{"id":6837,"date":"2021-03-26T15:30:32","date_gmt":"2021-03-26T14:30:32","guid":{"rendered":"http:\/\/bernstein.mylapo.de\/satellite-workshops\/"},"modified":"2026-05-21T10:16:08","modified_gmt":"2026-05-21T08:16:08","slug":"satellite-workshops","status":"publish","type":"page","link":"https:\/\/bernstein-network.de\/en\/bernstein-conference\/program\/satellite-workshops\/","title":{"rendered":"Satellite Workshops"},"content":{"rendered":"<div id='av_section_1'  class='avia-section av-7rjl82-84ef7e726bf78a698a8f75f367566c56 main_color avia-section-default avia-no-border-styling  avia-builder-el-0  el_before_av_section  avia-builder-el-first  avia-bg-style-scroll container_wrap fullsize'  ><div class='container av-section-cont-open' ><main  class='template-page content  av-content-full alpha units'><div class='post-entry post-entry-type-page post-entry-6837'><div class='entry-content-wrapper clearfix'>\n\n<style type=\"text\/css\" data-created_by=\"avia_inline_auto\" id=\"style-css-av-5hrx02-ea2938837ea1c6a0e23a8516c5b334bb\">\n.flex_column.av-5hrx02-ea2938837ea1c6a0e23a8516c5b334bb{\nborder-radius:0px 0px 0px 0px;\npadding:0px 0px 0px 0px;\n}\n<\/style>\n<div  class='flex_column av-5hrx02-ea2938837ea1c6a0e23a8516c5b334bb av_one_full  avia-builder-el-1  avia-builder-el-no-sibling  first flex_column_div av-zero-column-padding  '     ><p>\n<style type=\"text\/css\" data-created_by=\"avia_inline_auto\" id=\"style-css-av-4ldxoy-ba64a50229fb7ce258ca73215c7e9079\">\n#top .av-special-heading.av-4ldxoy-ba64a50229fb7ce258ca73215c7e9079{\npadding-bottom:10px;\n}\nbody .av-special-heading.av-4ldxoy-ba64a50229fb7ce258ca73215c7e9079 .av-special-heading-tag .heading-char{\nfont-size:25px;\n}\n.av-special-heading.av-4ldxoy-ba64a50229fb7ce258ca73215c7e9079 .av-subheading{\nfont-size:15px;\n}\n<\/style>\n<div  class='av-special-heading av-4ldxoy-ba64a50229fb7ce258ca73215c7e9079 av-special-heading-h1 blockquote modern-quote  avia-builder-el-2  el_before_av_hr  avia-builder-el-first '><h1 class='av-special-heading-tag '  >Satellite Workshops<\/h1><div class=\"special-heading-border\"><div class=\"special-heading-inner-border\"><\/div><\/div><\/div><br \/>\n\n<style type=\"text\/css\" data-created_by=\"avia_inline_auto\" id=\"style-css-av-khnqxmhr-f005f3883bc28340180a25d2f926af5c\">\n#top .hr.av-khnqxmhr-f005f3883bc28340180a25d2f926af5c{\nmargin-top:0px;\nmargin-bottom:30px;\n}\n.hr.av-khnqxmhr-f005f3883bc28340180a25d2f926af5c .hr-inner{\nwidth:60px;\nborder-color:#004c93;\n}\n<\/style>\n<div  class='hr av-khnqxmhr-f005f3883bc28340180a25d2f926af5c hr-custom  avia-builder-el-3  el_after_av_heading  el_before_av_textblock  hr-left hr-icon-no'><span class='hr-inner inner-border-av-border-fat'><span class=\"hr-inner-style\"><\/span><\/span><\/div><br \/>\n<section  class='av_textblock_section av-kn66nyia-3246d696ca1396c9228ab296399ff080 '  ><div class='avia_textblock' ><p><span data-contrast=\"auto\">The Satellite Workshops of the Bernstein Conference provide a stage to discuss topical research questions, novel scientific approaches, and challenges in computational neuroscience and related fields.<br \/>\n<\/span><\/p>\n<\/div><\/section><br \/>\n<section  class='av_textblock_section av-l2u7tq1u-f6fc7a98cec93166bdf79127c6fbdcfc '  ><div class='avia_textblock' ><p><strong>All workshops will be held in parallel sessions and moderated by the workshop organizers.<\/strong><\/p>\n<\/div><\/section><\/p><\/div>\n\n<\/div><\/div><\/main><!-- close content main element --><\/div><\/div>\n<style type=\"text\/css\" data-created_by=\"avia_inline_auto\" id=\"style-css-av-kn66oj2g-f81dc645b5d6ab198b5cad5f045df200\">\n.avia-section.av-kn66oj2g-f81dc645b5d6ab198b5cad5f045df200{\nbackground-color:#f5f7fa;\nbackground-image:unset;\n}\n<\/style>\n<div id='av_section_2'  class='avia-section av-kn66oj2g-f81dc645b5d6ab198b5cad5f045df200 main_color avia-section-default avia-no-border-styling  avia-builder-el-6  el_after_av_section  el_before_av_section  avia-bg-style-scroll container_wrap fullsize'  ><div class='container av-section-cont-open' ><div class='template-page content  av-content-full alpha units'><div class='post-entry post-entry-type-page post-entry-6837'><div class='entry-content-wrapper clearfix'>\n<div  class='flex_column av-muz6q-6c27bd0ea0358a553d38615cd7bd7ea7 av_one_third  avia-builder-el-7  el_before_av_one_third  avia-builder-el-first  first flex_column_div  '     ><style type=\"text\/css\" data-created_by=\"avia_inline_auto\" id=\"style-css-av-l2930gfx-ceaa0979849f2328514c3ce6cdb899e4\">\n.iconbox.av-l2930gfx-ceaa0979849f2328514c3ce6cdb899e4 .iconbox_icon{\nbackground-color:#f5f7fa;\nborder:1px solid #f5f7fa;\ncolor:#004c93;\n}\n.iconbox.av-l2930gfx-ceaa0979849f2328514c3ce6cdb899e4 .iconbox_icon.avia-svg-icon svg:first-child{\nfill:#004c93;\nstroke:#004c93;\n}\n<\/style>\n<article  class='iconbox iconbox_top av-l2930gfx-ceaa0979849f2328514c3ce6cdb899e4 av-no-box  avia-builder-el-8  avia-builder-el-no-sibling ' ><div class=\"iconbox_content\"><header class=\"entry-content-header\" aria-label=\"Icon: Date\"><div class='iconbox_icon heading-color avia-iconfont avia-font-entypo-fontello' data-av_icon='\ue85b' data-av_iconfont='entypo-fontello'  ><\/div><h3 class='iconbox_content_title ' >Date<\/h3><\/header><div class='iconbox_content_container ' ><p>Monday, Sep 28, 14:00 &#8211; 18:30 CEST<br \/>\nTuesday, Sep 29, 8:30 &#8211; 12:30 CEST<\/p>\n<\/div><\/div><footer class=\"entry-footer\"><\/footer><\/article><\/div>\n<style type=\"text\/css\" data-created_by=\"avia_inline_auto\" id=\"style-css-av-2b1oqa-d62a65a34698d033d119d1d680500314\">\n.flex_column.av-2b1oqa-d62a65a34698d033d119d1d680500314{\nborder-radius:0px 0px 0px 0px;\npadding:0px 0px 0px 0px;\n}\n<\/style>\n<div  class='flex_column av-2b1oqa-d62a65a34698d033d119d1d680500314 av_one_third  avia-builder-el-9  el_after_av_one_third  el_before_av_one_third  flex_column_div av-zero-column-padding  '     ><style type=\"text\/css\" data-created_by=\"avia_inline_auto\" id=\"style-css-av-l2931ns5-6feab6dc5f628ddde86c5bf7f101dfe8\">\n.iconbox.av-l2931ns5-6feab6dc5f628ddde86c5bf7f101dfe8 .iconbox_icon{\nbackground-color:#f5f7fa;\nborder:1px solid #f5f7fa;\ncolor:#004c93;\n}\n.iconbox.av-l2931ns5-6feab6dc5f628ddde86c5bf7f101dfe8 .iconbox_icon.avia-svg-icon svg:first-child{\nfill:#004c93;\nstroke:#004c93;\n}\n<\/style>\n<article  class='iconbox iconbox_top av-l2931ns5-6feab6dc5f628ddde86c5bf7f101dfe8 av-no-box  avia-builder-el-10  avia-builder-el-no-sibling ' ><div class=\"iconbox_content\"><header class=\"entry-content-header\" aria-label=\"Icon: Venue\"><div class='iconbox_icon heading-color avia-iconfont avia-font-entypo-fontello' data-av_icon='\ue842' data-av_iconfont='entypo-fontello'  ><\/div><h3 class='iconbox_content_title ' >Venue<\/h3><\/header><div class='iconbox_content_container ' ><p>Goethe University<br \/>\nCampus Westend \/ Seminarhaus<br \/>\nMax-Horkheimer-Stra\u00dfe 4<br \/>\n60323 Frankfurt am Main<br \/>\nGermany<\/p>\n<\/div><\/div><footer class=\"entry-footer\"><\/footer><\/article><\/div><div  class='flex_column av-4arzoy-b000443fff6a7e3973093a8d47497089 av_one_third  avia-builder-el-11  el_after_av_one_third  avia-builder-el-last  flex_column_div  '     ><style type=\"text\/css\" data-created_by=\"avia_inline_auto\" id=\"style-css-av-l2935cgh-257dbd9ce24d9de84b7f57d58671c743\">\n.iconbox.av-l2935cgh-257dbd9ce24d9de84b7f57d58671c743 .iconbox_icon{\nbackground-color:#f5f7fa;\nborder:1px solid #f5f7fa;\ncolor:#004c93;\n}\n.iconbox.av-l2935cgh-257dbd9ce24d9de84b7f57d58671c743 .iconbox_icon.avia-svg-icon svg:first-child{\nfill:#004c93;\nstroke:#004c93;\n}\n<\/style>\n<article  class='iconbox iconbox_top av-l2935cgh-257dbd9ce24d9de84b7f57d58671c743 av-no-box  avia-builder-el-12  avia-builder-el-no-sibling ' ><div class=\"iconbox_content\"><header class=\"entry-content-header\" aria-label=\"Icon: Workshop Chairs\"><div class='iconbox_icon heading-color avia-iconfont avia-font-entypo-fontello' data-av_icon='\ue80a' data-av_iconfont='entypo-fontello'  ><\/div><h3 class='iconbox_content_title ' >Workshop Chairs<\/h3><\/header><div class='iconbox_content_container ' ><p><strong>Katharina Wilmes <\/strong>| Institute of Neuroinformatics in Zurich, Switzerland (Chair)<\/p>\n<p><strong>Juan \u00c1lvaro Gallego <\/strong>|\u00a0Champalimaud Foundation, Portugal (Workshop Vice Chair)<\/p>\n<\/div><\/div><footer class=\"entry-footer\"><\/footer><\/article><\/div>\n\n<\/div><\/div><\/div><!-- close content main div --><\/div><\/div><div id='av_section_3'  class='avia-section av-4ugtr9e-19ab4f35e53ff0af20035b2c8f66ba87 main_color avia-section-default avia-no-border-styling  avia-builder-el-13  el_after_av_section  avia-builder-el-last  avia-bg-style-scroll container_wrap fullsize'  ><div class='container av-section-cont-open' ><div class='template-page content  av-content-full alpha units'><div class='post-entry post-entry-type-page post-entry-6837'><div class='entry-content-wrapper clearfix'>\n<div  class='flex_column av-4n971s2-1dbde7f783718447e70e760f37069372 av_one_full  avia-builder-el-14  el_before_av_one_full  avia-builder-el-first  first flex_column_div  '     ><p>\n<style type=\"text\/css\" data-created_by=\"avia_inline_auto\" id=\"style-css-av-4lyw62a-1067c394567d68da8516ba93c542709a\">\n#top .hr.av-4lyw62a-1067c394567d68da8516ba93c542709a{\nmargin-top:0px;\nmargin-bottom:30px;\n}\n.hr.av-4lyw62a-1067c394567d68da8516ba93c542709a .hr-inner{\nwidth:60px;\nborder-color:#004c93;\n}\n<\/style>\n<div  class='hr av-4lyw62a-1067c394567d68da8516ba93c542709a hr-custom  avia-builder-el-15  el_before_av_textblock  avia-builder-el-first  hr-left hr-icon-no'><span class='hr-inner inner-border-av-border-fat'><span class=\"hr-inner-style\"><\/span><\/span><\/div><br \/>\n<section  class='av_textblock_section av-4kvzpea-86479eae95e7e734cdaff5f464ce21a1 '  ><div class='avia_textblock' ><h3>Half-day workshops: Monday, Sept 28<\/h3>\n<\/div><\/section><\/p><\/div><div  class='flex_column av-4j50h0y-568e14174dc42a94e3abbb289c239e20 av_one_full  avia-builder-el-17  el_after_av_one_full  el_before_av_one_full  first flex_column_div  column-top-margin'     ><p><section  class='av_textblock_section av-4h6zwaa-d44bf021dbeb7659e0f534f45650871d '  ><div class='avia_textblock' ><p><span style=\"color: #000000;\"><strong>Plasticity and function of hippocampal memory circuits<\/strong><br \/>\n<em>Organizers:<\/em><br \/>\n<\/span><span style=\"color: #000000;\">Samuel Eckmann | University of Cambridge, UK<br \/>\nUri Cohen | Weizmann Institute of Science, Israel<\/span><em><br \/>\n<\/em><\/p>\n<\/div><\/section><br \/>\n<div  class='togglecontainer av-fxv84i-2ae963bad2c44942374afc1bf1852228  avia-builder-el-19  el_after_av_textblock  avia-builder-el-last  toggle_close_all' >\n<section class='av_toggle_section av-4e7pl2q-05fbdc62e596428e84716ffacacf56a4' ><div role=\"tablist\" class=\"single_toggle\" data-tags=\"{All} \"  ><p id='toggle-toggle-id-1' data-fake-id='#toggle-id-1' class='toggler  av-title-above '  role='tab' tabindex='0' aria-controls='toggle-id-1' data-slide-speed=\"200\" data-title=\"Description\" data-title-open=\"\" data-aria_collapsed=\"Click to expand: Description\" data-aria_expanded=\"Click to collapse: Description\">Description<span class=\"toggle_icon\"><span class=\"vert_icon\"><\/span><span class=\"hor_icon\"><\/span><\/span><\/p><div id='toggle-id-1' aria-labelledby='toggle-toggle-id-1' role='region' class='toggle_wrap  av-title-above'  ><div class='toggle_content invers-color ' ><p>This workshop aims to bridge the gap between theoretical models and experimental realities of hippocampal memory processing. We will explore how theoretical models of high-capacity associative memory may be supported by dendritic processing and the detailed connectivity between specific neuron subtypes. We further highlight the tension between one-shot memory storage via Behavioral Timescale Synaptic Plasticity (BTSP) and incremental STDP models in light of the emerging role of memory replay and consolidation. In summary, our workshop aims to facilitate a shift from the hippocampus as a static storage center to a dynamic memory system capable of generalizing across diverse contexts.<\/p>\n<\/div><\/div><\/div><\/section>\n<section class='av_toggle_section av-4c0msia-37fc34e3f9efff22bb8c262d2ba4b81b' ><div role=\"tablist\" class=\"single_toggle\" data-tags=\"{All} \"  ><p id='toggle-toggle-id-2' data-fake-id='#toggle-id-2' class='toggler  av-title-above '  role='tab' tabindex='0' aria-controls='toggle-id-2' data-slide-speed=\"200\" data-title=\"Schedule: Monday, Sept 28\" data-title-open=\"\" data-aria_collapsed=\"Click to expand: Schedule: Monday, Sept 28\" data-aria_expanded=\"Click to collapse: Schedule: Monday, Sept 28\">Schedule: Monday, Sept 28<span class=\"toggle_icon\"><span class=\"vert_icon\"><\/span><span class=\"hor_icon\"><\/span><\/span><\/p><div id='toggle-id-2' aria-labelledby='toggle-toggle-id-2' role='region' class='toggle_wrap  av-title-above'  ><div class='toggle_content invers-color ' ><table width=\"959\">\n<tbody>\n<tr>\n<td width=\"94\"><strong>14:00<\/strong><\/td>\n<td width=\"425\">Dmitry Krotov<br \/>\n<span style=\"font-family: inherit;\">(Independent Researcher, USA)<\/span><\/td>\n<td width=\"441\"><em>Dense Associative Memory and its potential role in brain computation<\/em><\/td>\n<\/tr>\n<tr>\n<td width=\"94\"><strong>14:30<\/strong><\/td>\n<td width=\"425\">Mohadeseh Shafiei Kafraj<br \/>\n<span style=\"font-family: inherit;\">(University College London, UK)<\/span><\/td>\n<td width=\"441\"><em>A dendritic associative memory with compositional representations<\/em><\/td>\n<\/tr>\n<tr>\n<td width=\"94\"><strong>15:00<\/strong><\/td>\n<td width=\"425\">Everton Agnes<br \/>\n(University of Basel, Switzerland)<\/td>\n<td width=\"441\"><em>Attractors meet dendrites: a circuit mechanism for selective recall in high-capacity memory networks<\/em><\/td>\n<\/tr>\n<tr>\n<td width=\"94\"><strong>15:30<\/strong><\/td>\n<td width=\"425\">Jake Watson<br \/>\n(Hospital del Mar Research Institute, Spain)<\/td>\n<td width=\"441\"><em>From cell types to circuits: hippocampal CA3 organisation across species<\/em><\/td>\n<\/tr>\n<tr>\n<td width=\"94\"><strong>16:00<\/strong><\/td>\n<td colspan=\"2\" width=\"865\"><em>\u00a0Coffee break<\/em><\/td>\n<\/tr>\n<tr>\n<td width=\"94\"><strong>16:30<\/strong><\/td>\n<td width=\"425\">Jozsef Csicsvari<br \/>\n(Institute of Science and Technology Austria (ISTA), Austria)<\/td>\n<td width=\"441\"><em>Converging manifold coding in the hippocampus and medial prefrontal cortex across increasing experience in spatial memory tasks<\/em><\/td>\n<\/tr>\n<tr>\n<td width=\"94\"><strong>17:00<\/strong><\/td>\n<td width=\"425\">Judit Makara<br \/>\n(HUN-REN Institute of Experimental Medicine, Hungary)<\/td>\n<td width=\"441\"><em>Reorganisation of hippocampal representations by learning and changing contexts<\/em><\/td>\n<\/tr>\n<tr>\n<td width=\"94\"><strong>17:30<\/strong><\/td>\n<td width=\"425\">Henning Sprekeler<br \/>\n(Technische Universit\u00e4t Berlin, Germany)<\/td>\n<td width=\"441\"><em>Memory consolidation and representational drift<\/em><\/td>\n<\/tr>\n<tr>\n<td width=\"94\"><strong>18:00<\/strong><\/td>\n<td colspan=\"2\" width=\"865\"><em>Discussion<\/em><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div><\/div><\/div><\/section>\n<\/div><\/p><\/div><div  class='flex_column av-4azh4n6-5bf088f217961a81d122a7c1c1b33d91 av_one_full  avia-builder-el-20  el_after_av_one_full  el_before_av_one_full  first flex_column_div  column-top-margin'     ><p><section  class='av_textblock_section av-498atn6-d42e45bfb38dcb00980fe6cb5d18c8dc '  ><div class='avia_textblock' ><p><span style=\"color: #000000;\"><strong>How the parts work together as a whole: From brain-wide neural representations to computations<\/strong><em><br \/>\nOrganizers:<\/em><br \/>\n<\/span><span style=\"color: #000000;\">Douglas Feitosa Tom\u00e9 | Institute of Science and Technology Austria (ISTA), Austria<br \/>\nAdrienne Fairhall | University of Washington, USA<\/span><em><br \/>\n<\/em><\/p>\n<\/div><\/section><br \/>\n<div  class='togglecontainer av-4796dpu-4dcadd9fd2e0b7d7181be2472e3b4cce  avia-builder-el-22  el_after_av_textblock  avia-builder-el-last  toggle_close_all' >\n<section class='av_toggle_section av-457awz6-7722941a0b152e131cc3bbdadf05ab72' ><div role=\"tablist\" class=\"single_toggle\" data-tags=\"{All} \"  ><p id='toggle-toggle-id-3' data-fake-id='#toggle-id-3' class='toggler  av-title-above '  role='tab' tabindex='0' aria-controls='toggle-id-3' data-slide-speed=\"200\" data-title=\"Description\" data-title-open=\"\" data-aria_collapsed=\"Click to expand: Description\" data-aria_expanded=\"Click to collapse: Description\">Description<span class=\"toggle_icon\"><span class=\"vert_icon\"><\/span><span class=\"hor_icon\"><\/span><\/span><\/p><div id='toggle-id-3' aria-labelledby='toggle-toggle-id-3' role='region' class='toggle_wrap  av-title-above'  ><div class='toggle_content invers-color ' ><p>Recent technological advances have enabled large-scale recordings across the brain, revealing distributed neural representations that span multiple regions and support perception, cognition, and behavior. While these developments have expanded our ability to map brain-wide dynamics, they raise a fundamental unresolved question: how are distributed neural representations coordinated to implement computations? This workshop will address this question by bringing together cutting-edge work at the interface of theoretical and experimental neuroscience. Bridging these domains is essential: the scale and complexity of brain-wide recordings require new theoretical frameworks to uncover underlying computations, while emerging theories of distributed computation demand targeted experimental validation. The program will include an introductory overview, research talks combining data-driven modeling with theory-guided experiments, and dedicated discussion sessions to foster active engagement and critical debate. We will focus on key questions such as how computations are distributed across brain regions, whether trade-offs exist between localized and distributed processing, and how inter-regional interactions give rise to coordinated function. By integrating perspectives across species, recording techniques, and computational frameworks, the workshop aims to synthesize current advances and identify general principles governing distributed neural computation.