design a protocol for translating simple Fourier contrasted stimuli (gratings) into activation pat...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="6954b239ba409a0011d32734e754b163" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":103068989,"asset_id":102927315,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/103068989/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="102927315"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="102927315"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 102927315; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=102927315]").text(description); $(".js-view-count[data-work-id=102927315]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 102927315; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='102927315']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "6954b239ba409a0011d32734e754b163" } } $('.js-work-strip[data-work-id=102927315]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":102927315,"title":"Assessment of optogenetically-driven strategies for prosthetic restoration of cortical vision in large-scale neural simulation of V1","internal_url":"https://www.academia.edu/102927315/Assessment_of_optogenetically_driven_strategies_for_prosthetic_restoration_of_cortical_vision_in_large_scale_neural_simulation_of_V1","owner_id":58717480,"coauthors_can_edit":true,"owner":{"id":58717480,"first_name":"Jan","middle_initials":null,"last_name":"Antolik","page_name":"JanAntolik","domain_name":"independent","created_at":"2017-01-10T01:02:44.948-08:00","display_name":"Jan Antolik","url":"https://independent.academia.edu/JanAntolik"},"attachments":[{"id":103068989,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/103068989/thumbnails/1.jpg","file_name":"s41598-021-88960-8.pdf","download_url":"https://www.academia.edu/attachments/103068989/download_file","bulk_download_file_name":"Assessment_of_optogenetically_driven_str.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/103068989/s41598-021-88960-8-libre.pdf?1686031544=\u0026response-content-disposition=attachment%3B+filename%3DAssessment_of_optogenetically_driven_str.pdf\u0026Expires=1740608526\u0026Signature=UNzOdGaX77Sl0AB~MgsVe6DirsDMW~2g1Z~j325RF6Wl3NMmDe0-HWCsyKCyC20NiYKKQ-U1Tn0odSvzSe~Pvf5eKHbTJ8WrRj4KN1wH00CHtXEHbTIU5GcDNZizwFOuqOyxxI3DyPG6I64BdPIjHBEwUMqoMp0LUG-xzr9jMvx0qaGrZhm0FeLE-OLDRPOTK-J5dnjzokX6u00eeApYd9NA~pufrJbHG-JnTl1bGeDgWyWmnLmMk5ui7i4iFdRP02oUEIqhNDx7-qMqGmhFJoIMIrNhgOv3kseVdfYPSdJSf2WlkYxW9BLJAzc9b8D5G~tlrj9qbX~Dtv8AatPq9w__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"},{"id":103068988,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/103068988/thumbnails/1.jpg","file_name":"s41598-021-88960-8.pdf","download_url":"https://www.academia.edu/attachments/103068988/download_file","bulk_download_file_name":"Assessment_of_optogenetically_driven_str.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/103068988/s41598-021-88960-8-libre.pdf?1686031554=\u0026response-content-disposition=attachment%3B+filename%3DAssessment_of_optogenetically_driven_str.pdf\u0026Expires=1740608526\u0026Signature=gxaJXhlYhLz-h7IDgGimAf2sRdKIrrlSuhPxDadaPKcMCpzNiuQaugA6GT2hRLmae3NAvU0YF8z~BaH~ug7OK8yYMkUl587pTKXtZDSubjli9BHb8CcrLkPIYM8ri3Laa9dSWvj4QNeOUKwLOEbJYBskNJtjW87HsM7g~yIV06y2igHS0F~I-qV2x2j5E0FMRT5l2OO~jBJWadDVoGqTWvIKIMwKauFBuaMqCeIqzW58aJE7J8Ocls55Oe6yHAPbRKwhKJ6l~1ROPrj-tJG54ulE15QH8pt2DYM77CerF0iPUFxMgEiprhMHQulm8zkAQog09~vAS176YnnOHd1UYw__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}]}, 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="102927314"><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/102927314/An_Anatomically_Constrained_Model_of_V1_Simple_Cells_Predicts_the_Coexistence_of_Push_Pull_and_Broad_Inhibition"><img alt="Research paper thumbnail of An Anatomically Constrained Model of V1 Simple Cells Predicts the Coexistence of Push–Pull and Broad Inhibition" class="work-thumbnail" src="https://attachments.academia-assets.com/103069041/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/102927314/An_Anatomically_Constrained_Model_of_V1_Simple_Cells_Predicts_the_Coexistence_of_Push_Pull_and_Broad_Inhibition">An Anatomically Constrained Model of V1 Simple Cells Predicts the Coexistence of Push–Pull and Broad Inhibition</a></div><div class="wp-workCard_item"><span>The Journal of Neuroscience</span><span>, 2021</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">The spatial organization and dynamic interactions between excitatory and inhibitory synaptic inpu...</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 spatial organization and dynamic interactions between excitatory and inhibitory synaptic inputs that define the receptive field (RF) of simple cells in the cat primary visual cortex (V1) still raise the following paradoxical issues: (1) stimulation of simple cells in V1 with drifting gratings supports a wiring schema of spatially segregated sets of excitatory and inhibitory inputs activated in an opponent way by stimulus contrast polarity and (2) in contrast, intracellular studies using flashed bars suggest that although ON and OFF excitatory inputs are indeed segregated, inhibitory inputs span the entire RF regardless of input contrast polarity. Here, we propose a biologically detailed computational model of simple cells embedded in a V1-like network that resolves this seeming contradiction. We varied parametrically the RF-correlation-based bias for excitatory and inhibitory synapses and found that a moderate bias of excitatory neurons to synapse onto other neurons with correlated receptive fields and a weaker bias of inhibitory neurons to synapse onto other neurons with anticorrelated receptive fields can explain the conductance input, the postsynaptic membrane potential, and the spike train dynamics under both stimulation paradigms. This computational study shows that the same structural model can reproduce the functional diversity of visual processing observed during different visual contexts.