<\/p>\n<\/div><\/div><\/div><\/section>\n<section class='av_toggle_section av-44tal42-32d21285746b2e5f533acf0c22bd60d8' ><div role=\"tablist\" class=\"single_toggle\" data-tags=\"{All} \"  ><p id='toggle-toggle-id-4' data-fake-id='#toggle-id-4' class='toggler  av-title-above '  role='tab' tabindex='0' aria-controls='toggle-id-4' data-slide-speed=\"200\" data-title=\"Schedule: Monday, Sept 28\" data-title-open=\"\" data-aria_collapsed=\"Click to expand: Schedule: Monday, Sept 28\" data-aria_expanded=\"Click to collapse: Schedule: Monday, Sept 28\">Schedule: Monday, Sept 28<span class=\"toggle_icon\"><span class=\"vert_icon\"><\/span><span class=\"hor_icon\"><\/span><\/span><\/p><div id='toggle-id-4' aria-labelledby='toggle-toggle-id-4' role='region' class='toggle_wrap  av-title-above'  ><div class='toggle_content invers-color ' ><table width=\"959\">\n<tbody>\n<tr>\n<td width=\"94\"><strong>14:00<\/strong><\/td>\n<td width=\"425\">Douglas Feitosa Tom\u00e9<br \/>\n(Institute of Science and Technology Austria (ISTA), Austria)<\/td>\n<td width=\"441\"><em>Introduction<\/em><\/td>\n<\/tr>\n<tr>\n<td width=\"94\"><strong>14:05<\/strong><\/td>\n<td width=\"425\">Tatiana Engel<br \/>\n(Princeton University, USA)<\/td>\n<td width=\"441\"><em>Brain-wide organization of intrinsic timescales at single-neuron resolution<\/em><\/td>\n<\/tr>\n<tr>\n<td width=\"94\"><strong>14:30<\/strong><\/td>\n<td width=\"425\">Charles Findling<br \/>\n(University of Geneva, Switzerland)<\/td>\n<td width=\"441\"><em>Brain-wide representations of prior information in mouse decision-making<\/em><\/td>\n<\/tr>\n<tr>\n<td width=\"94\"><strong>14:55<\/strong><\/td>\n<td width=\"425\">Denis Alevi<br \/>\n(Technische Universit\u00e4t Berlin, Germany)<\/td>\n<td width=\"441\"><em>Memory consolidation and brain-wide representational drift<\/em><\/td>\n<\/tr>\n<tr>\n<td width=\"94\"><strong>15:20<\/strong><\/td>\n<td width=\"425\">Gabriel Kreiman<br \/>\n(Harvard Medical School, USA)<\/td>\n<td width=\"441\"><em>What AI wants to be when it grows up: a brain<\/em><\/td>\n<\/tr>\n<tr>\n<td width=\"94\"><strong>15:45<\/strong><\/td>\n<td colspan=\"2\" width=\"865\"><em>Discussion with all speakers<\/em><\/td>\n<\/tr>\n<tr>\n<td width=\"94\"><strong>16:00<\/strong><\/td>\n<td colspan=\"2\" width=\"865\"><em>\u00a0Coffee break<\/em><\/td>\n<\/tr>\n<tr>\n<td width=\"94\"><strong>16:30<\/strong><\/td>\n<td width=\"425\">Adrienne Fairhall<br \/>\n(University of Washington, USA)<\/td>\n<td width=\"441\"><em>Distributed neural representations of beliefs mediate probabilistic inference<\/em><\/td>\n<\/tr>\n<tr>\n<td width=\"94\"><strong>16:55<\/strong><\/td>\n<td width=\"425\">Douglas Feitosa Tom\u00e9<br \/>\n(Institute of Science and Technology Austria (ISTA), Austria)<\/td>\n<td width=\"441\"><em>Distributed engrams enable parallelized orthogonal computations within and across brain regions<\/em><\/td>\n<\/tr>\n<tr>\n<td width=\"94\"><strong>17:20<\/strong><\/td>\n<td width=\"425\">Jennifer Li<br \/>\n(Max Planck Institute for Biological Cybernetics, Germany)<\/td>\n<td width=\"441\"><em>Brain-wide joint analysis of neuromodulatory and cognitive networks during spatial learning in freely swimming zebrafish<\/em><\/td>\n<\/tr>\n<tr>\n<td width=\"94\"><strong>17:45<\/strong><\/td>\n<td width=\"425\">Claudia Clopath<br \/>\n(Imperial College London, UK)<\/td>\n<td width=\"441\"><em>Why motor learning involves multiple systems: an algorithmic perspective<\/em><\/td>\n<\/tr>\n<tr>\n<td width=\"94\"><strong>18:10<\/strong><\/td>\n<td colspan=\"2\" width=\"865\"><em>Discussion with all speakers<\/em><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div><\/div><\/div><\/section>\n<\/div><\/p><\/div><div  class='flex_column av-42304uq-7a87f691d8fcfa4893eae62bde5a4989 av_one_full  avia-builder-el-23  el_after_av_one_full  el_before_av_one_full  first flex_column_div  column-top-margin'     ><p><section  class='av_textblock_section av-40d8mk2-5ec3822184c7fad1d347622a690589f0 '  ><div class='avia_textblock' ><p><span style=\"color: #000000;\"><strong>Neuromodulatory computations across the lifespan<\/strong><em><br \/>\nOrganizers:<\/em><br \/>\n<\/span><span style=\"color: #000000;\">Srikanth Ramaswamy | Newcastle University, UK<br \/>\nTrang-Anh Nghiem | Hertie Institute of AI in Brain Health, Germany<\/span><em><br \/>\n<\/em><\/p>\n<\/div><\/section><br \/>\n<div  class='togglecontainer av-3z7mn36-1efed2ecf58398bbd67d44cba9ac1ef5  avia-builder-el-25  el_after_av_textblock  avia-builder-el-last  toggle_close_all' >\n<section class='av_toggle_section av-3wwfyg2-d50f30b98c4026e3e2693e3d96a53a6b' ><div role=\"tablist\" class=\"single_toggle\" data-tags=\"{All} \"  ><p id='toggle-toggle-id-5' data-fake-id='#toggle-id-5' class='toggler  av-title-above '  role='tab' tabindex='0' aria-controls='toggle-id-5' data-slide-speed=\"200\" data-title=\"Description\" data-title-open=\"\" data-aria_collapsed=\"Click to expand: Description\" data-aria_expanded=\"Click to collapse: Description\">Description<span class=\"toggle_icon\"><span class=\"vert_icon\"><\/span><span class=\"hor_icon\"><\/span><\/span><\/p><div id='toggle-id-5' aria-labelledby='toggle-toggle-id-5' role='region' class='toggle_wrap  av-title-above'  ><div class='toggle_content invers-color ' ><p>Neuromodulation plays a key role in shaping neural computations and enabling adaptable learning and behavior throughout the lifetime. Accordingly, disrupted neuromodulation is hypothesized to underlie pathological development and aging: pharmacological interventions targeting neuromodulators such as dopamine, acetylcholine, and serotonin have been widely leveraged in neurodevelopmental disorders like attention deficit hyperactivity disorder and schizophrenia, as well as neurodegenerative diseases like Parkinson\u2019s and Alzheimer\u2019s. Yet, the mechanisms by which neuromodulators control cellular and synaptic mechanisms, influence neural dynamics, and modulate encoding and learning across the lifespan remains largely unknown. In this workshop, we aim to discuss how neuromodulatory systems, their associated neural representations, and learning rules change during development, aging, and related disorders. To this end, we will bring together theoretical and experimental experts on neuromodulation, neural computations, as well as neurodevelopment, aging, and their relevant pathologies. Our workshop will foster collaborations and generate actionable research questions for future multi-scale, lifetime-spanning studies. This is timely given the urgency posed by aging populations, rising neurodevelopmental diagnoses, and advances in neuroAI architectures leveraging neuromodulation-like mechanisms. By addressing mechanistic links between development, aging, and pathology, the workshop will catalyse innovations in diagnosis, therapeutic strategies, and machine learning systems. Ultimately, participants will identify critical knowledge gaps, develop interdisciplinary approaches, and lay the groundwork for transformative discoveries in brain health and adaptive intelligence across the human lifespan.<\/p>\n<\/div><\/div><\/div><\/section>\n<section class='av_toggle_section av-3wcld4y-5cb4c1caa28389fe31630263c5d6089d' ><div role=\"tablist\" class=\"single_toggle\" data-tags=\"{All} \"  ><p id='toggle-toggle-id-6' data-fake-id='#toggle-id-6' class='toggler  av-title-above '  role='tab' tabindex='0' aria-controls='toggle-id-6' data-slide-speed=\"200\" data-title=\"Schedule: Monday, Sept 28\" data-title-open=\"\" data-aria_collapsed=\"Click to expand: Schedule: Monday, Sept 28\" data-aria_expanded=\"Click to collapse: Schedule: Monday, Sept 28\">Schedule: Monday, Sept 28<span class=\"toggle_icon\"><span class=\"vert_icon\"><\/span><span class=\"hor_icon\"><\/span><\/span><\/p><div id='toggle-id-6' aria-labelledby='toggle-toggle-id-6' role='region' class='toggle_wrap  av-title-above'  ><div class='toggle_content invers-color ' ><table width=\"959\">\n<tbody>\n<tr>\n<td width=\"94\"><strong>14:00<\/strong><\/td>\n<td width=\"425\">Shervin Safavi<br \/>\n(TUD Dresden University of Technology, Germany)<\/td>\n<td width=\"441\"><em>Criticality, neuromodulatory state, and the computational machinery of perceptual decision-making<\/em><\/td>\n<\/tr>\n<tr>\n<td width=\"94\"><strong>14:40<\/strong><\/td>\n<td width=\"425\">Julia Costacurta<br \/>\n(Stanford University, USA)<\/td>\n<td width=\"441\"><em>Statistical signatures of neuromodulatory state in neural population dynamics<\/em><\/td>\n<\/tr>\n<tr>\n<td width=\"94\"><strong>15:10<\/strong><\/td>\n<td width=\"425\">Suraj Honnuraiah<br \/>\n(Harvard Medical School, USA)<\/td>\n<td width=\"441\"><em>Dendritic computation and neuromodulatory gain control: From neural circuits to neuromorphic architectures<\/em><\/td>\n<\/tr>\n<tr>\n<td width=\"94\"><strong>16:00<\/strong><\/td>\n<td colspan=\"2\" width=\"865\"><em>\u00a0Coffee break<\/em><\/td>\n<\/tr>\n<tr>\n<td width=\"94\"><strong>16:30<\/strong><\/td>\n<td width=\"425\">Robert Froemke<br \/>\n(New York University, USA)<\/td>\n<td width=\"441\"><em>Oxytocin as a neuromodulatory gatekeeper: Synaptic plasticity, social learning, and lifespan implications<\/em><\/td>\n<\/tr>\n<tr>\n<td width=\"94\"><strong>17:15<\/strong><\/td>\n<td width=\"425\">Fani Koulkouli<br \/>\n(Institut national de la sant\u00e9 et de la recherche m\u00e9dicale (INSERM), France)<\/td>\n<td width=\"441\"><em>Nicotinic acetylcholine receptors, cortical interneurons, and the breakdown of circuit balance in disease<\/em><\/td>\n<\/tr>\n<tr>\n<td width=\"94\"><strong>18:00<\/strong><\/td>\n<td width=\"425\">Ylermi Cabrera Leon<br \/>\n(Universidad de Las Palmas de Gran Canaria, Spain)<\/td>\n<td width=\"441\"><em>Sleep, spontaneous dynamics, and the neuromodulatory regulation of memory consolidation<\/em><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div><\/div><\/div><\/section>\n<\/div><\/p><\/div><div  class='flex_column av-dr22s2-c6ec1cb1b1cf8b33ad3b6a8d8ae7f3e1 av_one_full  avia-builder-el-26  el_after_av_one_full  el_before_av_hr  first flex_column_div  column-top-margin'     ><p><section  class='av_textblock_section av-3t456xe-85de72a6c676f82e65c4c726929ea865 '  ><div class='avia_textblock' ><p><span style=\"color: #000000;\"><strong>Towards a quantitative approach to behavior<\/strong><\/span><br \/>\n<span style=\"color: #000000;\"> <em>Organizers:<\/em><\/span><br \/>\n<span style=\"color: #000000;\"> Pietro Verzelli | Bonn University Hospital, Germany<\/span><br \/>\n<span style=\"color: #000000;\"> Jens Tillmann | Deutsches Zentrum f\u00fcr Neurodegenerative Erkrankungen (DZNE), Germany<em><br \/>\n<\/em><\/span><\/p>\n<\/div><\/section><br \/>\n<div  class='togglecontainer av-3quhlb6-c48d0d38219d823ade3627bc5b46d060  avia-builder-el-28  el_after_av_textblock  avia-builder-el-last  toggle_close_all' >\n<section class='av_toggle_section av-3pex8lu-de91abcec6ca39575537dc202f4c75b3' ><div role=\"tablist\" class=\"single_toggle\" data-tags=\"{All} \"  ><p id='toggle-toggle-id-7' data-fake-id='#toggle-id-7' class='toggler  av-title-above '  role='tab' tabindex='0' aria-controls='toggle-id-7' data-slide-speed=\"200\" data-title=\"Description\" data-title-open=\"\" data-aria_collapsed=\"Click to expand: Description\" data-aria_expanded=\"Click to collapse: Description\">Description<span class=\"toggle_icon\"><span class=\"vert_icon\"><\/span><span class=\"hor_icon\"><\/span><\/span><\/p><div id='toggle-id-7' aria-labelledby='toggle-toggle-id-7' role='region' class='toggle_wrap  av-title-above'  ><div class='toggle_content invers-color ' ><p>Behavior is the primary output of neural systems and a central object of study in neuroscience. Recent advances in experimental techniques and machine learning have made it possible to collect large-scale, high-resolution behavioral data across species and contexts. There is broad agreement that understanding behavior is essential for linking neural activity to function. Despite this abundance of data, there is still no shared framework for how to represent, structure, or interpret behavior. Moreover, at a conceptual level, even the question of what should count as behavior remains open: if one were to gather researchers and ask \u201cwhat is behavior?\u201d, one would likely obtain a wide range of answers. This is precisely what this workshop is about. We bring together researchers working on different model organisms and using complementary approaches\u2014experimental, computational, and theoretical\u2014to address how structure can be identified and formalized in behavior. Topics will include low-dimensional representations, data-driven embeddings, and the integration of behavioral and neural measurements. The workshop will place particular emphasis on comparing perspectives: what different communities consider as behavior, how they represent it, and what can be learned by confronting these viewpoints. By doing so, the goal is not only to provide a clearer picture of the current landscape, but also to take a step towards a more unified and principled quantitative science of behavior.<\/p>\n<\/div><\/div><\/div><\/section>\n<section class='av_toggle_section av-3nf87xu-a1e95f9acda3454c3b85426ec4f2091f' ><div role=\"tablist\" class=\"single_toggle\" data-tags=\"{All} \"  ><p id='toggle-toggle-id-8' data-fake-id='#toggle-id-8' class='toggler  av-title-above '  role='tab' tabindex='0' aria-controls='toggle-id-8' data-slide-speed=\"200\" data-title=\"Schedule: Monday, Sept 28\" data-title-open=\"\" data-aria_collapsed=\"Click to expand: Schedule: Monday, Sept 28\" data-aria_expanded=\"Click to collapse: Schedule: Monday, Sept 28\">Schedule: Monday, Sept 28<span class=\"toggle_icon\"><span class=\"vert_icon\"><\/span><span class=\"hor_icon\"><\/span><\/span><\/p><div id='toggle-id-8' aria-labelledby='toggle-toggle-id-8' role='region' class='toggle_wrap  av-title-above'  ><div class='toggle_content invers-color ' ><table width=\"959\">\n<tbody>\n<tr>\n<td width=\"94\"><strong>14:00<\/strong><\/td>\n<td width=\"425\">Pietro Verzelli<br \/>\n(Bonn University Hospital, Germany)<\/td>\n<td width=\"441\"><em>Introduction: What do we talk about when we talk about behavior? + Behavioral biomarkers of epilepsy<\/em><\/td>\n<\/tr>\n<tr>\n<td width=\"94\"><strong>14:30<\/strong><\/td>\n<td width=\"425\">Ahmed Al-Hady<br \/>\n(Max Planck Institute of Animal Behavior, Germany)<\/td>\n<td width=\"441\"><em>Social behavioral analytics for natural neuroscience<\/em><\/td>\n<\/tr>\n<tr>\n<td width=\"94\"><strong>15:00<\/strong><\/td>\n<td width=\"425\">Graziana Gatto<br \/>\n(University Hospital Cologne, Germany)<\/td>\n<td width=\"441\"><em>Behavior in motion: Standardizing how we measure action<\/em><\/td>\n<\/tr>\n<tr>\n<td width=\"94\"><strong>15:30<\/strong><\/td>\n<td width=\"425\">Fabio Naecth<br \/>\n(University of Vienna, Austria)<\/td>\n<td width=\"441\"><em>From high-resolution behavior quantification to predictive optogenetic virtual realities in <\/em>C. elegans<\/td>\n<\/tr>\n<tr>\n<td width=\"94\"><strong>16:00<\/strong><\/td>\n<td colspan=\"2\" width=\"865\"><em>\u00a0Coffee break<\/em><\/td>\n<\/tr>\n<tr>\n<td width=\"94\"><strong>16:30<\/strong><\/td>\n<td width=\"425\">Dominik Bach<br \/>\n(University of Bonn, Germany)<\/td>\n<td width=\"441\"><em>The grammar of human behaviour in a biological environment<\/em><\/td>\n<\/tr>\n<tr>\n<td width=\"94\"><strong>17:00<\/strong><\/td>\n<td width=\"425\">Mostafa Safaie<br \/>\n(Imperial College, UK)<\/td>\n<td width=\"441\"><em>Control of striatal activity reflects different circuit constraints compared to M1<\/em><\/td>\n<\/tr>\n<tr>\n<td width=\"94\"><strong>17:30<\/strong><\/td>\n<td width=\"425\">Jens Tillmann<br \/>\n(Deutsches Zentrum f\u00fcr Neurodegenerative Erkrankungen (DZNE), Germany)<\/td>\n<td width=\"441\"><em>Bridging the gap with neuro-behavioral models and large-scale datasets<\/em><\/td>\n<\/tr>\n<tr>\n<td width=\"94\"><strong>18:00<\/strong><\/td>\n<td width=\"425\">Closing discussion<\/td>\n<td width=\"441\"><em>Towards a quantitative science of behavior<\/em><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div><\/div><\/div><\/section>\n<\/div><\/p><\/div><div  class='hr av-3mk0oeq-3adef5539be4d864fe1a26a6e8b26f6a hr-default  avia-builder-el-29  el_after_av_one_full  el_before_av_one_full '><span class='hr-inner '><span class=\"hr-inner-style\"><\/span><\/span><\/div>\n<div  class='flex_column av-3k8dx2a-c2e6f18d04396f151c8e7a7064898874 av_one_full  avia-builder-el-30  el_after_av_hr  el_before_av_one_full  first flex_column_div  '     ><p>\n<style type=\"text\/css\" data-created_by=\"avia_inline_auto\" id=\"style-css-av-3iga6cy-166567a66e3e8cc28683e2d98ee75614\">\n#top .hr.av-3iga6cy-166567a66e3e8cc28683e2d98ee75614{\nmargin-top:0px;\nmargin-bottom:30px;\n}\n.hr.av-3iga6cy-166567a66e3e8cc28683e2d98ee75614 .hr-inner{\nwidth:60px;\nborder-color:#004c93;\n}\n<\/style>\n<div  class='hr av-3iga6cy-166567a66e3e8cc28683e2d98ee75614 hr-custom  avia-builder-el-31  el_before_av_textblock  avia-builder-el-first  hr-left hr-icon-no'><span class='hr-inner inner-border-av-border-fat'><span class=\"hr-inner-style\"><\/span><\/span><\/div><br \/>\n<section  class='av_textblock_section av-3gkd9du-431118a1930981124ba6efb887871176 '  ><div class='avia_textblock' ><h3>Half-day workshops: Tuesday, Sept 29<\/h3>\n<\/div><\/section><\/p><\/div><div  class='flex_column av-3ezlski-40a2b03c89de10c0f6bbc46a145865d6 av_one_full  avia-builder-el-33  el_after_av_one_full  el_before_av_one_full  first flex_column_div  column-top-margin'     ><p><section  class='av_textblock_section av-3dszdde-b9869697f544219104e7a39a81d49c4a '  ><div class='avia_textblock' ><p><span style=\"color: #000000;\"><strong>Controversies on the nature of spatial coding in the hippocampal formation across species<\/strong><em><br \/>\nOrganizers:<\/em><br \/>\n<\/span><span style=\"color: #000000;\">Andrej Bicanski | Max Planck Institute for Human Cognitive and Brain Sciences, Germany<br \/>\nJosh Jacobs | University of Chicago, USA<br \/>\nRichard Kempter | Humboldt-Universit\u00e4t zu Berlin, Germany<br \/>\nLukas Kunz | University of Bonn, Germany<\/span><em><br \/>\n<\/em><\/p>\n<\/div><\/section><br \/>\n<div  class='togglecontainer av-3brxwvm-212854e75b0327b50248097911bd74f4  avia-builder-el-35  el_after_av_textblock  avia-builder-el-last  toggle_close_all' >\n<section class='av_toggle_section av-3a5mi6a-7c1e6daff560d47c4f2d3b6f1e921f86' ><div role=\"tablist\" class=\"single_toggle\" data-tags=\"{All} \"  ><p id='toggle-toggle-id-9' data-fake-id='#toggle-id-9' class='toggler  av-title-above '  role='tab' tabindex='0' aria-controls='toggle-id-9' data-slide-speed=\"200\" data-title=\"Description\" data-title-open=\"\" data-aria_collapsed=\"Click to expand: Description\" data-aria_expanded=\"Click to collapse: Description\">Description<span class=\"toggle_icon\"><span class=\"vert_icon\"><\/span><span class=\"hor_icon\"><\/span><\/span><\/p><div id='toggle-id-9' aria-labelledby='toggle-toggle-id-9' role='region' class='toggle_wrap  av-title-above'  ><div class='toggle_content invers-color ' ><p>The hippocampal formation is home to some of the most intriguing neural codes in systems neuroscience. Low-dimensional spatial representation in the form of place cells, grid cells, head direction cells, and vectorial codes [1,2] are thought to underpin a neural basis for spatial navigation and memory [3,4]. Yet despite decades of intensive research, fundamental questions about the nature of these representations remain unresolved, and in some cases, actively contested. This workshop brings together leading and emerging scientists to interrogate, debate, and stress-test prevailing frameworks in the field. Rather than presenting a consensus view, we embrace productive disagreement as a scientific tool. Our format is deliberately confrontational in the best sense: pairs of researchers will stake out and defend positions (ideally opposing ones) in brief position statements. This will be followed by open, discussion-led sessions in which the audience is an active participant. Brevity in presentation is the goal, in order to maximize time for genuine scientific discussion. A central tension animating the workshop is the apparent divergence in spatial coding across species [5]. Rodent hippocampal place cells have long served as the canonical model of allocentric spatial representation, yet findings in non-human primates reveal a rather different picture [6], with neurons encoding spatial views rather than locations per se. Human place cells [7] seem much rarer than in the rodent. However, the workshop will not accept this as a clean separation. Instead the participants will ask if this apparent dichotomy reflects fundamental differences in the computational principles between the brains of different species, behavioral differences, or potentially experimental idiosyncrasies. Similarly, the discovery of grid cells transformed our understanding of spatial computation, offering what seems like an elegant solution to path integration through a metric, Euclidean representation of space [8]. Yet this canonical view is now under pressure from multiple directions [9]. Newer findings show that subsets of grid cells can encode future rather than current positions [10], grids deform in some environments [11], and appear to re-anchor mid-task [12] in certain experiments (i.e., they appear to operate in multiple reference frames). Theoretical accounts offer multiple responses, from attractor network models to eigenvector decompositions of environmental occupancy, each capturing some features of the data while struggling with others. These are two examples where productive debate and fresh ideas are needed. Overall the workshop will cover the following topics: Place cells in humans versus rodents: are we studying the same phenomenon? Grid cells and path integration: how tight is the link, and what breaks it? Can classical theories of grid cells as metric representations survive new challenges \u2014 including future-position coding, multiple reference frames, distortions of grid geometry, and eigenvector accounts of grid cell activity? Spatial view cells in monkeys: a fundamentally different solution, or a variation on a theme? The nature of representational drift in the hippocampal system: noise, reorganisation, or something more principled? Is the hippocampal system fundamentally spatial (and innate) or are place cells and related representations learned? Is human navigation in complex environments supported by the same hippocampal place cell map seen in rodents, or are there new computational principles in the human brain?<\/p>\n<p><span style=\"font-size: 10pt;\"><em>References<\/em>:<\/span><\/p>\n<p><span style=\"font-size: 10pt;\">[1] Moser, E. I., Kropff, E., &amp; Moser, M. B. (2008). Place cells, grid cells, and the brain&#8217;s spatial representation system. Annu. Rev. Neurosci., 31(1), 69-89.<\/span><br \/>\n<span style=\"font-size: 10pt;\"> [2] Bicanski, A., &amp; Burgess, N. (2020). Neuronal vector coding in spatial cognition. Nature Reviews Neuroscience, 21(9), 453-470.<\/span><br \/>\n<span style=\"font-size: 10pt;\"> [3] McNaughton, B. L., Battaglia, F. P., Jensen, O., Moser, E. I., &amp; Moser, M. B. (2006). Path integration and the neural basis of the&#8217;cognitive map&#8217;. Nature Reviews Neuroscience, 7(8), 663-678. [4] Eichenbaum, H., &amp; Cohen, N. J. (2014). Can we reconcile the declarative memory and spatial navigation views on hippocampal function?. Neuron, 83(4), 764-770.<\/span><br \/>\n<span style=\"font-size: 10pt;\"> [5] Olafsdottir, F., Epstein, R., Bicanski, A., Jacobs, J., Kunz, L., Donato, F., &#8230; &amp; Newcombe, N. S. (2026, January). Integrating across levels-from cells and circuits to brains and behavior. In Ernst Str\u00fcngmann Forum 2024 on Navigation. [6] Rolls, E. T. (1999). Spatial view cells and the representation of place in the primate hippocampus. Hippocampus, 9(4), 467-480.<\/span><br \/>\n<span style=\"font-size: 10pt;\"> [7] Mao, D. (2023). Neural correlates of spatial navigation in primate hippocampus. Neuroscience Bulletin, 39(2), 315-327.<\/span><br \/>\n<span style=\"font-size: 10pt;\"> [8] Bush, D., Barry, C., Manson, D., &amp; Burgess, N. (2015). Using grid cells for navigation. Neuron, 87(3), 507-520.<\/span><br \/>\n<span style=\"font-size: 10pt;\"> [9] Ginosar, G., Aljadeff, J., Las, L., Derdikman, D., &amp; Ulanovsky, N. (2023). Are grid cells used for navigation? On local metrics, subjective spaces, and black holes. Neuron, 111(12), 1858-1875.<\/span><br \/>\n<span style=\"font-size: 10pt;\"> [10] Ouchi, A. (2026). Predictive grid cells: Future spatial representations in the hippocampal-entorhinal circuit. Neuroscience Research, 105053.<\/span><br \/>\n<span style=\"font-size: 10pt;\"> [11] Krupic, J., Bauza, M., Burton, S., Barry, C., &amp; O\u2019Keefe, J. (2015). Grid cell symmetry is shaped by environmental geometry. Nature, 518(7538), 232-235.<\/span><br \/>\n<span style=\"font-size: 10pt;\"> [12] Peng, J.-J., Throm, B., Najafian Jazi, M., Yen, T.-Y., Pizzarelli, R., Monyer, H., &amp; Allen, K. (2025). Grid cells accurately track movement during path integration-based navigation despite switching reference frames. Nature Neuroscience.<\/span><\/p>\n<\/div><\/div><\/div><\/section>\n<section class='av_toggle_section av-385up76-bbe88dc5a08d1a823909d4f3c7918c39' ><div role=\"tablist\" class=\"single_toggle\" data-tags=\"{All} \"  ><p id='toggle-toggle-id-10' data-fake-id='#toggle-id-10' class='toggler  av-title-above '  role='tab' tabindex='0' aria-controls='toggle-id-10' data-slide-speed=\"200\" data-title=\"Schedule: Tuesday, Sept 29\" data-title-open=\"\" data-aria_collapsed=\"Click to expand: Schedule: Tuesday, Sept 29\" data-aria_expanded=\"Click to collapse: Schedule: Tuesday, Sept 29\">Schedule: Tuesday, Sept 29<span class=\"toggle_icon\"><span class=\"vert_icon\"><\/span><span class=\"hor_icon\"><\/span><\/span><\/p><div id='toggle-id-10' aria-labelledby='toggle-toggle-id-10' role='region' class='toggle_wrap  av-title-above'  ><div class='toggle_content invers-color ' ><table width=\"959\">\n<tbody>\n<tr>\n<td width=\"94\"><strong>8:30<\/strong><\/td>\n<td width=\"425\">Josh Jacobs<br \/>\n(University of Chicago, USA)<\/td>\n<td width=\"441\"><em>Introduction and neural coding in the human hippocampus during navigation <\/em><\/td>\n<\/tr>\n<tr>\n<td width=\"94\"><strong>8:50<\/strong><\/td>\n<td width=\"425\">Shachar Maidenbaum<br \/>\n(Ben-Gurion University of the Negev, Israel)<\/td>\n<td width=\"441\"><em>Spatial representations for memory and navigation across the reality spectrum<\/em><\/td>\n<\/tr>\n<tr>\n<td width=\"94\"><strong>9:10<\/strong><\/td>\n<td width=\"425\">Open discussion<\/td>\n<td width=\"441\"><em>Discussion of controversy 1<\/em><\/td>\n<\/tr>\n<tr>\n<td width=\"94\"><strong>9:30<\/strong><\/td>\n<td width=\"425\">Lukas Kunz<br \/>\n(University of Bonn, Germany)<\/td>\n<td width=\"441\"><em>On spatial view cells in humans<\/em><\/td>\n<\/tr>\n<tr>\n<td width=\"94\"><strong>9:40<\/strong><\/td>\n<td width=\"425\">Richard Kempter<br \/>\n(Humboldt-Universit\u00e4t zu Berlin, Germany)<\/td>\n<td width=\"441\"><em>TBA<\/em><\/td>\n<\/tr>\n<tr>\n<td width=\"94\"><strong>9:50<\/strong><\/td>\n<td width=\"425\">Open discussion<\/td>\n<td width=\"441\"><em>Discussion of controversy 2<\/em><\/td>\n<\/tr>\n<tr>\n<td width=\"94\"><strong>10:00<\/strong><\/td>\n<td colspan=\"2\" width=\"865\"><em>\u00a0Coffee break<\/em><\/td>\n<\/tr>\n<tr>\n<td width=\"94\"><strong>10:30<\/strong><\/td>\n<td width=\"425\">Andrej Bicanski<br \/>\n(Max Planck Institute for Human Cognitive and Brain Sciences, Germany)<\/td>\n<td width=\"441\"><em>Repurposing extant spatial navigation architectures across species and cognitive domains<\/em><\/td>\n<\/tr>\n<tr>\n<td width=\"94\"><strong>11:00<\/strong><\/td>\n<td width=\"425\">Natalie Schieferstein<br \/>\n(University of Bonn, Germany)<\/td>\n<td width=\"441\"><em>Representational drift and its relevance for spatial coding<\/em><\/td>\n<\/tr>\n<tr>\n<td width=\"94\"><strong>11:50<\/strong><\/td>\n<td width=\"425\">Open discussion<\/td>\n<td width=\"441\"><em>Discussion of controversy 3<\/em><\/td>\n<\/tr>\n<tr>\n<td width=\"94\"><strong>12:00<\/strong><\/td>\n<td width=\"425\">Dun Mao<br \/>\n(Chinese Academy of Sciences, China)<\/td>\n<td width=\"441\"><em>Alternative spatial representations in monkeys<\/em><\/td>\n<\/tr>\n<tr>\n<td width=\"94\"><strong>12:30<\/strong><\/td>\n<td width=\"425\">Gily Ginosar<br \/>\n(New York University, USA)<\/td>\n<td width=\"441\"><em>Neurobiology of natural behaviors: from bat entorhinal coding to gerbial social vocalizations<\/em><\/td>\n<\/tr>\n<tr>\n<td width=\"94\"><strong>13:00<\/strong><\/td>\n<td width=\"425\">Sang Ah Lee<br \/>\n(Seoul National University, South Korea)<\/td>\n<td width=\"441\"><em>Neural mechanisms of spatiotemporal binding in navigation and memory<\/em><\/td>\n<\/tr>\n<tr>\n<td width=\"94\"><strong>13:30<\/strong><\/td>\n<td width=\"425\">Open discussion<\/td>\n<td width=\"441\"><em>Discussion of controversy 4<\/em><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div><\/div><\/div><\/section>\n<\/div><\/p><\/div><div  class='flex_column av-36vzogy-658aebc137fd163b61d9c57b963921c8 av_one_full  avia-builder-el-36  el_after_av_one_full  el_before_av_one_full  first flex_column_div  column-top-margin'     ><p><section  class='av_textblock_section av-14lhaq-89490762d7842676296f1c00f572430e '  ><div class='avia_textblock' ><p><span style=\"color: #000000;\"><strong>Degeneracy in nervous system function<\/strong><em><br \/>\nOrganizers:<\/em><\/span><br \/>\n<span style=\"color: #000000;\"> Peter Jedlicka | Goethe University Frankfurt, Germany<\/span><br \/>\n<span style=\"color: #000000;\"> Luisa Ramirez | Johannes Gutenberg University Mainz, Germany<em><br \/>\n<\/em><\/span><\/p>\n<\/div><\/section><br \/>\n<div  class='togglecontainer av-335cxg2-d6939b3a13d6f4a000c65791032be982  avia-builder-el-38  el_after_av_textblock  avia-builder-el-last  toggle_close_all' >\n<section class='av_toggle_section av-31emp5e-60ce59783a8e463b9b263cee340e5e13' ><div role=\"tablist\" class=\"single_toggle\" data-tags=\"{All} \"  ><p id='toggle-toggle-id-11' data-fake-id='#toggle-id-11' class='toggler  av-title-above '  role='tab' tabindex='0' aria-controls='toggle-id-11' data-slide-speed=\"200\" data-title=\"Description\" data-title-open=\"\" data-aria_collapsed=\"Click to expand: Description\" data-aria_expanded=\"Click to collapse: Description\">Description<span class=\"toggle_icon\"><span class=\"vert_icon\"><\/span><span class=\"hor_icon\"><\/span><\/span><\/p><div id='toggle-id-11' aria-labelledby='toggle-toggle-id-11' role='region' class='toggle_wrap  av-title-above'  ><div class='toggle_content invers-color ' ><p>The brain integrates an astonishing amount of information to guide appropriate functional outcomes. To achieve this, while accounting for a wide range of internal and external conditions, neuronal function must be both reliable and versatile. Degeneracy \u2014 the ability of structurally different elements to produce equivalent functional outcomes \u2014 is emerging as a fundamental mechanism that enables this. Evidence for degeneracy exists at every level of nervous system organization: in the many ion channel combinations that yield the same neuronal firing properties, in the parallel circuit configurations that support identical computations, and in the distinct neuronal ensembles that encode the same stimulus. Yet despite this ubiquity, degeneracy has been called the &#8220;brain&#8217;s best kept secret&#8221; (Albantakis et al., 2024, J. Neurosci.), and remains understudied as an organizing principle of neural function. This workshop brings together experimental and computational neuroscientists who have directly demonstrated degeneracy in their systems, spanning multiple levels of organization \u2014 from ion channels to neural populations \u2014 and across invertebrate and vertebrate species. By combining empirical and theoretical perspectives, we aim to establish degeneracy as a unifying framework for understanding how the nervous system achieves both robustness and versatility, and to stimulate discussion on its mechanistic origins, functional consequences, and normative underpinnings. We will open the workshop at the level of single neurons. Dr. Stefanie Ryglewski (JGU Mainz) will present experimental evidence that heterogeneity in ion channel isoforms safeguards neuronal excitability in flight motoneurons, building on a rich framework of computational predictions. Her talk will start the discussion on how structural variability, far from being noise, is a feature that the nervous system exploits to ensure functional robustness. To continue this discussion from a theoretical angle, we will ask how heterogeneity at the cellular level shapes the dynamics and computational properties of entire networks. Dr. Richard Gast (The Scripps Research Institute, USA) will address this directly by presenting mean-field frameworks for neural populations with heterogeneous spiking properties and discussing how within-type variability controls the repertoire of computations a network can perform. Zooming into a concrete circuit, Dr. Juan Vargas (JGU Mainz) will then present striking experimental evidence of degeneracy in the Drosophila motion detection pathway. He will show that direction-selective responses can be recovered by partial circuit rescues, demonstrating that structurally distinct configurations can achieve the same computation. This talk will provide experimental evidence of circuit-level degeneracy, raising the question of whether such degeneracy is particular of the fly visual system, or a more general organizational principle. Prof. Fleur Zeldenrust (Donders Institute for Brain, Cognition and Behaviour, The\u00a0 Netherlands) will argue for the latter. From a theoretical perspective, she will show that heterogeneous recurrent networks can achieve equivalent and robust information transfer across a wide range of structural configurations. She will discuss the energy-information trade-offs that shape which degenerate solutions the nervous system selects \u2014 connecting the biophysical level back to normative principles. We will next shift to vertebrate systems, where degeneracy manifests at the level of neural population codes. Prof. Simon Rumpel (Johannes Gutenberg University, Mainz) will show that in the mouse auditory cortex, the same stable representational map can be realized by a large number of distinct neuronal configurations. Remarkably, this degeneracy is not merely a static property: it can be directly observed over time, as sound-evoked activity patterns drift across days in the same individual \u2014 a phenomenon known as representational drift \u2014 while perception remains unchanged. Building on this, Prof. Stephanie Palmer (University of Chicago, USA) will discuss the principles behind the different degenerate solutions in nervous systems. Drawing on her work on predictive coding in the retina and visual cortex, she will show that neural populations are optimized to predict their future inputs \u2014 and that this predictive optimality provides a powerful normative framework for understanding why certain degenerate configurations are favored over others. Prof. Elad Schneidman (Weizmann Institute of Science, Israel) will close the workshop by asking what degeneracy means from a principled, information-theoretic standpoint. Using maximum entropy models and tools from statistical physics, he will address what is preserved and what varies across degenerate population codes, and how the architecture of neural circuits \u2014 from local connectivity to connectome-level wiring \u2014 shapes the space of equivalent solutions available to the brain. Together, our symposium shows many intriguing examples of degeneracy in nervous system function. Across all talks, we will identify \u2014 both through experimental and theoretical approaches \u2014 how degeneracy serves as a \u2018built-in\u2019 mechanism that is immediately available at many levels in the nervous system to ensure reliable and versatile function. At the same time, we will discuss the limitations and trade-offs of such a system. With this, we aim to promote this understudied topic as a useful, if not necessary, framework for understanding nervous system function.