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="69ada2114468d555cd6f86ebed1da308" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":103069041,"asset_id":102927314,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/103069041/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="102927314"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="102927314"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 102927314; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=102927314]").text(description); $(".js-view-count[data-work-id=102927314]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 102927314; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='102927314']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "69ada2114468d555cd6f86ebed1da308" } } $('.js-work-strip[data-work-id=102927314]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":102927314,"title":"An Anatomically Constrained Model of V1 Simple Cells Predicts the Coexistence of Push–Pull and Broad Inhibition","internal_url":"https://www.academia.edu/102927314/An_Anatomically_Constrained_Model_of_V1_Simple_Cells_Predicts_the_Coexistence_of_Push_Pull_and_Broad_Inhibition","owner_id":58717480,"coauthors_can_edit":true,"owner":{"id":58717480,"first_name":"Jan","middle_initials":null,"last_name":"Antolik","page_name":"JanAntolik","domain_name":"independent","created_at":"2017-01-10T01:02:44.948-08:00","display_name":"Jan Antolik","url":"https://independent.academia.edu/JanAntolik"},"attachments":[{"id":103069041,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/103069041/thumbnails/1.jpg","file_name":"Broad2021.pdf","download_url":"https://www.academia.edu/attachments/103069041/download_file","bulk_download_file_name":"An_Anatomically_Constrained_Model_of_V1.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/103069041/Broad2021-libre.pdf?1686036719=\u0026response-content-disposition=attachment%3B+filename%3DAn_Anatomically_Constrained_Model_of_V1.pdf\u0026Expires=1740608526\u0026Signature=U3YEvSEePmX7ZFrArq~0kWHCjwmPfbEGnHzykF5sijJvXnFtAwsA2duZWnIWZzTN2ZQqCUFo2J8K60emPcqGokfKa8U0JBroBVeZZU6Jsz1HyahpNBsPhDEyYay2P9rptvT0I3lZIo9ka0TKnDjHiwoQVA6DDskTvpfrSQ~zNa3th9yg5QKz2NTibclm2WOh0fEX2g53v5djJSZLbhf8fTW7Tp8Eat2iSQThkC5e70BeLHB0oE0Vmj7bt9y73mAVUNpLwDcyoMbu4~tvJw~ymygvhUUAb4FYb6j~QRLXTafj1QzS7hbhEIZIiTceo7FBUucQpO9Bu3pdo2cUmFH17A__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}]}, 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="102927313"><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/102927313/Arkheia_Data_Management_and_Communication_for_Open_Computational_Neuroscience"><img alt="Research paper thumbnail of Arkheia: Data Management and Communication for Open Computational Neuroscience" class="work-thumbnail" src="https://attachments.academia-assets.com/103069052/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/102927313/Arkheia_Data_Management_and_Communication_for_Open_Computational_Neuroscience">Arkheia: Data Management and Communication for Open Computational Neuroscience</a></div><div class="wp-workCard_item"><span>Frontiers in Neuroinformatics</span><span>, 2018</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="f22323f563ab4e3c6d9a795430704ac4" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":103069052,"asset_id":102927313,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/103069052/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="102927313"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="102927313"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 102927313; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=102927313]").text(description); $(".js-view-count[data-work-id=102927313]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 102927313; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='102927313']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "f22323f563ab4e3c6d9a795430704ac4" } } $('.js-work-strip[data-work-id=102927313]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":102927313,"title":"Arkheia: Data Management and Communication for Open Computational Neuroscience","internal_url":"https://www.academia.edu/102927313/Arkheia_Data_Management_and_Communication_for_Open_Computational_Neuroscience","owner_id":58717480,"coauthors_can_edit":true,"owner":{"id":58717480,"first_name":"Jan","middle_initials":null,"last_name":"Antolik","page_name":"JanAntolik","domain_name":"independent","created_at":"2017-01-10T01:02:44.948-08:00","display_name":"Jan Antolik","url":"https://independent.academia.edu/JanAntolik"},"attachments":[{"id":103069052,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/103069052/thumbnails/1.jpg","file_name":"fninf-12-00006.pdf","download_url":"https://www.academia.edu/attachments/103069052/download_file","bulk_download_file_name":"Arkheia_Data_Management_and_Communicatio.