<\/p>\n<\/div><\/div><\/div><\/section>\n<section class='av_toggle_section av-30zii3m-62cc6973646a697c72dea36e142812a6' ><div role=\"tablist\" class=\"single_toggle\" data-tags=\"{All} \"  ><p id='toggle-toggle-id-12' data-fake-id='#toggle-id-12' class='toggler  av-title-above '  role='tab' tabindex='0' aria-controls='toggle-id-12' data-slide-speed=\"200\" data-title=\"Schedule: Tuesday, Sept 29\" data-title-open=\"\" data-aria_collapsed=\"Click to expand: Schedule: Tuesday, Sept 29\" data-aria_expanded=\"Click to collapse: Schedule: Tuesday, Sept 29\">Schedule: Tuesday, Sept 29<span class=\"toggle_icon\"><span class=\"vert_icon\"><\/span><span class=\"hor_icon\"><\/span><\/span><\/p><div id='toggle-id-12' aria-labelledby='toggle-toggle-id-12' role='region' class='toggle_wrap  av-title-above'  ><div class='toggle_content invers-color ' ><table width=\"959\">\n<tbody>\n<tr>\n<td width=\"94\"><strong>8:30<\/strong><\/td>\n<td width=\"425\">Stefanie Ryglewski<br \/>\n(Johannes Gutenberg University, Germany)<\/td>\n<td width=\"441\"><em>Robustness through variability: ion channel isoform diversity safeguards neuronal excitability<\/em><\/td>\n<\/tr>\n<tr>\n<td width=\"94\"><strong>9:10<\/strong><\/td>\n<td width=\"425\">Richard Gast<br \/>\n(The Scripps Research Institute, USA)<\/td>\n<td width=\"441\"><em>TBA<\/em><\/td>\n<\/tr>\n<tr>\n<td width=\"94\"><strong>9:50<\/strong><\/td>\n<td width=\"425\">Juan Vargas<br \/>\n(Johannes Gutenberg University Mainz, Germany)<\/td>\n<td width=\"441\"><em>Coexisting implementations of direction selectivity in the <\/em>Drosophila<em> visual system<\/em><\/td>\n<\/tr>\n<tr>\n<td width=\"94\"><strong>10:30<\/strong><\/td>\n<td colspan=\"2\" width=\"865\"><em>\u00a0Coffee break<\/em><\/td>\n<\/tr>\n<tr>\n<td width=\"94\"><strong>11:00<\/strong><\/td>\n<td width=\"425\">Fleur Zeldenrust<br \/>\n(Donders Institute for Brain, Cognition, and Behaviour, The Netherlands)<\/td>\n<td width=\"441\"><em>Heterogeneity in the brain: Lessons from theory and experiment<\/em><em>\u2028<\/em><\/td>\n<\/tr>\n<tr>\n<td width=\"94\"><strong>11:30<\/strong><\/td>\n<td width=\"425\">Simon Rumpel<br \/>\n(Johannes Gutenberg University Mainz, Germany)<\/td>\n<td width=\"441\"><em>Degenerate coding of sensory stimuli in the neocortex<\/em><\/td>\n<\/tr>\n<tr>\n<td width=\"94\"><strong>12:00<\/strong><\/td>\n<td width=\"425\">Stephanie Palmer<br \/>\n(University of Chicago, USA)<\/td>\n<td width=\"441\"><em>TBA<\/em><\/td>\n<\/tr>\n<tr>\n<td width=\"94\"><strong>12:30<\/strong><\/td>\n<td width=\"425\">Elad Schneidman<br \/>\n(Weizmann Institute of Science, Israel)<\/td>\n<td width=\"441\"><em>TBA<\/em><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div><\/div><\/div><\/section>\n<\/div><\/p><\/div><div  class='flex_column av-2yjjhr6-12d1dfca6ba411d42d6522b0521e6363 av_one_full  avia-builder-el-39  el_after_av_one_full  el_before_av_one_full  first flex_column_div  column-top-margin'     ><p><section  class='av_textblock_section av-2wwzieq-40cc40f583023287c4e5051c1679edf1 '  ><div class='avia_textblock' ><p><span style=\"color: #000000;\"><strong>Exotic extracellular waveforms: can we separate non-somatic signals from artefacts?<\/strong><em><br \/>\nOrganizers:<\/em><\/span><br \/>\n<span style=\"color: #000000;\"> Paula Kuokkanen | Humboldt-Universit\u00e4t zu Berlin, Germany<\/span><br \/>\n<span style=\"color: #000000;\"> J\u00e9r\u00e9mie Sibille | Charit\u00e9 \u2013 Universit\u00e4tsmedizin Berlin, Germany<em><br \/>\n<\/em><\/span><\/p>\n<\/div><\/section><br \/>\n<div  class='togglecontainer av-2v8ydhe-17312ccedbdfaef098a6083cbf29e7d3  avia-builder-el-41  el_after_av_textblock  avia-builder-el-last  toggle_close_all' >\n<section class='av_toggle_section av-2t8vmzm-e1941d9a026131f1532089f5d6396cc2' ><div role=\"tablist\" class=\"single_toggle\" data-tags=\"{All} \"  ><p id='toggle-toggle-id-13' data-fake-id='#toggle-id-13' class='toggler  av-title-above '  role='tab' tabindex='0' aria-controls='toggle-id-13' data-slide-speed=\"200\" data-title=\"Description\" data-title-open=\"\" data-aria_collapsed=\"Click to expand: Description\" data-aria_expanded=\"Click to collapse: Description\">Description<span class=\"toggle_icon\"><span class=\"vert_icon\"><\/span><span class=\"hor_icon\"><\/span><\/span><\/p><div id='toggle-id-13' aria-labelledby='toggle-toggle-id-13' role='region' class='toggle_wrap  av-title-above'  ><div class='toggle_content invers-color ' ><p>The extracellular neuronal action potentials show a multitude of waveforms beyond the best-known somatic spikes. Detecting and sorting these shapes remains challenging in neuroscience, especially given the wealth of newer observations stemming from high-density silicon probes. Furthermore, a substantial fraction of the more exotic waveforms in electrophysiological recordings can be missed by current detection algorithms due to the uncertainty about their origin, rendering parts of the material unusable. Aiming for generalizable solutions, this workshop will approach this question of the origin of exotic extracellular waveforms by shedding new light based on putative contributions from dendrites, soma, and axons. As a matter of fact, biophysics has already predicted that each of these compartments can generate distinct extracellular waveforms, which do contribute to extracellular activity; their presence in the actual recorded spikes, by, e.g., Neuropixels, still remains a limit more than an improvement. The central aim of this workshop is to build a landscape of the expected waveform from these three compartments, given their known biophysical properties, toward more inclusive, and hopefully more precise, interpretations of in vivo electrophysiological data.<\/p>\n<\/div><\/div><\/div><\/section>\n<section class='av_toggle_section av-2sfk95u-0685cc5c05b1de6a50facd9a90b7655e' ><div role=\"tablist\" class=\"single_toggle\" data-tags=\"{All} \"  ><p id='toggle-toggle-id-14' data-fake-id='#toggle-id-14' class='toggler  av-title-above '  role='tab' tabindex='0' aria-controls='toggle-id-14' data-slide-speed=\"200\" data-title=\"Schedule: Tuesday, Sept 29\" data-title-open=\"\" data-aria_collapsed=\"Click to expand: Schedule: Tuesday, Sept 29\" data-aria_expanded=\"Click to collapse: Schedule: Tuesday, Sept 29\">Schedule: Tuesday, Sept 29<span class=\"toggle_icon\"><span class=\"vert_icon\"><\/span><span class=\"hor_icon\"><\/span><\/span><\/p><div id='toggle-id-14' aria-labelledby='toggle-toggle-id-14' role='region' class='toggle_wrap  av-title-above'  ><div class='toggle_content invers-color ' ><table width=\"959\">\n<tbody>\n<tr>\n<td width=\"94\"><span style=\"color: #000000;\"><strong>8:30<\/strong><\/span><\/td>\n<td width=\"425\"><span style=\"color: #000000;\">J\u00e9r\u00e9mie Sibille<\/span><br \/>\n<span style=\"color: #000000;\">(Charit\u00e9 \u2013 Universit\u00e4tsmedizin Berlin, Germany)<\/span><\/td>\n<td width=\"441\"><span style=\"color: #000000;\"><em>The bestiary of extracellular waveforms from different neuron compartments<\/em><\/span><\/td>\n<\/tr>\n<tr>\n<td width=\"94\"><span style=\"color: #000000;\"><strong>8:45<\/strong><\/span><\/td>\n<td width=\"425\"><span style=\"color: #000000;\">Gaute Einevoll<\/span><br \/>\n<span style=\"color: #000000;\">(University of Oslo\u00a0&amp;\u00a0Norwegian University of Life Sciences (NBMU), Norway)<\/span><\/td>\n<td width=\"441\"><span style=\"color: #000000;\"><em>Biophysics of extracellular waveforms and their summation<\/em><\/span><\/td>\n<\/tr>\n<tr>\n<td width=\"94\"><span style=\"color: #000000;\"><strong>9:15<\/strong><\/span><\/td>\n<td width=\"425\"><span style=\"color: #000000;\">Ian Christopher Tanoh<\/span><br \/>\n<span style=\"color: #000000;\">(Stanford University, USA)<\/span><\/td>\n<td width=\"441\"><span style=\"color: #000000;\"><em>Identifying multi-compartment Hodgkin-Huxley models with high-density extracellular voltage recordings<\/em><\/span><\/td>\n<\/tr>\n<tr>\n<td width=\"94\"><span style=\"color: #000000;\"><strong>9:35<\/strong><\/span><\/td>\n<td width=\"425\"><span style=\"color: #000000;\">Rishikesh Narayanan<\/span><br \/>\n<span style=\"color: #000000;\">(Indian Institute of Science, India)<\/span><\/td>\n<td width=\"441\"><span style=\"color: #000000;\"><em>Active dendritic and gap junctional contributions to extracellular field potentials<\/em><\/span><\/td>\n<\/tr>\n<tr>\n<td width=\"94\"><span style=\"color: #000000;\"><strong>10:00<\/strong><\/span><\/td>\n<td width=\"425\"><span style=\"color: #000000;\"><em>Coffee break<\/em><\/span><\/td>\n<td width=\"441\"><span style=\"color: #000000;\"><em>\u00a0<\/em><\/span><\/td>\n<\/tr>\n<tr>\n<td width=\"94\"><span style=\"color: #000000;\"><strong>10:30<\/strong><\/span><\/td>\n<td width=\"425\"><span style=\"color: #000000;\">Alexandra Tzilivaki<\/span><br \/>\n<span style=\"color: #000000;\">(Charit\u00e9 \u2013 Universit\u00e4tsmedizin Berlin, Germany)<\/span><\/td>\n<td width=\"441\"><span style=\"color: #000000;\"><em>From cellular mechanisms to network connectivity: GABAergic interneurons shape memory-related oscillations<\/em><\/span><\/td>\n<\/tr>\n<tr>\n<td width=\"94\"><span style=\"color: #000000;\"><strong>11:00<\/strong><\/span><\/td>\n<td width=\"425\"><span style=\"color: #000000;\">Angelique Paulck<\/span><br \/>\n<span style=\"color: #000000;\">(Harvard Medical School &amp; Massachusetts General Research Institute, USA)<\/span><\/td>\n<td width=\"441\"><span style=\"color: #000000;\"><em>Extracellular waveforms in human recordings<\/em><\/span><\/td>\n<\/tr>\n<tr>\n<td width=\"94\"><span style=\"color: #000000;\"><strong>11:30<\/strong><\/span><\/td>\n<td width=\"425\"><span style=\"color: #000000;\">Eran Stark<\/span><br \/>\n<span style=\"color: #000000;\">(University of Haifa, Israel)<\/span><\/td>\n<td width=\"441\"><span style=\"color: #000000;\"><em>Positive, biphasic, and triphasic extracellular waveforms correspond to return currents and non-somatic spikes<\/em><\/span><\/td>\n<\/tr>\n<tr>\n<td width=\"94\"><span style=\"color: #000000;\"><strong>12:10<\/strong><\/span><\/td>\n<td colspan=\"2\" width=\"865\"><span style=\"color: #000000;\"><em>\u00a0 Extended discussion<\/em><\/span><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div><\/div><\/div><\/section>\n<\/div><\/p><\/div><div  class='flex_column av-2qtdchu-b5f771bc7677cdb958fe3e110a45b469 av_one_full  avia-builder-el-42  el_after_av_one_full  el_before_av_one_full  first flex_column_div  column-top-margin'     ><p><section  class='av_textblock_section av-2pivtky-0a67447571403d01187b3792af4d6ff2 '  ><div class='avia_textblock' ><p><span style=\"color: #000000;\"><strong>Dendritic inhibition for efficient computation and learning<\/strong><\/span><br \/>\n<span style=\"color: #000000;\"> <em> Organizers:<\/em><\/span><br \/>\n<span style=\"color: #000000;\"> Lucas Rudelt | Max Planck Institute for Dynamics and Self-Organization, Germany<\/span><br \/>\n<span style=\"color: #000000;\"> Viola Priesemann | Max Planck Institute for Dynamics and Self-Organization, Germany<em><br \/>\n<\/em><\/span><\/p>\n<\/div><\/section><br \/>\n<div  class='togglecontainer av-9hagde-3f8d0332902dd4d91be9a9ae54bfd6fa  avia-builder-el-44  el_after_av_textblock  avia-builder-el-last  toggle_close_all' >\n<section class='av_toggle_section av-2lv88ky-01bf4dff34d111bd2a9a49b487379635' ><div role=\"tablist\" class=\"single_toggle\" data-tags=\"{All} \"  ><p id='toggle-toggle-id-15' data-fake-id='#toggle-id-15' class='toggler  av-title-above '  role='tab' tabindex='0' aria-controls='toggle-id-15' data-slide-speed=\"200\" data-title=\"Description\" data-title-open=\"\" data-aria_collapsed=\"Click to expand: Description\" data-aria_expanded=\"Click to collapse: Description\">Description<span class=\"toggle_icon\"><span class=\"vert_icon\"><\/span><span class=\"hor_icon\"><\/span><\/span><\/p><div id='toggle-id-15' aria-labelledby='toggle-toggle-id-15' role='region' class='toggle_wrap  av-title-above'  ><div class='toggle_content invers-color ' ><p>Experimental and theoretical work of the last decades established the balance of excitation and inhibition to be key to support neural computations. Traditionally, this has referred to a co-tuning of excitation and inhibition at the level of neurons or populations, with substantial impact on neural network dynamics. However, recent experimental evidence suggests a coordination of excitation and inhibition on an even finer scale: Inhibition can have a strong, localized effect on individual dendrites, inhibitory plasticity depends on local postsynaptic signaling, and vice versa, inhibition has a strong modulatory effect on the induction of local excitatory plasticity. However, in contrast to E-I balance at the neuronal level, our understanding of the functional implications of dendritic inhibition and heterosynaptic plasticity at the level of individual dendrites is still fragmented. In this workshop, we therefore bring together experimentalists and theoreticians to shed light on the role of a dendritic inhibition for neural computation and learning. In particular, we will discuss (i) mechanisms of inhibitory plasticity that can establish a local co-tuning between excitation and inhibition, (ii) the implications of dendritic inhibition on subcellular and network computations, and (iii) the role of inhibition for heterosynaptic plasticity and learning. By approaching the problem from these different angles, the workshop aims at providing a more coherent picture of the intricate interplay of excitation and inhibition at dendrites, and its importance for efficient neural computation and learning.<\/p>\n<\/div><\/div><\/div><\/section>\n<section class='av_toggle_section av-2kasrv6-a8140d2042902a834cb0f2c12c3ae625' ><div role=\"tablist\" class=\"single_toggle\" data-tags=\"{All} \"  ><p id='toggle-toggle-id-16' data-fake-id='#toggle-id-16' class='toggler  av-title-above '  role='tab' tabindex='0' aria-controls='toggle-id-16' data-slide-speed=\"200\" data-title=\"Schedule: Tuesday, Sept 29\" data-title-open=\"\" data-aria_collapsed=\"Click to expand: Schedule: Tuesday, Sept 29\" data-aria_expanded=\"Click to collapse: Schedule: Tuesday, Sept 29\">Schedule: Tuesday, Sept 29<span class=\"toggle_icon\"><span class=\"vert_icon\"><\/span><span class=\"hor_icon\"><\/span><\/span><\/p><div id='toggle-id-16' aria-labelledby='toggle-toggle-id-16' role='region' class='toggle_wrap  av-title-above'  ><div class='toggle_content invers-color ' ><table width=\"959\">\n<tbody>\n<tr>\n<td width=\"94\"><strong>8:30<\/strong><\/td>\n<td width=\"425\">Josef Bischofberger<br \/>\n(University of Basel, Switzerland)<\/td>\n<td width=\"441\"><em>Environmental enrichment improves\u00a0learning and\u00a0hippocampal sparse coding\u00a0via enhanced dendritic inhibition<\/em><\/td>\n<\/tr>\n<tr>\n<td width=\"94\"><strong>9:00<\/strong><\/td>\n<td width=\"425\">Corette J. Wierenga<br \/>\n(Radboud University, The Netherlands)<\/td>\n<td width=\"441\"><em>Dendritic coordination of excitatory and inhibitory synapses \u2013 a role for neuromodulation?<\/em><\/td>\n<\/tr>\n<tr>\n<td width=\"94\"><strong>9:30<\/strong><\/td>\n<td width=\"425\">Eleonora Pali<br \/>\n(Pavia University, Italy)<\/td>\n<td width=\"441\"><em>Dendritic processing drives spike-timing dependent plasticity (STDP) in cerebellar Golgi cells<\/em><\/td>\n<\/tr>\n<tr>\n<td width=\"94\"><strong>10:00<\/strong><\/td>\n<td colspan=\"2\" width=\"865\"><em>Coffee break<\/em><\/td>\n<\/tr>\n<tr>\n<td width=\"94\"><strong>10:30<\/strong><\/td>\n<td width=\"425\">Henning Sprekeler<br \/>\n(Technische Universit\u00e4t Berlin, Germany)<\/td>\n<td width=\"441\"><em>Optimizing interneuron circuits for compartment-specific feedback inhibition<\/em><\/td>\n<\/tr>\n<tr>\n<td width=\"94\"><strong>11:00<\/strong><\/td>\n<td width=\"425\">Everton Joao Agnes<br \/>\n(University of Basel, Switzerland)<\/td>\n<td width=\"441\"><em>Inhibitory plasticity for local control of excitatory learning<\/em><\/td>\n<\/tr>\n<tr>\n<td width=\"94\"><strong>11:30<\/strong><\/td>\n<td width=\"425\">Gaia Tavosanis<br \/>\n(RWTH Aachen University, Germany)<\/td>\n<td width=\"441\"><em>Modularity of inhibition in a cerebellum-like circuit<\/em><\/td>\n<\/tr>\n<tr>\n<td width=\"94\"><strong>12:00<\/strong><\/td>\n<td width=\"425\">Albert Gidon<br \/>\n(Humboldt Universit\u00e4t zu Berlin, Germany)<\/td>\n<td width=\"441\"><em>The right way to inhibit a neuron<\/em><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div><\/div><\/div><\/section>\n<\/div><\/p><\/div><div  class='flex_column av-2hieg4i-c93851c116acbc66e451964ab20cd684 av_one_full  avia-builder-el-45  el_after_av_one_full  el_before_av_one_full  first flex_column_div  column-top-margin'     ><p>\n<style type=\"text\/css\" data-created_by=\"avia_inline_auto\" id=\"style-css-av-2gle6ci-919ede92bbb5d074feb5e676655b32ec\">\n#top .hr.av-2gle6ci-919ede92bbb5d074feb5e676655b32ec{\nmargin-top:0px;\nmargin-bottom:30px;\n}\n.hr.av-2gle6ci-919ede92bbb5d074feb5e676655b32ec .hr-inner{\nwidth:60px;\nborder-color:#004c93;\n}\n<\/style>\n<div  class='hr av-2gle6ci-919ede92bbb5d074feb5e676655b32ec hr-custom  avia-builder-el-46  el_before_av_textblock  avia-builder-el-first  hr-left hr-icon-no'><span class='hr-inner inner-border-av-border-fat'><span class=\"hr-inner-style\"><\/span><\/span><\/div><br \/>\n<section  class='av_textblock_section av-2ffa6qa-cfc4b77536f9461d7d341350e8f4ba30 '  ><div class='avia_textblock' ><h3>Full-day workshops: Monday, Sept 28 &#8211; Tuesday, Sept 29<\/h3>\n<\/div><\/section><\/p><\/div><div  class='flex_column av-2dq86n6-3e9257aee1b5adbf9462501d68f505f8 av_one_full  avia-builder-el-48  el_after_av_one_full  el_before_av_one_full  first flex_column_div  column-top-margin'     ><p><section  class='av_textblock_section av-2bvauj6-04ddce93b63a2a1a6d465ee899976e6b '  ><div class='avia_textblock' ><p><span style=\"color: #000000;\"><strong>&#8220;1, 2, 3, 4, 5, Once I Caught a Fish Alive&#8221;: Sequence learning in recurrent neural networks<\/strong><em><br \/>\nOrganizers:<\/em><br \/>\n<\/span><span style=\"color: #000000;\">Andrey Formozov | Heidelberg University, Germany<br \/>\nMihai A. Petrovici | University of Bern, Switzerland<br \/>\n<\/span><span style=\"color: #000000;\">Martin Both | University of Heidelberg, Germany<\/span><\/p>\n<\/div><\/section><br \/>\n<div  class='togglecontainer av-2984hrm-333e9f0d63ab7b0df0e3ad0ff3690642  avia-builder-el-50  el_after_av_textblock  avia-builder-el-last  toggle_close_all' >\n<section class='av_toggle_section av-28vkw1u-4aeace344a6e1ae5a3f97848398ad9d9' ><div role=\"tablist\" class=\"single_toggle\" data-tags=\"{All} \"  ><p id='toggle-toggle-id-17' data-fake-id='#toggle-id-17' class='toggler  av-title-above '  role='tab' tabindex='0' aria-controls='toggle-id-17' data-slide-speed=\"200\" data-title=\"Description\" data-title-open=\"\" data-aria_collapsed=\"Click to expand: Description\" data-aria_expanded=\"Click to collapse: Description\">Description<span class=\"toggle_icon\"><span class=\"vert_icon\"><\/span><span class=\"hor_icon\"><\/span><\/span><\/p><div id='toggle-id-17' aria-labelledby='toggle-toggle-id-17' role='region' class='toggle_wrap  av-title-above'  ><div class='toggle_content invers-color ' ><p>Learning and representing temporal structure is a fundamental challenge for neural systems. From episodic memory and navigation to motor control and cognition, the brain must learn, store, and flexibly replay information that unfolds over time and spans multiple temporal scales, from moments to a lifetime. Recurrent neural networks (RNNs) provide a natural conceptual framework for this problem, as their intrinsic dynamics enable memory, sequence generation, and temporal integration. Yet, despite substantial progress, key questions remain unresolved: How do recurrent networks learn complex temporal structures using biologically plausible mechanisms? How are learned sequences maintained and replayed while neural representations continue to drift? These questions led to the development of a plethora of distinct approaches to building fundamental theories of brain operation and the interpretation of observable experimental data. This workshop brings together complementary perspectives from experimental studies, theoretical neuroscience, and computational modeling to explore mechanisms of sequence learning, replay, and memory in recurrent networks. Particular emphasis lies on biologically grounded approaches, which are continuously debated (what does \u201cbiologically-plausible\u201d truly mean?), including local and multi-factor plasticity rules, spiking dynamics, and the role of cellular and circuit-level structure in shaping temporal representations. Topics include the emergence and replay of sequences in recurrent circuits, the interaction between network dynamics and synaptic plasticity, and the stability of memory representations in the presence of drift. The event is a gathering of true \u201cmulti-instrumentalists\u201d with broad, mutually overlapping expertise and interests, that will tackle the problem of sequence learning from complementary perspectives. The workshop will open with a joint team talk by Rosanna Sammons and Stefano Masserini on the distinct aspects of the structure and function of the CA3 hippocampal module, with insights from anatomical and electrophysiological experimental work as well as computational modeling. Christian Tetzlaff will guide us through advances in our understanding of sequence learning and biologically plausible models of sequence learning in the hippocampus. Mattia Chini will give his vision and overview of empirical evidence on preconfigured architectures in developing brain and emergence of protosequences, while Ben von H\u00fcnerbein will unfold the discussion on modeling of sequence learning, which, on the one hand, draws on recent advances in neurobiology and, on the other hand, holds a potential for highly efficient implementations on neuromorphic hardware. Finally, Eleanor Holton provides insights into the differences and similarities between human learning and ANNs, concluding the workshop&#8217;s evening session. The morning session of the workshop will start with a brief recap of the previous day and a talk by Icaro Lopez Costa and Tatjana Tchumatchenko, followed by a discussion on how artificial agents can learn to target multiple goals in complex environments. Next, Manuel Brenner will demonstrate an approach to dissecting the functional role of nonlinearity in recurrent networks, and Claudia Clopath will present how temporal backbones that support sequence generation can be modeled in neuronal circuits. Finally, Raoul-Martin Memmesheimer will provide us with the insights on how structured sequences can be learned and consolidated across the brain in the presence of representational drift. The session will conclude with a comprehensive recap and a vivid, integrative discussion that emphasizes the role of incorporating biological insights into computational models, while also exploring novel approaches to the validation of such models using experimental data.<\/p>\n<\/div><\/div><\/div><\/section>\n<section class='av_toggle_section av-272cfaq-2bf1de96e36399493baa3fafa309209a' ><div role=\"tablist\" class=\"single_toggle\" data-tags=\"{All} \"  ><p id='toggle-toggle-id-18' data-fake-id='#toggle-id-18' class='toggler  av-title-above '  role='tab' tabindex='0' aria-controls='toggle-id-18' data-slide-speed=\"200\" data-title=\"Schedule: Monday, Sept 28\" data-title-open=\"\" data-aria_collapsed=\"Click to expand: Schedule: Monday, Sept 28\" data-aria_expanded=\"Click to collapse: Schedule: Monday, Sept 28\">Schedule: Monday, Sept 28<span class=\"toggle_icon\"><span class=\"vert_icon\"><\/span><span class=\"hor_icon\"><\/span><\/span><\/p><div id='toggle-id-18' aria-labelledby='toggle-toggle-id-18' role='region' class='toggle_wrap  av-title-above'  ><div class='toggle_content invers-color ' ><table width=\"959\">\n<tbody>\n<tr>\n<td width=\"94\"><strong>14:00<\/strong><\/td>\n<td width=\"425\">Opening and overview of the workshop<\/td>\n<td width=\"441\"><em>Intro: Sequence learning in recurrent neural networks<\/em><\/td>\n<\/tr>\n<tr>\n<td width=\"94\"><strong>14:30<\/strong><\/td>\n<td width=\"425\">Rosanna Sammons &amp; Stefano Masserini<br \/>\n( Charit\u00e9 \u2013 Universit\u00e4tsmedizin Berlin &amp; Humboldt Universit\u00e4t zu Berlin, Germany)<\/td>\n<td width=\"441\"><em>Structure of the CA3 pyramidal population and its influence on the local network<\/em><\/td>\n<\/tr>\n<tr>\n<td width=\"94\"><strong>15:25<\/strong><\/td>\n<td width=\"425\">Christian Tetzlaff<br \/>\n(University Medical Center G\u00f6ttingen, Germany)<\/td>\n<td width=\"441\"><em>The dynamic control of neuronal activity sequences and their functional implication<\/em><\/td>\n<\/tr>\n<tr>\n<td width=\"94\"><strong>16:00<\/strong><\/td>\n<td colspan=\"2\" width=\"865\"><em>\u00a0Coffee break<\/em><\/td>\n<\/tr>\n<tr>\n<td width=\"94\"><strong>16:30<\/strong><\/td>\n<td width=\"425\">Mattia Chini<br \/>\n(GIGA &#8211; University Li\u00e8ge, Belgium)<\/td>\n<td width=\"441\"><em>Preconfigured architecture of the developing mouse brain and emergence of protosequences<\/em><\/td>\n<\/tr>\n<tr>\n<td width=\"94\"><strong>17:05<\/strong><\/td>\n<td width=\"425\">Ben von H\u00fcnerbein<br \/>\n(University of Bern, Switzerland)<\/td>\n<td width=\"441\"><em>Biologically plausible learning of complex sequences in structured recurrent neural networks<\/em><\/td>\n<\/tr>\n<tr>\n<td width=\"94\"><strong>17:40<\/strong><\/td>\n<td width=\"425\">Eleanor Holton<br \/>\n(Princeton University, USA)<\/td>\n<td width=\"441\"><em>Humans and neural networks show similar patterns of transfer and interference during continual learning<\/em><\/td>\n<\/tr>\n<tr>\n<td width=\"94\"><strong>18:15<\/strong><\/td>\n<td colspan=\"2\" width=\"865\"><em>Recap, Q&amp;A and discussion<\/em><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div><\/div><\/div><\/section>\n<section class='av_toggle_section av-24titxe-f1fc9bf46c600c0c5d306a9e2381cbd5' ><div role=\"tablist\" class=\"single_toggle\" data-tags=\"{All} \"  ><p id='toggle-toggle-id-19' data-fake-id='#toggle-id-19' class='toggler  av-title-above '  role='tab' tabindex='0' aria-controls='toggle-id-19' data-slide-speed=\"200\" data-title=\"Schedule: Tuesday, Sept 29\" data-title-open=\"\" data-aria_collapsed=\"Click to expand: Schedule: Tuesday, Sept 29\" data-aria_expanded=\"Click to collapse: Schedule: Tuesday, Sept 29\">Schedule: Tuesday, Sept 29<span class=\"toggle_icon\"><span class=\"vert_icon\"><\/span><span class=\"hor_icon\"><\/span><\/span><\/p><div id='toggle-id-19' aria-labelledby='toggle-toggle-id-19' role='region' class='toggle_wrap  av-title-above'  ><div class='toggle_content invers-color ' ><table width=\"959\">\n<tbody>\n<tr>\n<td width=\"94\"><strong>8:30<\/strong><\/td>\n<td colspan=\"2\" width=\"865\"><em>A brief overview of the previous day<\/em><\/td>\n<\/tr>\n<tr>\n<td width=\"94\"><strong>8:50<\/strong><\/td>\n<td width=\"425\">Icaro Lopez Costa &amp; Tatjana Tchumatchenko<br \/>\n(University of Bonn, Germany)<\/td>\n<td width=\"441\"><em>Solving a sequence of 2D navigational tasks with ANNs (to be updated)<\/em><\/td>\n<\/tr>\n<tr>\n<td width=\"94\"><strong>9:45<\/strong><\/td>\n<td width=\"425\">Manuel Brenner<br \/>\n(Ernst Str\u00fcngmann Institute, Germany)<\/td>\n<td width=\"441\"><em>Computational roles of nonlinearity in sequence modeling<\/em><\/td>\n<\/tr>\n<tr>\n<td width=\"94\"><strong>10:00<\/strong><\/td>\n<td colspan=\"2\" width=\"865\"><em>Coffee break<\/em><\/td>\n<\/tr>\n<tr>\n<td width=\"94\"><strong>10:30<\/strong><\/td>\n<td width=\"425\">Claudia Clopath<br \/>\n(Imperial College London, UK)<\/td>\n<td width=\"441\"><em>Modelling temporal backbones in circuits<\/em><\/td>\n<\/tr>\n<tr>\n<td width=\"94\"><strong>11:05<\/strong><\/td>\n<td width=\"425\">Raoul-Martin Memmesheimer<br \/>\n(University of Bonn, Germany)<\/td>\n<td width=\"441\"><em>Structural sequences: learning, drift and consolidation (to be updated)<\/em><\/td>\n<\/tr>\n<tr>\n<td width=\"94\"><strong>11:40<\/strong><\/td>\n<td width=\"425\"><em>Recap, Q&amp;A and discussion<\/em><\/td>\n<td width=\"441\"><em>Biologically plausible models of the sequence learning and their validation<\/em><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div><\/div><\/div><\/section>\n<\/div><\/p><\/div><div  class='flex_column av-238v802-8295aa48d4ae8f38aef15396eeeeb35c av_one_full  avia-builder-el-51  el_after_av_one_full  el_before_av_one_full  first flex_column_div  column-top-margin'     ><p><section  class='av_textblock_section av-226bu0i-468b5686d7e51072ee2260052032c2b2 '  ><div class='avia_textblock' ><p><span style=\"color: #000000;\"><strong>Shaping plasticity: Structural constraints and temporal dynamics in learning<\/strong><em><br \/>\nOrganizers:<\/em><br \/>\n<\/span><span style=\"color: #000000;\">Anna-Maria J\u00fcrgensen | University of Cambridge &amp; Imperial College London, UK<br \/>\nEmmanouil Giannakakis | Imperial College London, UK<\/span><em><br \/>\n<\/em><\/p>\n<\/div><\/section><br \/>\n<div  class='togglecontainer av-20r3wua-33bf093e336f9cfccdf568c286b40f28  avia-builder-el-53  el_after_av_textblock  avia-builder-el-last  toggle_close_all' >\n<section class='av_toggle_section av-1xouflu-301820a623d9f7686d794092e3908452' ><div role=\"tablist\" class=\"single_toggle\" data-tags=\"{All} \"  ><p id='toggle-toggle-id-20' data-fake-id='#toggle-id-20' class='toggler  av-title-above '  role='tab' tabindex='0' aria-controls='toggle-id-20' data-slide-speed=\"200\" data-title=\"Description\" data-title-open=\"\" data-aria_collapsed=\"Click to expand: Description\" data-aria_expanded=\"Click to collapse: Description\">Description<span class=\"toggle_icon\"><span class=\"vert_icon\"><\/span><span class=\"hor_icon\"><\/span><\/span><\/p><div id='toggle-id-20' aria-labelledby='toggle-toggle-id-20' role='region' class='toggle_wrap  av-title-above'  ><div class='toggle_content invers-color ' ><p>Synaptic plasticity rules operate over the signals made available by network connectivity. The anatomical and functional structure of a circuit determines which neural representations contribute to pre- and postsynaptic activity, reinforcement signals, recurrent feedback, or additional modulatory factors. As a result, learning is inherently constrained by network architecture: Connectivity defines the information accessible to plasticity rules, thereby shaping the landscape of possible associative changes. This inductive bias, imposed by network architecture, structurally partitions plasticity by determining which signals can jointly influence synaptic plasticity across spatial scales, from individual synapses to local circuits and distributed brain regions. Such partitioning increases the precision of credit assignment and reduces interference, supporting continual learning and stabilizing previously acquired associations. Additionally, these structural constraints allow learning based on weaker or noisier representations and enable generalization by preconfiguring circuits to preferentially link particular representations. The temporal dynamics of plasticity arise from the coordinated timing of these interacting representations, as they persist and evolve over time through eligibility traces and network dynamics. Plasticity rules are determined both by the anatomical availability of signals and their temporal orchestration within the circuit. In light of recent advances in connectomics, this workshop will explore how network architecture functions as a structural prior over learning dynamics, biasing, and constraining synaptic plasticity.<\/p>\n<\/div><\/div><\/div><\/section>\n<section class='av_toggle_section av-1x2ee3m-8be19c507fd116064f71d3c7c055ef92' ><div role=\"tablist\" class=\"single_toggle\" data-tags=\"{All} \"  ><p id='toggle-toggle-id-21' data-fake-id='#toggle-id-21' class='toggler  av-title-above '  role='tab' tabindex='0' aria-controls='toggle-id-21' data-slide-speed=\"200\" data-title=\"Schedule: Monday, Sept 28\" data-title-open=\"\" data-aria_collapsed=\"Click to expand: Schedule: Monday, Sept 28\" data-aria_expanded=\"Click to collapse: Schedule: Monday, Sept 28\">Schedule: Monday, Sept 28<span class=\"toggle_icon\"><span class=\"vert_icon\"><\/span><span class=\"hor_icon\"><\/span><\/span><\/p><div id='toggle-id-21' aria-labelledby='toggle-toggle-id-21' role='region' class='toggle_wrap  av-title-above'  ><div class='toggle_content invers-color ' ><table width=\"959\">\n<tbody>\n<tr>\n<td width=\"94\"><strong>14:00<\/strong><\/td>\n<td width=\"425\">Emmanouil Giannakakis &amp; Anna-Maria J\u00fcrgensen<br \/>\n(Imperial College London &amp; University of Cambridge, UK)<\/td>\n<td width=\"441\"><em>Introduction <\/em><\/td>\n<\/tr>\n<tr>\n<td width=\"94\"><strong>14:15<\/strong><\/td>\n<td width=\"425\">Wulfram Gerstner<br \/>\n(\u00c9cole polytechnique f\u00e9d\u00e9rale de Lausanne, Switzerland)<\/td>\n<td width=\"441\"><em>Beyond Hebb: Self-supervised representation learning with local predictive plasticity rule<\/em><\/td>\n<\/tr>\n<tr>\n<td width=\"94\"><strong>15:00<\/strong><\/td>\n<td width=\"425\">Loreen Hert\u00e4g<br \/>\n(Technische Universit\u00e4t Berlin, Germany)<\/td>\n<td width=\"441\"><em>Shaping prediction-error circuits: the role of inhibitory plasticity and connectivity<\/em><\/td>\n<\/tr>\n<tr>\n<td width=\"94\"><strong>16:00<\/strong><\/td>\n<td colspan=\"2\" width=\"865\"><em>\u00a0Coffee break<\/em><\/td>\n<\/tr>\n<tr>\n<td width=\"94\"><strong>16:30<\/strong><\/td>\n<td width=\"425\">Claire Meissner-Bernard<br \/>\n(Sorbonne University, France)<\/td>\n<td width=\"441\"><em>Formation and properties of memory networks with excitatory-inhibitory assemblies<\/em><\/td>\n<\/tr>\n<tr>\n<td width=\"94\"><strong>17:15<\/strong><\/td>\n<td width=\"425\">Tim Vogels<br \/>\n(Institute of Science and Technology Austria (ISTA), Austria)<\/td>\n<td width=\"441\"><em>TBA<\/em><\/td>\n<\/tr>\n<tr>\n<td width=\"94\"><strong>18:00<\/strong><\/td>\n<td width=\"425\">Emmanouil Giannakakis &amp; Anna-Maria J\u00fcrgensen<br \/>\n(Imperial College London &amp; University of Cambridge, UK)<\/td>\n<td width=\"441\"><em>Concluding remarks and discussion <\/em><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div><\/div><\/div><\/section>\n<section class='av_toggle_section av-1uumy6q-930055146c867d7e45669ea1b37d6aa8' ><div role=\"tablist\" class=\"single_toggle\" data-tags=\"{All} \"  ><p id='toggle-toggle-id-22' data-fake-id='#toggle-id-22' class='toggler  av-title-above '  role='tab' tabindex='0' aria-controls='toggle-id-22' data-slide-speed=\"200\" data-title=\"Schedule: Tuesday, Sept 29\" data-title-open=\"\" data-aria_collapsed=\"Click to expand: Schedule: Tuesday, Sept 29\" data-aria_expanded=\"Click