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/103069052/fninf-12-00006-libre.pdf?1686036705=\u0026response-content-disposition=attachment%3B+filename%3DArkheia_Data_Management_and_Communicatio.pdf\u0026Expires=1740608526\u0026Signature=UWVMjsC5eFcLHG3H-l1-7o6px0NcEmCfpEVd-0Q5oYNPaSCfmyaYDDUeay0UFnKLADJ0BK1FsD13YDGuGDysB0rD8MZ6zEJH1mcVlzT6fsg9ycvurKJT6yevJX1vFz~~8qp9UCnswzrMLu9yWiy-eC~y5KzYm627rJhFmVHhN0PuTed3miuEHtEOJU0O5IQcJTegJ78k3dkYLABm9XAlI7z3rZzTcIm9TWDuXLf4ZKuS7iged2reuUvmyD06CPLHtFsm1ugD10Ig1Jm5JTPxishFOOSqkPGKA778aTVcnJOxNWI2p66Z2sAWt1zp5qfe3zH9Ldld3y37-od7s0E3hQ__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}]}, 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="102927312"><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/102927312/Cortical_visual_prosthesis_a_detailed_large_scale_simulation_study"><img alt="Research paper thumbnail of Cortical visual prosthesis: a detailed large-scale simulation study" class="work-thumbnail" src="https://attachments.academia-assets.com/103069040/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/102927312/Cortical_visual_prosthesis_a_detailed_large_scale_simulation_study">Cortical visual prosthesis: a detailed large-scale simulation study</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Recent advances in applying optogenetics in primates initiated the development of light based pro...</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">Recent advances in applying optogenetics in primates initiated the development of light based prosthetic implants for sensory restoration. Thanks to being the most well explored cortical area that is readily accessible at the surface of the brain, vision restoration via direct optogenetic activation of primary visual cortex is one of the most promising early targets for a optogenetics based prosthetic program. However, two fundamental elements of the cortical optogenetic prosthesis remain unclear. First, the exact mechanisms of neural dynamics under direct cortical stimulation, especially in the context of living, active and functionally specific intra-cortical neural circuitry, is poorly understood. Second, we lack protocols for transformation of arbitrary visual stimuli into light activation patterns that would induce perception of the said stimulus by the subject. In this study we address these issues using a large-scale spiking neural network modeling strategy of high biological...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="9d7ae930474c41c6be0bd909e3c09b75" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":103069040,"asset_id":102927312,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/103069040/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="102927312"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="102927312"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 102927312; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=102927312]").text(description); $(".js-view-count[data-work-id=102927312]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 102927312; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='102927312']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "9d7ae930474c41c6be0bd909e3c09b75" } } $('.js-work-strip[data-work-id=102927312]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":102927312,"title":"Cortical visual prosthesis: a detailed large-scale simulation study","internal_url":"https://www.academia.edu/102927312/Cortical_visual_prosthesis_a_detailed_large_scale_simulation_study","owner_id":58717480,"coauthors_can_edit":true,"owner":{"id":58717480,"first_name":"Jan","middle_initials":null,"last_name":"Antolik","page_name":"JanAntolik","domain_name":"independent","created_at":"2017-01-10T01:02:44.948-08:00","display_name":"Jan Antolik","url":"https://independent.academia.edu/JanAntolik"},"attachments":[{"id":103069040,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/103069040/thumbnails/1.jpg","file_name":"610378.full.pdf","download_url":"https://www.academia.edu/attachments/103069040/download_file","bulk_download_file_name":"Cortical_visual_prosthesis_a_detailed_la.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/103069040/610378.full-libre.pdf?1686036724=\u0026response-content-disposition=attachment%3B+filename%3DCortical_visual_prosthesis_a_detailed_la.pdf\u0026Expires=1740608526\u0026Signature=Y26lvldwFruxtP1zhyHm9gLtR17L6ZJ7wVtZQoEvpKJZ2xEp0wTDNJhr8kZAuWcTZfY5ri~eRsfKP1ym0BUon8NtkMHleZvFUH4Iay-DOOblAGhETZ3kRAUfITRYHB9nsdW9-4tjxuDjbbv2Y2FUEJ8eWZvut-jeqQqEO7ew3bgr5eqTZIoj8p83Bha9WTcD0V1xMixUZpe62CKmjrulfytXNyM0XYVz05~VtDxZIeQTb2x~~4xiDuy0wN4gdgrJ2qsf1aeuYOkXB9EH8Iqi48m3F3GOrvamwz3A2PUDOV97uHAGvhR~GM4BQDBDflLeJLy4w2TVXgIIxSZvgpkSKg__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}]}, 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="102927311"><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/102927311/A_comprehensive_data_driven_model_of_cat_primary_visual_cortex"><img alt="Research paper thumbnail of A comprehensive data-driven model of cat primary visual cortex" class="work-thumbnail" src="https://attachments.academia-assets.com/103069042/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/102927311/A_comprehensive_data_driven_model_of_cat_primary_visual_cortex">A comprehensive data-driven model of cat primary visual cortex</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Knowledge integration based on the relationship between structure and function of the neural subs...</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">Knowledge integration based on the relationship between structure and function of the neural substrate is one of the main targets of neuroinformatics and data-driven computational modeling. However, the multiplicity of data sources, the diversity of benchmarks, the mixing of observables of different natures, and the necessity of a long-term, systematic approach make such a task challenging. Here we present a first snapshot of a long-term integrative modeling program designed to address this issue: a comprehensive spiking model of cat primary visual cortex satisfying an unprecedented range of anatomical, statistical and functional constraints under a wide range of visual input statistics. In the presence of physiological levels of tonic stochastic bombardment by spontaneous thalamic activity, the modeled cortical reverberations self-generate a sparse asynchronous ongoing activity that quantitatively matches a range of experimentally measured statistics. When integrating feed-forward ...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="82df6256e55e0c739a53f0b546a0319e" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":103069042,"asset_id":102927311,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/103069042/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="102927311"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="102927311"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 102927311; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=102927311]").text(description); $(".js-view-count[data-work-id=102927311]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 102927311; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='102927311']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); 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$(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="102927310"><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/102927310/Rapid_Long_Range_Disynaptic_Inhibition_Explains_the_Formation_of_Cortical_Orientation_Maps"><img alt="Research paper thumbnail of Rapid Long-Range Disynaptic Inhibition Explains the Formation of Cortical Orientation Maps" class="work-thumbnail" src="https://attachments.academia-assets.com/103069035/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/102927310/Rapid_Long_Range_Disynaptic_Inhibition_Explains_the_Formation_of_Cortical_Orientation_Maps">Rapid Long-Range Disynaptic Inhibition Explains the Formation of Cortical Orientation Maps</a></div><div class="wp-workCard_item"><span>Frontiers in Neural Circuits</span><span>, 2017</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Competitive interactions are believed to underlie many types of cortical processing, ranging from...</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">Competitive interactions are believed to underlie many types of cortical processing, ranging from memory formation, attention and development of cortical functional organization (e.g., development of orientation maps in primary visual cortex). In the latter case, the competitive interactions happen along the cortical surface, with local populations of neurons reinforcing each other, while competing with those displaced more distally. This specific configuration of lateral interactions is however in stark contrast with the known properties of the anatomical substrate, i.e., excitatory connections (mediating reinforcement) having longer reach than inhibitory ones (mediating competition). No satisfactory biologically plausible resolution of this conflict between anatomical measures, and assumed cortical function has been proposed. Recently a specific pattern of delays between different types of neurons in cat cortex has been discovered, where direct mono-synaptic excitation has approximately the same delay, as the combined delays of the disynaptic inhibitory interactions between excitatory neurons (i.e., the sum of delays from excitatory to inhibitory and from inhibitory to excitatory neurons). Here we show that this specific pattern of delays represents a biologically plausible explanation for how short-range inhibition can support competitive interactions that underlie the development of orientation maps in primary visual cortex. We demonstrate this statement analytically under simplifying conditions, and subsequently show using network simulations that development of orientation maps is preserved when long-range excitation, direct inhibitory to inhibitory interactions, and moderate inequality in the delays between excitatory and inhibitory pathways is added.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="44ba747b60e1e315c377df43f23f9a61" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":103069035,"asset_id":102927310,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/103069035/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="102927310"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="102927310"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 102927310; 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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="102927274"><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/102927274/Mechanisms_for_Stable_Robust_and_Adaptive_Development_of_Orientation_Maps_in_the_Primary_Visual_Cortex"><img alt="Research paper thumbnail of Mechanisms for Stable, Robust, and Adaptive Development of Orientation Maps in the Primary Visual Cortex" class="work-thumbnail" src="https://attachments.academia-assets.com/103068991/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/102927274/Mechanisms_for_Stable_Robust_and_Adaptive_Development_of_Orientation_Maps_in_the_Primary_Visual_Cortex">Mechanisms for Stable, Robust, and Adaptive Development of Orientation Maps in the Primary Visual Cortex</a></div><div class="wp-workCard_item"><span>The Journal of Neuroscience</span><span>, 2013</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Development of orientation maps in ferret and cat primary visual cortex (V1) has been shown to be...