to collapse: Schedule: Tuesday, Sept 29\">Schedule: Tuesday, Sept 29<span class=\"toggle_icon\"><span class=\"vert_icon\"><\/span><span class=\"hor_icon\"><\/span><\/span><\/p><div id='toggle-id-22' aria-labelledby='toggle-toggle-id-22' role='region' class='toggle_wrap  av-title-above'  ><div class='toggle_content invers-color ' ><table width=\"959\">\n<tbody>\n<tr>\n<td width=\"94\"><strong>8:30<\/strong><\/td>\n<td width=\"425\">Emmanouil Giannakakis &amp; Anna-Maria J\u00fcrgensen<br \/>\n(Imperial College London &amp; University of Cambridge, UK)<\/td>\n<td width=\"440\"><em>Introduction <\/em><\/td>\n<\/tr>\n<tr>\n<td width=\"94\"><strong>8:35<\/strong><\/td>\n<td width=\"425\">Andr\u00e9 Fiala<br \/>\n(University of G\u00f6ttingen, Germany)<\/td>\n<td width=\"440\"><em>Beyond simple associations: Dissecting neuronal circuits and dynamics of higher-order learning in Drosophila<\/em><\/td>\n<\/tr>\n<tr>\n<td width=\"94\"><strong>9:15<\/strong><\/td>\n<td width=\"425\">Albert Cardona<br \/>\n(University of Cambridge &amp; MRC Laboratory of Molecular Medicine Cambridge, UK)<\/td>\n<td width=\"440\"><em>The role of axo-axonic synapses in signal processing<\/em><\/td>\n<\/tr>\n<tr>\n<td width=\"94\"><strong>10:00<\/strong><\/td>\n<td colspan=\"2\" width=\"865\"><em>Coffee break<\/em><\/td>\n<\/tr>\n<tr>\n<td width=\"94\"><strong>10:30<\/strong><\/td>\n<td width=\"425\">Panayiota Poirazi<br \/>\n(Foundation for Research and Technology Hellas, Greece)<\/td>\n<td width=\"440\"><em>Exploring the role of synaptic and dendritic plasticity in flexible learning<\/em><\/td>\n<\/tr>\n<tr>\n<td width=\"94\"><strong>11:15<\/strong><\/td>\n<td width=\"425\">Andreas L\u00fcthi<br \/>\n(Friedrich Miescher Institute for Biomedical Research (FMI), Switzerland)<\/td>\n<td width=\"440\"><em>TBA<\/em><\/td>\n<\/tr>\n<tr>\n<td width=\"94\"><strong>12:00<\/strong><\/td>\n<td width=\"425\">Emmanouil Giannakakis, Anna-Maria J\u00fcrgensen<br \/>\n(Imperial College London &amp; University of Cambridge, UK)<\/td>\n<td width=\"440\"><em>Concluding remarks and discussion <\/em><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div><\/div><\/div><\/section>\n<\/div><\/p><\/div><div  class='flex_column av-1tsgf5u-b443aac9ea4fc8ea1923524fb03b3316 av_one_full  avia-builder-el-54  el_after_av_one_full  el_before_av_one_full  first flex_column_div  column-top-margin'     ><p><section  class='av_textblock_section av-1ri2vwi-67d21da23ccdcbae46f3be16fe9990b4 '  ><div class='avia_textblock' ><p><span style=\"color: #000000;\"><strong>Advances in optimization of biologically constrained models and how to use them<\/strong><em><br \/>\nOrganizers:<\/em><br \/>\n<\/span><span style=\"color: #000000;\">Alessio Quaresima | Institut de l\u2019Audition &#8211; Institut Pasteur, France<br \/>\nJulia Gygax | Friedrich Miescher Institute for Biomedical Research (FMI), Switzerland<br \/>\nMaciej Kania | Institute of Science and Technology Austria (ISTA), Austria<br \/>\nZoe Harrington | Institute of Science and Technology Austria (ISTA), Austria<\/span><em><br \/>\n<\/em><\/p>\n<\/div><\/section><br \/>\n<div  class='togglecontainer av-1q1riz6-3d21ee7b68d965aca523d8f90311afe5  avia-builder-el-56  el_after_av_textblock  avia-builder-el-last  toggle_close_all' >\n<section class='av_toggle_section av-1oagpxu-2cb5c789ee182979c25d66f6b28ab7af' ><div role=\"tablist\" class=\"single_toggle\" data-tags=\"{All} \"  ><p id='toggle-toggle-id-23' data-fake-id='#toggle-id-23' class='toggler  av-title-above '  role='tab' tabindex='0' aria-controls='toggle-id-23' data-slide-speed=\"200\" data-title=\"Description\" data-title-open=\"\" data-aria_collapsed=\"Click to expand: Description\" data-aria_expanded=\"Click to collapse: Description\">Description<span class=\"toggle_icon\"><span class=\"vert_icon\"><\/span><span class=\"hor_icon\"><\/span><\/span><\/p><div id='toggle-id-23' aria-labelledby='toggle-toggle-id-23' role='region' class='toggle_wrap  av-title-above'  ><div class='toggle_content invers-color ' ><p>Models in computational neuroscience provide understanding at different levels. Abstract models with numerically tractable algorithms can be optimized to reproduce experimental data, but they often lack direct interpretability of the underlying biological mechanisms. Conversely, biologically constrained models provide mechanistic insights into brain circuits. Still, it is challenging to achieve computations or reproduce data with them, as they cannot be explicitly trained due to their non-differentiable dynamics or overall complexity. In this workshop, we will present novel approaches to model optimization that combine the best of both worlds to gain insights into brain computations. The methods to optimize biologically constrained models often fall into two groups. On the one hand, the model parameter space is explored algorithmically using high-throughput approaches and tested against experimental data. Unlike traditional &#8220;hand-tuned&#8221; parameter exploration, these novel methods offer entire families of solutions. Furthermore, they identify degeneracies in the parameter space that can lead to testable experimental predictions. On the other hand, one can optimize models to solve a task. Adding biological constraints steers them to exhibit realistic activity patterns while performing a specific function. This approach enables us to identify the role of different biological constraints on computations and how they shape the model\u2019s solution to the task. Given the rapid advances in optimizing biologically constrained models, it is necessary to provide a comprehensive overview of the leading methods, their respective advantages and limitations, and the research questions for which they are best suited. This workshop will feature a series of talks ranging from gradient-free optimization to surrogate gradients and Bayesian inference. Our goal is to provide conceptual frameworks and tools for investigating complex systems with no obvious analytical solutions. We will discuss how the identified solution space should be interpreted in the context of the optimization method used and how this advances our understanding of brain computations.<\/p>\n<\/div><\/div><\/div><\/section>\n<section class='av_toggle_section av-1mtjxeq-2e11ed946b0137c5d91be548037aa111' ><div role=\"tablist\" class=\"single_toggle\" data-tags=\"{All} \"  ><p id='toggle-toggle-id-24' data-fake-id='#toggle-id-24' class='toggler  av-title-above '  role='tab' tabindex='0' aria-controls='toggle-id-24' data-slide-speed=\"200\" data-title=\"Schedule: Monday, Sept 28\" data-title-open=\"\" data-aria_collapsed=\"Click to expand: Schedule: Monday, Sept 28\" data-aria_expanded=\"Click to collapse: Schedule: Monday, Sept 28\">Schedule: Monday, Sept 28<span class=\"toggle_icon\"><span class=\"vert_icon\"><\/span><span class=\"hor_icon\"><\/span><\/span><\/p><div id='toggle-id-24' aria-labelledby='toggle-toggle-id-24' role='region' class='toggle_wrap  av-title-above'  ><div class='toggle_content invers-color ' ><table width=\"959\">\n<tbody>\n<tr>\n<td width=\"94\"><strong>14:00<\/strong><\/td>\n<td width=\"425\">Maciej Kania<br \/>\n(Institute of Science and Technology Austria (ISTA), Austria)<\/td>\n<td width=\"441\"><em>Introduction <\/em><\/td>\n<\/tr>\n<tr>\n<td width=\"94\"><strong>14:05<\/strong><\/td>\n<td width=\"425\">Julia Gygax<br \/>\n(Friedrich Miescher Institute for Biomedical Research (FMI), Switzerland)<\/td>\n<td width=\"441\"><em>Theory behind surrogate gradients and how to use them to study neuronal assemblies<\/em><\/td>\n<\/tr>\n<tr>\n<td width=\"94\"><strong>14:35<\/strong><\/td>\n<td width=\"425\">Guillaume Bellec<br \/>\n(TU Wien, Austria)<\/td>\n<td width=\"441\"><em>Perturbation testing to validate deep learning models of cortical computation<\/em><\/td>\n<\/tr>\n<tr>\n<td width=\"94\"><strong>15:05<\/strong><\/td>\n<td width=\"425\">Rory Bedford<br \/>\n(Friedrich Miescher Institute for Biomedical Research (FMI), Switzerland)<\/td>\n<td width=\"441\"><em>Connectome-constrained spiking network models of functional activity<\/em><\/td>\n<\/tr>\n<tr>\n<td width=\"94\"><strong>15:35<\/strong><\/td>\n<td width=\"425\">Dan Goodman<br \/>\n(Imperial College London, UK)<\/td>\n<td width=\"441\"><em>Learning to use neuromodulation for efficient sensory processing<\/em><\/td>\n<\/tr>\n<tr>\n<td width=\"94\"><strong>16:00<\/strong><\/td>\n<td colspan=\"2\" width=\"865\"><em>\u00a0Coffee break<\/em><\/td>\n<\/tr>\n<tr>\n<td width=\"94\"><strong>16:30<\/strong><\/td>\n<td width=\"425\">Robert G\u00fctig<br \/>\n(Charit\u00e9 \u2013 Universit\u00e4tsmedizin Berlin, Germany)<\/td>\n<td width=\"441\"><em>Interactions between long- and short-term synaptic plasticity transform temporal neural representations into spatial<\/em><\/td>\n<\/tr>\n<tr>\n<td width=\"94\"><strong>17:00<\/strong><\/td>\n<td width=\"425\">Jonathan Cornford<br \/>\n(University of Leeds, UK)<\/td>\n<td width=\"441\"><em>Mirror descent as a framework for normative brain-like learning<\/em><\/td>\n<\/tr>\n<tr>\n<td width=\"94\"><strong>17:30<\/strong><\/td>\n<td width=\"425\">Julijana Gjorgjieva<br \/>\n(Technical University of Munich, Germany)<\/td>\n<td width=\"441\"><em>Discovering biologically plausible rules from trained RNNs<\/em><\/td>\n<\/tr>\n<tr>\n<td width=\"94\"><strong>18:00<\/strong><\/td>\n<td colspan=\"2\" width=\"865\"><em>Discussion<\/em><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div><\/div><\/div><\/section>\n<section class='av_toggle_section av-1lbhjyq-b966bca0acf0b4ccc4001ec1e8d65599' ><div role=\"tablist\" class=\"single_toggle\" data-tags=\"{All} \"  ><p id='toggle-toggle-id-25' data-fake-id='#toggle-id-25' class='toggler  av-title-above '  role='tab' tabindex='0' aria-controls='toggle-id-25' data-slide-speed=\"200\" data-title=\"Schedule: Tuesday, Sept 29\" data-title-open=\"\" data-aria_collapsed=\"Click to expand: Schedule: Tuesday, Sept 29\" data-aria_expanded=\"Click to collapse: Schedule: Tuesday, Sept 29\">Schedule: Tuesday, Sept 29<span class=\"toggle_icon\"><span class=\"vert_icon\"><\/span><span class=\"hor_icon\"><\/span><\/span><\/p><div id='toggle-id-25' aria-labelledby='toggle-toggle-id-25' role='region' class='toggle_wrap  av-title-above'  ><div class='toggle_content invers-color ' ><table width=\"959\">\n<tbody>\n<tr>\n<td width=\"94\"><strong>8:30<\/strong><\/td>\n<td width=\"425\">Maciej Kania<br \/>\n<span style=\"font-family: inherit;\">(Institute of Science and Technology Austria (ISTA), Austria)<\/span><\/td>\n<td width=\"441\"><em>Introduction <\/em><\/td>\n<\/tr>\n<tr>\n<td width=\"94\"><strong>8:35<\/strong><\/td>\n<td width=\"425\">Jakob Macke<br \/>\n(Max Planck Institute for Intelligent Systems, University of T\u00fcbingen &amp; T\u00fcbingen AI Center, Germany)<\/td>\n<td width=\"441\"><em>Simulation-based inference: Progress, promise, open problems<\/em><\/td>\n<\/tr>\n<tr>\n<td width=\"94\"><strong>9:05<\/strong><\/td>\n<td width=\"425\">Zoe Harrington<br \/>\n(Institute of Science and Technology Austria (ISTA), Austria)<\/td>\n<td width=\"441\"><em>Geometric and topological analysis of SBI posteriors over plasticity rule space in spiking networks<\/em><\/td>\n<\/tr>\n<tr>\n<td width=\"94\"><strong>9:35<\/strong><\/td>\n<td width=\"425\">Anastasia Krouglova<br \/>\n(VIB-Neuroelectronics Research Flanders (NERF) &amp; KU Leuven, Belgium)<\/td>\n<td width=\"441\"><em>Multifidelity simulation-based inference for computationally expensive simulators<\/em><\/td>\n<\/tr>\n<tr>\n<td width=\"94\"><strong>10:00<\/strong><\/td>\n<td colspan=\"2\" width=\"865\"><em>\u00a0Coffee break<\/em><\/td>\n<\/tr>\n<tr>\n<td width=\"94\"><strong>10:30<\/strong><\/td>\n<td width=\"425\">Alessio Quaresima<br \/>\n(Institut de l\u2019Audition &#8211; Institut Pasteur, France)<\/td>\n<td width=\"441\"><em>Data-optimized biophysical model identifies parallel functional subnetworks <\/em><\/td>\n<\/tr>\n<tr>\n<td width=\"94\"><strong>11:00<\/strong><\/td>\n<td width=\"425\">Helmut Strey<br \/>\n(State University of New York at Stony Brook &amp; Massachusetts Institute of Technology, USA)<\/td>\n<td width=\"441\"><em>Scientific machine learning of chaotic systems discovers governing equations for neural populations<\/em><\/td>\n<\/tr>\n<tr>\n<td width=\"94\"><strong>11:30<\/strong><\/td>\n<td width=\"425\">Adrienne Fairhall<br \/>\n(University of Washington, USA)<\/td>\n<td width=\"441\"><em>Meta-learning learning rules that build and maintain circuit motifs<\/em><\/td>\n<\/tr>\n<tr>\n<td width=\"94\"><strong>12:00<\/strong><\/td>\n<td colspan=\"2\" width=\"865\"><em>Discussion<\/em><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div><\/div><\/div><\/section>\n<\/div><\/p><\/div><div  class='flex_column av-1jchm2q-1511d98b87cda17f70d76d2b18ca2271 av_one_full  avia-builder-el-57  el_after_av_one_full  el_before_av_one_full  first flex_column_div  column-top-margin'     ><p><section  class='av_textblock_section av-1i0v5ea-8e6eafc6e20aca3610aa4ce43980b5aa '  ><div class='avia_textblock' ><p><span style=\"color: #000000;\"><strong>Bridging the species divide: comparative approaches to study neural signatures<\/strong><br \/>\n<em>Organizers:<\/em><br \/>\n<\/span><span style=\"color: #000000;\">Natalie Schaworonkow | Ernst Str\u00fcngmann Institute, Germany<br \/>\nDennis Nestvogel | Max Planck Institute of Psychiatry, Germany<\/span><em><br \/>\n<\/em><\/p>\n<\/div><\/section><br \/>\n<div  class='togglecontainer av-1gk27r6-1e74c079186bfe38f5e548da2af37ed8  avia-builder-el-59  el_after_av_textblock  avia-builder-el-last  toggle_close_all' >\n<section class='av_toggle_section av-1f3fa5u-6d853a5736f06a47fba438513d8095c9' ><div role=\"tablist\" class=\"single_toggle\" data-tags=\"{All} \"  ><p id='toggle-toggle-id-26' data-fake-id='#toggle-id-26' class='toggler  av-title-above '  role='tab' tabindex='0' aria-controls='toggle-id-26' data-slide-speed=\"200\" data-title=\"Description\" data-title-open=\"\" data-aria_collapsed=\"Click to expand: Description\" data-aria_expanded=\"Click to collapse: Description\">Description<span class=\"toggle_icon\"><span class=\"vert_icon\"><\/span><span class=\"hor_icon\"><\/span><\/span><\/p><div id='toggle-id-26' aria-labelledby='toggle-toggle-id-26' role='region' class='toggle_wrap  av-title-above'  ><div class='toggle_content invers-color ' ><p>Neuroscience has become immensely data-rich, with high-density recordings across many species. But animal and human research remains largely siloed, even within the same model system. Cross-species comparison of electrophysiological activity offers a promising path to establish necessary and sufficient conditions for linking circuit dynamics and behavior, leveraging the excellent temporal resolution and access to mesoscale dynamics that electrophysiology provides. Many neural signatures appear similar across species, but what does it mean to equate rhythms or transient events between different species? This challenge is compounded by the fact that even within the same species and brain region, electrophysiological signatures are often labeled inconsistently across studies. For example, the alpha rhythm in the mouse has been proposed to occur at a frequency of 3\u20135 Hz, which differs significantly from the canonical range of 8\u201313 Hz observed in humans. Furthermore, there are examples of abrupt relabeling, such as the reclassification of posterior 8 Hz activity from theta to alpha during human development. Neuroscience has become immensely data-rich, with high-density recordings across many species. But animal and human research remains largely siloed, even within the same model system. Cross-species comparison of electrophysiological activity offers a promising path to establish necessary and sufficient conditions for linking circuit dynamics and behavior, leveraging the excellent temporal resolution and access to mesoscale dynamics that electrophysiology provides. Many neural signatures appear similar across species, but what does it mean to equate rhythms or transient events between different species? This challenge is compounded by the fact that even within the same species and brain region, electrophysiological signatures are often labeled inconsistently across studies. For example, the alpha rhythm in the mouse has been proposed to occur at a frequency of 3\u20135 Hz, which differs significantly from the canonical range of 8\u201313 Hz observed in humans. Furthermore, there are examples of abrupt relabeling, such as the reclassification of posterior 8 Hz activity from theta to alpha during human development. In this workshop, we examine electrophysiological signatures across species through specific examples (theta, alpha, beta, and gamma activity). We ask whether we can establish common definitions and terminology based on criteria beyond frequency alone, including spatial distribution, generative mechanisms at the cellular level, and functional modulation by behavior. We hope to stimulate discussion about the minimal conditions necessary to identify functionally equivalent electrophysiological signatures across species and, through these signatures, investigate behavior and cognition.