</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">Development of orientation maps in ferret and cat primary visual cortex (V1) has been shown to be stable, in that the earliest measurable maps are similar in form to the eventual adult map, robust, in that similar maps develop in both dark rearing and in a variety of normal visual environments, and yet adaptive, in that the final map pattern reflects the statistics of the specific visual environment. How can these three properties be reconciled? Using mechanistic models of the development of neural connectivity in V1, we show for the first time that realistic stable, robust, and adaptive map development can be achieved by including two low-level mechanisms originally motivated from single-neuron results. Specifically, contrast-gain control in the retinal ganglion cells and the lateral geniculate nucleus reduces variation in the presynaptic drive due to differences in input patterns, while homeostatic plasticity of V1 neuron excitability reduces the postsynaptic variability in firing...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="c09e066c023efa7e15fbe415c4d69504" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":103068991,"asset_id":102927274,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/103068991/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="102927274"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="102927274"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 102927274; 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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="30887560"><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/30887560/Automatic_annotation_of_medical_records"><img alt="Research paper thumbnail of Automatic annotation of medical records" 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/30887560/Automatic_annotation_of_medical_records">Automatic annotation of medical records</a></div><div class="wp-workCard_item"><span>Studies in health technology and informatics</span><span>, 2005</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">One of the research projects running at the medical informatics department of the Institute of Co...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">One of the research projects running at the medical informatics department of the Institute of Computer Science AS CR explores the problem of medical information representation and development of electronic health record (EHR). With respect to this effort an interesting problem arises: how to transfer knowledge from a medical record written in a free text form into a structured electronic format represented by the EHR. Currently, this task was solved by writing extraction rules (regular expressions) for every element of information that is to be extracted from the medical record. However, such approach is very time consuming and requires supervision of a skilled programmer whenever the target area of medicine is changed. In this article we explore the possibility to mechanize this process by automatically generating the extraction rules from a pre-annotated corpus of medical records. Since we are currently in the phase of data acquisition and preliminary tests we will not present an...</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="30887560"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="30887560"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 30887560; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=30887560]").text(description); 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dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=30887560]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":30887560,"title":"Automatic annotation of medical records","internal_url":"https://www.academia.edu/30887560/Automatic_annotation_of_medical_records","owner_id":58717480,"coauthors_can_edit":true,"owner":{"id":58717480,"first_name":"Jan","middle_initials":null,"last_name":"Antolik","page_name":"JanAntolik","domain_name":"independent","created_at":"2017-01-10T01:02:44.948-08:00","display_name":"Jan Antolik","url":"https://independent.academia.edu/JanAntolik"},"attachments":[]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="30887559"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/30887559/Unified_developmental_model_of_maps_complex_cells_and_surround_modulation_in_the_primary_visual_cortex"><img alt="Research paper thumbnail of Unified developmental model of maps, complex cells and surround modulation in the primary visual cortex" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" rel="nofollow" href="https://www.academia.edu/30887559/Unified_developmental_model_of_maps_complex_cells_and_surround_modulation_in_the_primary_visual_cortex">Unified developmental model of maps, complex cells and surround modulation in the primary visual cortex</a></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="30887559"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="30887559"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 