<\/p>\n<\/div><\/div><\/div><\/section>\n<section class='av_toggle_section av-1d61fbm-5dd00bdd68a7e65a66f306446292d886' ><div role=\"tablist\" class=\"single_toggle\" data-tags=\"{All} \"  ><p id='toggle-toggle-id-27' data-fake-id='#toggle-id-27' class='toggler  av-title-above '  role='tab' tabindex='0' aria-controls='toggle-id-27' data-slide-speed=\"200\" data-title=\"Schedule: Monday, Sept 28\" data-title-open=\"\" data-aria_collapsed=\"Click to expand: Schedule: Monday, Sept 28\" data-aria_expanded=\"Click to collapse: Schedule: Monday, Sept 28\">Schedule: Monday, Sept 28<span class=\"toggle_icon\"><span class=\"vert_icon\"><\/span><span class=\"hor_icon\"><\/span><\/span><\/p><div id='toggle-id-27' aria-labelledby='toggle-toggle-id-27' role='region' class='toggle_wrap  av-title-above'  ><div class='toggle_content invers-color ' ><table width=\"959\">\n<tbody>\n<tr>\n<td width=\"94\"><strong>14:00<\/strong><\/td>\n<td colspan=\"2\" width=\"865\"><em>General introduction <\/em><\/td>\n<\/tr>\n<tr>\n<td width=\"94\"><strong>14:30<\/strong><\/td>\n<td width=\"425\">Lukas Kunz<br \/>\n(University Hospital Bonn, Germany)<\/td>\n<td width=\"441\"><em>Investigating the relationship between theta and spiking activity through human single-unit recordings<\/em><\/td>\n<\/tr>\n<tr>\n<td width=\"94\"><strong>15:00<\/strong><\/td>\n<td width=\"425\">Abraham Z. Vollan<br \/>\n(Kavli Institute for Systems Neuroscience, Norway)<\/td>\n<td width=\"441\"><em>Adaptive modulation of theta sweeps in the rodent spatial navigation circuit<\/em><\/td>\n<\/tr>\n<tr>\n<td width=\"94\"><strong>15:30<\/strong><\/td>\n<td width=\"425\">Pascal Malkemper<br \/>\n(Max Planck Institute for Neurobiology of Behavior, Germany)<\/td>\n<td width=\"441\"><em>Hippocampal rhythms in a strictly subterranean mammal<\/em><\/td>\n<\/tr>\n<tr>\n<td width=\"94\"><strong>16:00<\/strong><\/td>\n<td colspan=\"2\" width=\"865\"><em>\u00a0Coffee break<\/em><\/td>\n<\/tr>\n<tr>\n<td width=\"94\"><strong>16:30<\/strong><\/td>\n<td width=\"425\">Sacha van Albada<br \/>\n(University of Cologne &amp; Forschungszentrum J\u00fclich, Germany)<\/td>\n<td width=\"441\"><em>Mean-field and spiking models of thalamocortical alpha rhythm generation<\/em><\/td>\n<\/tr>\n<tr>\n<td width=\"94\"><strong>17:00<\/strong><\/td>\n<td width=\"425\">Tzvetan Popov<br \/>\n(University of Zurich, Switzerland &amp; University of Konstanz, Germany)<\/td>\n<td width=\"441\"><em>Alpha rhythms in honey bees and humans<\/em><\/td>\n<\/tr>\n<tr>\n<td width=\"94\"><strong>17:30<\/strong><\/td>\n<td width=\"425\">Alina Studenova<br \/>\n(Max Planck Institute for Human Cognitive and Brain Sciences, Germany)<\/td>\n<td width=\"441\"><em>On the relevance of alpha oscillations in generation of evoked responses<\/em><\/td>\n<\/tr>\n<tr>\n<td width=\"94\"><strong>18:00<\/strong><\/td>\n<td colspan=\"2\" width=\"865\"><em>General discussion<\/em><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div><\/div><\/div><\/section>\n<section class='av_toggle_section av-1b12pya-1ba715469e0c8772584c79023d83a86a' ><div role=\"tablist\" class=\"single_toggle\" data-tags=\"{All} \"  ><p id='toggle-toggle-id-28' data-fake-id='#toggle-id-28' class='toggler  av-title-above '  role='tab' tabindex='0' aria-controls='toggle-id-28' data-slide-speed=\"200\" data-title=\"Schedule: Tuesday, Sept 29\" data-title-open=\"\" data-aria_collapsed=\"Click to expand: Schedule: Tuesday, Sept 29\" data-aria_expanded=\"Click to collapse: Schedule: Tuesday, Sept 29\">Schedule: Tuesday, Sept 29<span class=\"toggle_icon\"><span class=\"vert_icon\"><\/span><span class=\"hor_icon\"><\/span><\/span><\/p><div id='toggle-id-28' aria-labelledby='toggle-toggle-id-28' role='region' class='toggle_wrap  av-title-above'  ><div class='toggle_content invers-color ' ><table width=\"959\">\n<tbody>\n<tr>\n<td width=\"94\"><strong>8:30<\/strong><\/td>\n<td width=\"425\">Anton Sirota<br \/>\n(LMU Munich, Germany)<\/td>\n<td width=\"441\"><em>Anatomical and spectral dissection of high frequency oscillations \u2013 towards detection and sorting of units of circuit computation. <\/em><\/td>\n<\/tr>\n<tr>\n<td width=\"94\"><strong>9:00<\/strong><\/td>\n<td width=\"425\">Julia Veit<br \/>\n(University of Bremen &amp; University of Freiburg, Germany)<\/td>\n<td width=\"441\"><em>Inhibitory control of cortical gamma synchronization<\/em><\/td>\n<\/tr>\n<tr>\n<td width=\"94\"><strong>9:30<\/strong><\/td>\n<td width=\"425\">Eric Drebitz<br \/>\n(University of Bremen, Germany)<\/td>\n<td width=\"441\"><em>Gamma synchronization between neurons in the visual cortex is causal for effective information processing and behavior<\/em><\/td>\n<\/tr>\n<tr>\n<td width=\"94\"><strong>10:00<\/strong><\/td>\n<td colspan=\"2\" width=\"865\"><em>\u00a0Coffee break<\/em><\/td>\n<\/tr>\n<tr>\n<td width=\"94\"><strong>10:30<\/strong><\/td>\n<td width=\"425\">TBA<\/td>\n<td width=\"441\"><em>TBA<\/em><\/td>\n<\/tr>\n<tr>\n<td width=\"94\"><strong>11:00<\/strong><\/td>\n<td width=\"425\">TBA<\/td>\n<td width=\"441\"><em>TBA<\/em><\/td>\n<\/tr>\n<tr>\n<td width=\"94\"><strong>11:30<\/strong><\/td>\n<td width=\"425\">TBA<\/td>\n<td width=\"441\"><em>TBA<\/em><\/td>\n<\/tr>\n<tr>\n<td width=\"94\"><strong>12:00<\/strong><\/td>\n<td colspan=\"2\" width=\"865\"><em>General discussion<\/em><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div><\/div><\/div><\/section>\n<\/div><\/p><\/div><div  class='flex_column av-19tcnaa-f69c2a3507b2f29364da88352c071012 av_one_full  avia-builder-el-60  el_after_av_one_full  el_before_av_one_full  first flex_column_div  column-top-margin'     ><p><section  class='av_textblock_section av-17rjbpe-5acd27de247b379f6f176916ecf59b00 '  ><div class='avia_textblock' ><p><span style=\"color: #000000;\"><strong>From lab to wild: how internal states enable adaptive behavior<\/strong><br \/>\n<em>Organizers:<\/em><br \/>\nClaire Sturgill | TUD Dresden University of Technology, Germany<\/span><br \/>\n<span style=\"color: #000000;\">Arman Behrad | TUD Dresden University of Technology, Germany<\/span><em><br \/>\n<\/em><\/p>\n<\/div><\/section><br \/>\n<div  class='togglecontainer av-16az6f6-e5e50d17be9084550a474cc0a3233112  avia-builder-el-62  el_after_av_textblock  avia-builder-el-last  toggle_close_all' >\n<section class='av_toggle_section av-14c6ivm-6f247b56e4eb976d53ac815425e86a36' ><div role=\"tablist\" class=\"single_toggle\" data-tags=\"{All} \"  ><p id='toggle-toggle-id-29' data-fake-id='#toggle-id-29' class='toggler  av-title-above '  role='tab' tabindex='0' aria-controls='toggle-id-29' data-slide-speed=\"200\" data-title=\"Description\" data-title-open=\"\" data-aria_collapsed=\"Click to expand: Description\" data-aria_expanded=\"Click to collapse: Description\">Description<span class=\"toggle_icon\"><span class=\"vert_icon\"><\/span><span class=\"hor_icon\"><\/span><\/span><\/p><div id='toggle-id-29' aria-labelledby='toggle-toggle-id-29' role='region' class='toggle_wrap  av-title-above'  ><div class='toggle_content invers-color ' ><p data-start=\"0\" data-end=\"2025\">Brains are not purely stimulus\u2013response machines; instead, behavior is profoundly shaped by internal states\u2014hidden, temporally persistent variables that modulate how sensory inputs are processed and transformed into behavioral output. These hidden processes include, for instance, arousal, motivation, attention, affect, and homeostatic drives, and integrate information from the external environment and physiological conditions into centralized brain states that orchestrate behavior and physiology (Marques, \u2026, Li 2019). A central challenge is that the same behavioral output can arise from different internal conditions, and conversely, the same sensory input can lead to different actions depending on an organism\u2019s internal state\u2014especially in naturalistic and ecological settings where task epochs and experimenter-defined labels are absent (Gupta et al. 2024). Signatures of such internal processes are often studied with diverse terminologies: \u201cinternal states\u201d in systems neuroscience, \u201cinternal values or decisions\u201d in ecological approaches to neuroscience, or \u201cinternal cognitive processes\u201d in cognitive neuroscience. In this workshop, we will reconcile these parallel perspectives, establishing shared definitions and benchmarks, and fostering collaboration between experimental and theoretical communities working on different facets of internal states in the brain and behavior. In this workshop, we will mainly focus on two approaches to internal state: (1) Naturalistic behavioral and physiological readouts. Internal states are inferred from changes in movement kinematics, engagement, exploration\/exploitation structure, and multimodal neurophysiological signals. (2) Neural population dynamics, where internal states manifest as low-dimensional manifolds, attractors, or transitions between input-driven and autonomous regimes. By bringing these perspectives together, we will aim to define common principles, evaluation criteria, and open challenges for identifying internal states across neural systems.<\/p>\n<p data-start=\"2027\" data-end=\"2398\" data-is-last-node=\"\" data-is-only-node=\"\"><span style=\"font-size: 10pt;\"><em>References:<\/em><\/span><\/p>\n<p data-start=\"2027\" data-end=\"2398\" data-is-last-node=\"\" data-is-only-node=\"\"><span style=\"font-size: 10pt;\">Marques, J. C., Li, M., Schaak, D., Robson, D. N., &amp; Li, J. M. (2020). Internal state dynamics shape brainwide activity and foraging behaviour. Nature, 577(7789), 239-243.<\/span><br data-start=\"2198\" data-end=\"2201\" \/><span style=\"font-size: 10pt;\">Gupta, D., DePasquale, B., Kopec, C. D., &amp; Brody, C. D. (2024). Trial-history biases in evidence accumulation can give rise to apparent lapses in decision-making. Nature communications, 15(1), 662.<\/span><\/p>\n<\/div><\/div><\/div><\/section>\n<section class='av_toggle_section av-12cqaki-3b90700d06c0e56bf12757b7b6d36055' ><div role=\"tablist\" class=\"single_toggle\" data-tags=\"{All} \"  ><p id='toggle-toggle-id-30' data-fake-id='#toggle-id-30' class='toggler  av-title-above '  role='tab' tabindex='0' aria-controls='toggle-id-30' data-slide-speed=\"200\" data-title=\"Schedule: Monday, Sept 28\" data-title-open=\"\" data-aria_collapsed=\"Click to expand: Schedule: Monday, Sept 28\" data-aria_expanded=\"Click to collapse: Schedule: Monday, Sept 28\">Schedule: Monday, Sept 28<span class=\"toggle_icon\"><span class=\"vert_icon\"><\/span><span class=\"hor_icon\"><\/span><\/span><\/p><div id='toggle-id-30' aria-labelledby='toggle-toggle-id-30' role='region' class='toggle_wrap  av-title-above'  ><div class='toggle_content invers-color ' ><table width=\"959\">\n<tbody>\n<tr>\n<td width=\"94\"><strong>14:00<\/strong><\/td>\n<td width=\"425\">Jonathan Pillow<br \/>\n(Princeton University, USA)<\/td>\n<td width=\"441\"><em>TBA<\/em><\/td>\n<\/tr>\n<tr>\n<td width=\"94\"><strong>14:40<\/strong><\/td>\n<td width=\"425\">Jennifer Mengbo Li<br \/>\n(Max Planck Institute for Biological Cybernetics, Germany)<\/td>\n<td width=\"441\"><em>Brainwide joint analysis of neuromodulatory and cognitive networks during spatial learning in freely swimming zebrafish<\/em><\/td>\n<\/tr>\n<tr>\n<td width=\"94\"><strong>15:20<\/strong><\/td>\n<td width=\"425\">Shervin Safavi<br \/>\n(TUD Dresden University of Technology, Germany)<\/td>\n<td width=\"441\"><em>Neural, computational, and behavioral mechanisms of internal decision processes<\/em><\/td>\n<\/tr>\n<tr>\n<td width=\"94\"><strong>16:00<\/strong><\/td>\n<td colspan=\"2\" width=\"865\"><em>\u00a0Coffee break<\/em><\/td>\n<\/tr>\n<tr>\n<td width=\"94\"><strong>16:30<\/strong><\/td>\n<td width=\"425\">Diksha Gupta<br \/>\n(University College London, UK)<\/td>\n<td width=\"441\"><em>TBA<\/em><\/td>\n<\/tr>\n<tr>\n<td width=\"94\"><strong>17:10<\/strong><\/td>\n<td width=\"425\">Saurabh Vyas<br \/>\n(Carnegie Mellon University, USA)<\/td>\n<td width=\"441\"><em>Flexible problem solving in prefrontal cortex<\/em><\/td>\n<\/tr>\n<tr>\n<td width=\"94\"><strong>17:50<\/strong><\/td>\n<td width=\"425\">Jessica Cardin<br \/>\n(Yale University, USA)<\/td>\n<td width=\"441\"><em>TBA<\/em><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div><\/div><\/div><\/section>\n<section class='av_toggle_section av-10nlxlu-14c149452b2fb44faa91fcc969601631' ><div role=\"tablist\" class=\"single_toggle\" data-tags=\"{All} \"  ><p id='toggle-toggle-id-31' data-fake-id='#toggle-id-31' class='toggler  av-title-above '  role='tab' tabindex='0' aria-controls='toggle-id-31' data-slide-speed=\"200\" data-title=\"Schedule: Tuesday, Sept 29\" data-title-open=\"\" data-aria_collapsed=\"Click to expand: Schedule: Tuesday, Sept 29\" data-aria_expanded=\"Click to collapse: Schedule: Tuesday, Sept 29\">Schedule: Tuesday, Sept 29<span class=\"toggle_icon\"><span class=\"vert_icon\"><\/span><span class=\"hor_icon\"><\/span><\/span><\/p><div id='toggle-id-31' aria-labelledby='toggle-toggle-id-31' role='region' class='toggle_wrap  av-title-above'  ><div class='toggle_content invers-color ' ><table width=\"959\">\n<tbody>\n<tr>\n<td width=\"94\"><strong>8:30<\/strong><\/td>\n<td width=\"425\">Thomas Luo<br \/>\n(University of Utah, USA)<\/td>\n<td width=\"441\"><em>A neural marker of internal decision commitment<\/em><\/td>\n<\/tr>\n<tr>\n<td width=\"94\"><strong>9:10<\/strong><\/td>\n<td width=\"425\">Cindy Poo<br \/>\n(Allen Institute for Neural Dynamics, USA)<\/td>\n<td width=\"441\"><em>Adaptive behavior in a dynamic patch foraging environment<\/em><\/td>\n<\/tr>\n<tr>\n<td width=\"94\"><strong>10:00<\/strong><\/td>\n<td colspan=\"2\" width=\"865\"><em>\u00a0Coffee break<\/em><\/td>\n<\/tr>\n<tr>\n<td width=\"94\"><strong>10:30<\/strong><\/td>\n<td width=\"425\">Tim Buschman<br \/>\n(Princeton University, USA)<\/td>\n<td width=\"441\"><em>TBA<\/em><\/td>\n<\/tr>\n<tr>\n<td width=\"94\"><strong>11:10<\/strong><\/td>\n<td width=\"425\">Roxana Zeraati<br \/>\n(Max Planck Institute for Biological Cybernetics, Germany)<\/td>\n<td width=\"441\"><em>Optimal foraging under naturalistic temporal dynamics<\/em><\/td>\n<\/tr>\n<tr>\n<td width=\"94\"><strong>11:50<\/strong><\/td>\n<td colspan=\"2\" width=\"865\"><em>Speaker panel<\/em><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div><\/div><\/div><\/section>\n<\/div><\/p><\/div><div  class='flex_column av-10bueoy-2497a4ec0ea667f2888387e26901fe57 av_one_full  avia-builder-el-63  el_after_av_one_full  el_before_av_one_full  first flex_column_div  column-top-margin'     ><p><section  class='av_textblock_section av-xgiexu-5036cd8e5bc1ae16a82cecd77f0c89a8 '  ><div class='avia_textblock' ><p><span style=\"color: #000000;\"><strong>Reconciling biology and function in large-scale brain models<\/strong><em><br \/>\nOrganizers:<\/em><br \/>\n<\/span><span style=\"color: #000000;\">Sacha van Albada | Forschungszentrum J\u00fclich &amp; University of Cologne, Germany<br \/>\nHans Ekkehard Plesser | Norwegian University of Life Sciences, Norway<\/span><em><br \/>\n<\/em><\/p>\n<\/div><\/section><br \/>\n<div  class='togglecontainer av-wh29ky-ac8bc9f9fbd47957a1770959f373ff2a  avia-builder-el-65  el_after_av_textblock  avia-builder-el-last  toggle_close_all' >\n<section class='av_toggle_section av-umvxoy-b2f694ff92133ad1606de148cec958a5' ><div role=\"tablist\" class=\"single_toggle\" data-tags=\"{All} \"  ><p id='toggle-toggle-id-32' data-fake-id='#toggle-id-32' class='toggler  av-title-above '  role='tab' tabindex='0' aria-controls='toggle-id-32' data-slide-speed=\"200\" data-title=\"Description\" data-title-open=\"\" data-aria_collapsed=\"Click to expand: Description\" data-aria_expanded=\"Click to collapse: Description\">Description<span class=\"toggle_icon\"><span class=\"vert_icon\"><\/span><span class=\"hor_icon\"><\/span><\/span><\/p><div id='toggle-id-32' aria-labelledby='toggle-toggle-id-32' role='region' class='toggle_wrap  av-title-above'  ><div class='toggle_content invers-color ' ><p>Integrating biological constraints with realistic functional properties in neural network models\u2014from learning to behavioral output\u2014is a long-standing challenge in computational neuroscience. \u201cTop-down\u201d models of learning and function generally incorporate some biologically plausible aspects, but usually not to an extent that allows for testing these models directly against biological data or for disambiguating between potential underlying biological mechanisms. With \u201cbottom-up\u201d models systematically derived from biological data, on the other hand, scientists rarely investigate which network functions the models may support. However, recent years have brought considerable progress in the development of brain models that capture the cytoarchitecture, connectivity, and electrophysiology of large portions of the mammalian brain at cellular resolution. While the large-scale nature of these models is not a unique requirement for their biological plausibility, small models tend to focus on a few select phenomena besides distorting aspects of dynamics and learning, highlighting a need to move beyond minimal models. Integrative large-scale models can, in principle, serve as increasingly reliable test beds for theories of learning and information processing in the brain. The problem is then how we can reconcile the bottom-up and top-down approaches to arrive at an integrated understanding of brain function. In this workshop, we will address how to integrate structure, dynamics, and function in network models by considering the following questions: What are the scientific, technical, and organizational hurdles in bringing together biological constraints with realistic network function? What are some promising approaches? How can we tackle the problem in a way that not only reproduces available data but also generates reliable predictions? How can we arrive at well-constrained models suitable for disambiguating between different theories of brain function? How can we adapt learning rules proposed in more artificial settings (e.g., non-spiking models, feedforward networks, small-scale networks) to biological constraints? How can we develop integrative models that combine various functional properties? Guided by these questions, we hope to take a step forward toward a practical work program for reconciling biology and function in large-scale brain models.