30887559; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=30887559]").text(description); $(".js-view-count[data-work-id=30887559]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 30887559; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='30887559']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=30887559]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":30887559,"title":"Unified developmental model of maps, complex cells and surround modulation in the primary visual cortex","internal_url":"https://www.academia.edu/30887559/Unified_developmental_model_of_maps_complex_cells_and_surround_modulation_in_the_primary_visual_cortex","owner_id":58717480,"coauthors_can_edit":true,"owner":{"id":58717480,"first_name":"Jan","middle_initials":null,"last_name":"Antolik","page_name":"JanAntolik","domain_name":"independent","created_at":"2017-01-10T01:02:44.948-08:00","display_name":"Jan Antolik","url":"https://independent.academia.edu/JanAntolik"},"attachments":[]}, 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="30849537"><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/30849537/Model_Constrained_by_Visual_Hierarchy_Improves_Prediction_of_Neural_Responses_to_Natural_Scenes"><img alt="Research paper thumbnail of Model Constrained by Visual Hierarchy Improves Prediction of Neural Responses to Natural Scenes" 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/30849537/Model_Constrained_by_Visual_Hierarchy_Improves_Prediction_of_Neural_Responses_to_Natural_Scenes">Model Constrained by Visual Hierarchy Improves Prediction of Neural Responses to Natural Scenes</a></div><div class="wp-workCard_item"><span>PLOS Computational Biology</span><span>, 2016</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Accurate estimation of neuronal receptive fields is essential for understanding sensory processin...</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">Accurate estimation of neuronal receptive fields is essential for understanding sensory processing in the early visual system. Yet a full characterization of receptive fields is still incomplete, especially with regard to natural visual stimuli and in complete populations of cortical neurons. While previous work has incorporated known structural properties of the early visual system, such as lateral connectivity, or imposing simple-cell-like receptive field structure, no study has exploited the fact that nearby V1 neurons share common feed-forward input from thalamus and other upstream cortical neurons. We introduce a new method for estimating receptive fields simultaneously for a population of V1 neurons, using a model-based analysis incorporating knowledge of the feed-forward visual hierarchy. We assume that a population of V1 neurons shares a common pool of thalamic inputs, and consists of two layers of simple and complex-like V1 neurons. When fit to recordings of a local population of mouse layer 2/3 V1 neurons, our model offers an accurate description of their response to natural images and significant improvement of prediction power over the current state-of-the-art methods. We show that the responses of a large local population of V1 neurons with locally diverse receptive fields can be described with surprisingly limited number of thalamic inputs, consistent with recent experimental findings. Our structural model not only offers an improved functional characterization of V1 neurons, but also provides a framework for studying the relationship between connectivity and function in visual cortical areas.</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="30849537"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="30849537"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 30849537; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); 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window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=30849535]").text(description); $(".js-view-count[data-work-id=30849535]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 30849535; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='30849535']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); 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This poses a challenge for existing tool chains, as the set of tools involved in a typical modeler&#39;s workflow is expanding concomitantly, with growing complexity in the metadata flowing between them. For many parts of the workflow, a range of tools is available; however, numerous areas lack dedicated tools, while integration of existing tools is limited. This forces modelers to either handle the workflow manually, leading to errors, or to write substantial amounts of code to automate parts of the workflow, in both cases reducing their productivity. To address these issues, we have developed Mozaik: a workflow system for spiking neuronal network simulations written in Python. Mozaik integrates model, experiment and stimulation...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="c8edbec658b5df075c0a411111da8fd0" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":51277542,"asset_id":30849534,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/51277542/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="30849534"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="30849534"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 30849534; 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