<\/p>\n<\/div><\/div><\/div><\/section>\n<section class='av_toggle_section av-taf91u-8b538b9f0712f5dc3aae5ec4ce1d679f' ><div role=\"tablist\" class=\"single_toggle\" data-tags=\"{All} \"  ><p id='toggle-toggle-id-33' data-fake-id='#toggle-id-33' class='toggler  av-title-above '  role='tab' tabindex='0' aria-controls='toggle-id-33' data-slide-speed=\"200\" data-title=\"Schedule: Monday, Sept 28\" data-title-open=\"\" data-aria_collapsed=\"Click to expand: Schedule: Monday, Sept 28\" data-aria_expanded=\"Click to collapse: Schedule: Monday, Sept 28\">Schedule: Monday, Sept 28<span class=\"toggle_icon\"><span class=\"vert_icon\"><\/span><span class=\"hor_icon\"><\/span><\/span><\/p><div id='toggle-id-33' aria-labelledby='toggle-toggle-id-33' role='region' class='toggle_wrap  av-title-above'  ><div class='toggle_content invers-color ' ><table width=\"959\">\n<tbody>\n<tr>\n<td width=\"94\"><strong>14:00<\/strong><\/td>\n<td width=\"425\">Jun Igarashi<br \/>\n(RIKEN, Japan)<\/td>\n<td width=\"441\"><em>Oscillatory activity in large-scale simulations of connectome-constrained spiking neural network models of the cerebral cortex<\/em><\/td>\n<\/tr>\n<tr>\n<td width=\"94\"><strong>14:30<\/strong><\/td>\n<td width=\"425\">Lies Van Dael<br \/>\n(Forschungszentrum J\u00fclich, Germany)<\/td>\n<td width=\"441\"><em>Multi-area spiking network model of macaque cortex with joint excitatory\/inhibitory clusters<\/em><\/td>\n<\/tr>\n<tr>\n<td width=\"94\"><strong>15:00<\/strong><\/td>\n<td width=\"425\">J\u00e1n Antol\u00edk<br \/>\n(Charles University, Czech Republic)<\/td>\n<td width=\"441\"><em>Bridging spontaneous, visually evoked, and artificially evoked states with a single underlying substrate<\/em><\/td>\n<\/tr>\n<tr>\n<td width=\"94\"><strong>15:30<\/strong><\/td>\n<td colspan=\"2\" width=\"865\"><em>Discussion<\/em><\/td>\n<\/tr>\n<tr>\n<td width=\"94\"><strong>16:00<\/strong><\/td>\n<td colspan=\"2\" width=\"865\"><em>\u00a0Coffee break<\/em><\/td>\n<\/tr>\n<tr>\n<td width=\"94\"><strong>16:30<\/strong><\/td>\n<td width=\"425\">Francesca Mastrogiuseppe<br \/>\n(Scuola Internazionale Superiore di Studi Avanzati (SISSA), Italy)<\/td>\n<td width=\"441\"><em>Input-dependent directionality of interactions between cortical areas <\/em><\/td>\n<\/tr>\n<tr>\n<td width=\"94\"><strong>17:00<\/strong><\/td>\n<td width=\"425\">Caroline Haimerl<br \/>\n(Champalimaud Foundation, Portugal)<\/td>\n<td width=\"441\"><em>From action to abstraction: multiscale representation learning for perception and action<\/em><\/td>\n<\/tr>\n<tr>\n<td width=\"94\"><strong>17:30<\/strong><\/td>\n<td width=\"425\">Chris Eliasmith<br \/>\n(University of Waterloo, Canada)<\/td>\n<td width=\"441\"><em>Task-performing models of multiple connected brain regions<\/em><\/td>\n<\/tr>\n<tr>\n<td width=\"94\"><strong>18:00<\/strong><\/td>\n<td colspan=\"2\" width=\"865\"><em>Discussion<\/em><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div><\/div><\/div><\/section>\n<section class='av_toggle_section av-rx3paq-8cd1f2bec16e00f0dc1a224ef68f477d' ><div role=\"tablist\" class=\"single_toggle\" data-tags=\"{All} \"  ><p id='toggle-toggle-id-34' data-fake-id='#toggle-id-34' class='toggler  av-title-above '  role='tab' tabindex='0' aria-controls='toggle-id-34' data-slide-speed=\"200\" data-title=\"Schedule: Tuesday, Sept 29\" data-title-open=\"\" data-aria_collapsed=\"Click to expand: Schedule: Tuesday, Sept 29\" data-aria_expanded=\"Click to collapse: Schedule: Tuesday, Sept 29\">Schedule: Tuesday, Sept 29<span class=\"toggle_icon\"><span class=\"vert_icon\"><\/span><span class=\"hor_icon\"><\/span><\/span><\/p><div id='toggle-id-34' aria-labelledby='toggle-toggle-id-34' role='region' class='toggle_wrap  av-title-above'  ><div class='toggle_content invers-color ' ><table width=\"959\">\n<tbody>\n<tr>\n<td width=\"94\"><strong>8:30<\/strong><\/td>\n<td width=\"425\">Jakob Macke<br \/>\n(University of T\u00fcbingen, Germany)<\/td>\n<td width=\"441\"><em>Learning mechanistic models from neurons to networks to computations <\/em><\/td>\n<\/tr>\n<tr>\n<td width=\"94\"><strong>9:00<\/strong><\/td>\n<td width=\"425\">Mihai A. Petrovici<br \/>\n(University of Bern, Switzerland)<\/td>\n<td width=\"441\"><em>Error transport in cortical microcircuits<\/em><\/td>\n<\/tr>\n<tr>\n<td width=\"94\"><strong>9:30<\/strong><\/td>\n<td width=\"425\">Mario Senden<br \/>\n(Maastricht University, The Netherlands)<\/td>\n<td width=\"441\"><em>Reconciling biology and function via biophysics-informed deep learning <\/em><\/td>\n<\/tr>\n<tr>\n<td width=\"94\"><strong>10:00<\/strong><\/td>\n<td colspan=\"2\" width=\"865\"><em>\u00a0Coffee break<\/em><\/td>\n<\/tr>\n<tr>\n<td width=\"94\"><strong>10:30<\/strong><\/td>\n<td width=\"425\">Daniela Gandolfi<br \/>\n(University of Modena and Reggio Emilia, Italy)<\/td>\n<td width=\"441\"><em>Linking structure to dynamics and learning in a large-scale cerebellar model <\/em><\/td>\n<\/tr>\n<tr>\n<td width=\"94\"><strong>11:00<\/strong><\/td>\n<td width=\"425\">Stefan Mihalas<br \/>\n(Allen Institute, USA)<\/td>\n<td width=\"441\"><em>Differentiable biorealistic model of a cortical microcircuit <\/em><\/td>\n<\/tr>\n<tr>\n<td width=\"94\"><strong>11:30<\/strong><\/td>\n<td width=\"425\">Markus Diesmann<br \/>\n(Forschungszentrum J\u00fclich, Germany)<\/td>\n<td width=\"441\"><em>Building on models: past, present, perspectives<\/em><\/td>\n<\/tr>\n<tr>\n<td width=\"94\"><strong>12:00<\/strong><\/td>\n<td colspan=\"2\" width=\"865\"><em>Discussion<\/em><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div><\/div><\/div><\/section>\n<\/div><\/p><\/div><div  class='flex_column av-p3w0gi-521e15bdc0533d00618b620c91e0012f av_one_full  avia-builder-el-66  el_after_av_one_full  el_before_av_one_full  first flex_column_div  column-top-margin'     ><p><section  class='av_textblock_section av-oqitxe-5c2f84cd9a866103ec79dffdfe2f39eb '  ><div class='avia_textblock' ><p><span style=\"color: #000000;\"><strong>Quo vadis, neural network theory?<\/strong><br \/>\n<em>Organizers:<\/em><br \/>\n<\/span><span style=\"color: #000000;\">Alexander van Meegen | RWTH Aachen University, Germany<br \/>\nJacob Zavatone-Veth | Harvard University, USA<\/span><em><br \/>\n<\/em><\/p>\n<\/div><\/section><br \/>\n<div  class='togglecontainer av-my58aa-14e2d4675edc9e57b7b5f2b10c160281  avia-builder-el-68  el_after_av_textblock  avia-builder-el-last  toggle_close_all' >\n<section class='av_toggle_section av-knch6q-a2638291704b0a6d76f7b19755493bef' ><div role=\"tablist\" class=\"single_toggle\" data-tags=\"{All} \"  ><p id='toggle-toggle-id-35' data-fake-id='#toggle-id-35' class='toggler  av-title-above '  role='tab' tabindex='0' aria-controls='toggle-id-35' data-slide-speed=\"200\" data-title=\"Description\" data-title-open=\"\" data-aria_collapsed=\"Click to expand: Description\" data-aria_expanded=\"Click to collapse: Description\">Description<span class=\"toggle_icon\"><span class=\"vert_icon\"><\/span><span class=\"hor_icon\"><\/span><\/span><\/p><div id='toggle-id-35' aria-labelledby='toggle-toggle-id-35' role='region' class='toggle_wrap  av-title-above'  ><div class='toggle_content invers-color ' ><p>Driven by the remarkable progress of artificial intelligence, the theory of artificial neural networks has advanced substantially in recent years. These developments promise a deeper understanding of neural networks more broadly, including biological neural networks. Theoretical insights may inspire novel analysis methods to address the growing deluge of data, helping us make sense of large-scale recordings and reconstructions. Furthermore, they may open new perspectives and questions, motivating new experiments as well as reinterpretations of existing data. The guiding question of this workshop is how theoretical advances for artificial networks can be translated into the greatest impact on neuroscience. As a first step, the current status quo will be assessed through invited speakers who are at the forefront of neural network theory. All speakers will conclude their talks with a view toward the future: how do they envision the impact of their work? These visions will then be critically discussed with the audience, considering both the promises and the potential pitfalls. As a synthesis, the workshop will conclude with a panel discussion focused on the broader perspective.<\/p>\n<\/div><\/div><\/div><\/section>\n<section class='av_toggle_section av-1vdcde-81e69a77fa53ab68cea575455944d922' ><div role=\"tablist\" class=\"single_toggle\" data-tags=\"{All} \"  ><p id='toggle-toggle-id-36' data-fake-id='#toggle-id-36' class='toggler  av-title-above '  role='tab' tabindex='0' aria-controls='toggle-id-36' data-slide-speed=\"200\" data-title=\"Schedule: Monday, Sept 28\" data-title-open=\"\" data-aria_collapsed=\"Click to expand: Schedule: Monday, Sept 28\" data-aria_expanded=\"Click to collapse: Schedule: Monday, Sept 28\">Schedule: Monday, Sept 28<span class=\"toggle_icon\"><span class=\"vert_icon\"><\/span><span class=\"hor_icon\"><\/span><\/span><\/p><div id='toggle-id-36' aria-labelledby='toggle-toggle-id-36' role='region' class='toggle_wrap  av-title-above'  ><div class='toggle_content invers-color ' ><table width=\"959\">\n<tbody>\n<tr>\n<td width=\"94\"><strong>14:00<\/strong><\/td>\n<td width=\"425\">SueYeon Chung<br \/>\n(Harvard University, USA)<\/td>\n<td width=\"441\"><em>Neural population geometry and optimal coding of tasks with shared latent structure<\/em><\/td>\n<\/tr>\n<tr>\n<td width=\"94\"><strong>14:40<\/strong><\/td>\n<td width=\"425\">Valentin Schmutz<br \/>\n(University of Oxford, UK)<\/td>\n<td width=\"441\"><em>High-dimensional neuronal activity from low-dimensional latent dynamics: a solvable model<\/em><\/td>\n<\/tr>\n<tr>\n<td width=\"94\"><strong>15:20<\/strong><\/td>\n<td width=\"425\">Stefano Sarao Mannelli<br \/>\n(Chalmers University of Technology, Sweden)<\/td>\n<td width=\"441\"><em>A theory of initialisation\u2019s impact on specialisation<\/em><\/td>\n<\/tr>\n<tr>\n<td width=\"94\"><strong>16:00<\/strong><\/td>\n<td colspan=\"2\" width=\"865\"><em>\u00a0Coffee break<\/em><\/td>\n<\/tr>\n<tr>\n<td width=\"94\"><strong>16:30<\/strong><\/td>\n<td width=\"425\">Agostina Palmigiano<br \/>\n(University College London, UK)<\/td>\n<td width=\"441\"><em>A unified theory of feature learning in RNNs and DNNs<\/em><\/td>\n<\/tr>\n<tr>\n<td width=\"94\"><strong>17:10<\/strong><\/td>\n<td width=\"425\">Lorenzo Tiberi<br \/>\n(Harvard University, USA)<\/td>\n<td width=\"441\"><em>Manifold geometry underlies a unified code for category and category-independent features<\/em><\/td>\n<\/tr>\n<tr>\n<td width=\"94\"><strong>17:50<\/strong><\/td>\n<td width=\"425\">Sebastian Goldt<br \/>\n(Scuola Internazionale Superiore di Studi Avanzati (SISSA), Italy)<\/td>\n<td width=\"441\"><em>Diverse perceptual biases emerge from Hebbian plasticity in a recurrent neural network model<\/em><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div><\/div><\/div><\/section>\n<section class='av_toggle_section av-hzf476-cfc94b20dd7309d38b9a0ad4d4c13e24' ><div role=\"tablist\" class=\"single_toggle\" data-tags=\"{All} \"  ><p id='toggle-toggle-id-37' data-fake-id='#toggle-id-37' class='toggler  av-title-above '  role='tab' tabindex='0' aria-controls='toggle-id-37' data-slide-speed=\"200\" data-title=\"Schedule: Tuesday, Sept 29\" data-title-open=\"\" data-aria_collapsed=\"Click to expand: Schedule: Tuesday, Sept 29\" data-aria_expanded=\"Click to collapse: Schedule: Tuesday, Sept 29\">Schedule: Tuesday, Sept 29<span class=\"toggle_icon\"><span class=\"vert_icon\"><\/span><span class=\"hor_icon\"><\/span><\/span><\/p><div id='toggle-id-37' aria-labelledby='toggle-toggle-id-37' role='region' class='toggle_wrap  av-title-above'  ><div class='toggle_content invers-color ' ><table width=\"959\">\n<tbody>\n<tr>\n<td width=\"94\"><strong>8:30<\/strong><\/td>\n<td width=\"425\">Tatiana Engel<br \/>\n(Princeton University, USA)<\/td>\n<td width=\"441\"><em>The dynamics and geometry of choice in the premotor cortex<\/em><\/td>\n<\/tr>\n<tr>\n<td width=\"94\"><strong>9:10<\/strong><\/td>\n<td width=\"425\">David Clark<br \/>\n(Harvard University, USA)<\/td>\n<td width=\"441\"><em>Structure, disorder, and dynamics in task-trained recurrent neural circuits<\/em><\/td>\n<\/tr>\n<tr>\n<td width=\"94\"><strong>10:00<\/strong><\/td>\n<td colspan=\"2\" width=\"865\"><em>\u00a0Coffee break<\/em><\/td>\n<\/tr>\n<tr>\n<td width=\"94\"><strong>10:30<\/strong><\/td>\n<td width=\"425\">Francesca Mignacco<br \/>\n(Princeton University, USA)<\/td>\n<td width=\"441\"><em>Optimal protocols for continual learning via statistical physics and control theory<\/em><\/td>\n<\/tr>\n<tr>\n<td width=\"94\"><strong>11:10<\/strong><\/td>\n<td width=\"425\">Kanaka Rajan<br \/>\n(Harvard University, USA)<\/td>\n<td width=\"441\"><em>Measuring and controlling solution degeneracy across task-trained recurrent neural networks<\/em><\/td>\n<\/tr>\n<tr>\n<td width=\"94\"><strong>11:50<\/strong><\/td>\n<td colspan=\"2\" width=\"865\"><em>Panel discussion<\/em><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div><\/div><\/div><\/section>\n<\/div><\/p><\/div><div  class='flex_column av-g6yate-f862f4bdff79d17d3f37ab499431060f av_one_full  avia-builder-el-69  el_after_av_one_full  el_before_av_hr  first flex_column_div  column-top-margin'     ><p><section  class='av_textblock_section av-eso8s2-4da4675dbdc831bbbd74542141dbcaa8 '  ><div class='avia_textblock' ><p><span style=\"color: #000000;\"><strong>Going wild \u2013 Cognition, behaviour, and neural processing in natural and naturalistic settings<\/strong><br \/>\n<em>Organizers:<\/em><br \/>\n<\/span><span style=\"color: #000000;\">Fred Wolf | Max Planck Institute for Dynamics and Self-Organization &amp; University of G\u00f6ttingen, Germany<br \/>\nJan Benda | University of T\u00fcbingen, Germany<br \/>\n<\/span><span style=\"color: #000000;\">Kerstin Schmidt | Federal University of Rio Grande do Norte (UFRN Natal, Brazil)<br \/>\n<\/span><\/p>\n<\/div><\/section><br \/>\n<div  class='togglecontainer av-czicma-ad0532b67a1cdde00543d8f12db0ff2b  avia-builder-el-71  el_after_av_textblock  avia-builder-el-last  toggle_close_all' >\n<section class='av_toggle_section av-aqc402-7769e6140ef5c0d2daa1ad6eb76ffe12' ><div role=\"tablist\" class=\"single_toggle\" data-tags=\"{All} \"  ><p id='toggle-toggle-id-38' data-fake-id='#toggle-id-38' class='toggler  av-title-above '  role='tab' tabindex='0' aria-controls='toggle-id-38' data-slide-speed=\"200\" data-title=\"Description\" data-title-open=\"\" data-aria_collapsed=\"Click to expand: Description\" data-aria_expanded=\"Click to collapse: Description\">Description<span class=\"toggle_icon\"><span class=\"vert_icon\"><\/span><span class=\"hor_icon\"><\/span><\/span><\/p><div id='toggle-id-38' aria-labelledby='toggle-toggle-id-38' role='region' class='toggle_wrap  av-title-above'  ><div class='toggle_content invers-color ' ><p>Animal and human brains evolved to process information and organize behaviour in complex dynamic ecosystems such as primary forests, primal river basins, and similar habitats set in a world that was largely not yet shaped by near-universal anthropogenic change. Over the past decade, neurotechnology, optical instrumentation, and revolutionary advances in information technology have opened exciting avenues to observe, interact with, and quantitatively measure behavior, physiology, and neural function in unrestrained animals in complex environments. Perhaps the most revolutionary current frontier in this research direction is driven by studies of cognition, behaviour, and neural function in maximally naturalistic settings and wild natural habitats. Increasingly, a new generation of study designs integrates field-based and lab-based approaches to uncover the mechanisms and dynamics of exactly the behaviours that animal brains evolved to perform best. This emerging field of \u201cnatural neuroscience\u201d studies raises novel and unique challenges in a wide range of domains, from big data methods to the development of mobile and miniature devices and to computational modelling and fundamental theory. The workshop \u201cGoing Wild \u2013 Cognition, Behaviour and Neural Processing in Natural and Naturalistic Settings\u201d is designed to provide a platform for projects at the forefront of this emerging field and to bring together experts rooted in behavioural ecology and evolution with computational and systems neuroscientists. Topics covered range from primate cognition, navigation, and social foraging to prey capture and the coordination of animal group behaviour. The studies presented, as well as a dedicated discussion session, will offer ample opportunities to learn about and exchange on the novel computational, systems, and data-science challenges arising with this emergent research direction. On an equal footing, we will facilitate discussions on fundamental questions such as \u201cWhat really defines an \u2018ecologically valid\u2019 behavioural task in lab and field research?\u201d and \u201cAre there novel ethical dimensions for responsible animal research arising in studies conducted in wild and natural settings?\u201d.<\/p>\n<\/div><\/div><\/div><\/section>\n<section class='av_toggle_section av-9f35fm-26d09a454c8e7d3942e12c440f022cf6' ><div role=\"tablist\" class=\"single_toggle\" data-tags=\"{All} \"  ><p id='toggle-toggle-id-39' data-fake-id='#toggle-id-39' class='toggler  av-title-above '  role='tab' tabindex='0' aria-controls='toggle-id-39' data-slide-speed=\"200\" data-title=\"Schedule: Monday, Sept 28\" data-title-open=\"\" data-aria_collapsed=\"Click to expand: Schedule: Monday, Sept 28\" data-aria_expanded=\"Click to collapse: Schedule: Monday, Sept 28\">Schedule: Monday, Sept 28<span class=\"toggle_icon\"><span class=\"vert_icon\"><\/span><span class=\"hor_icon\"><\/span><\/span><\/p><div id='toggle-id-39' aria-labelledby='toggle-toggle-id-39' role='region' class='toggle_wrap  av-title-above'  ><div class='toggle_content invers-color ' ><p><strong>Session: Mammals<\/strong><\/p>\n<table width=\"959\">\n<tbody>\n<tr>\n<td width=\"94\"><strong>14:00<\/strong><\/td>\n<td width=\"425\">Jan Benda &amp; Fred Wolf<br \/>\n(University of T\u00fcbingen \/ Max Planck Institute for Dynamics and Self-Organization &amp; University of G\u00f6ttingen, Germany)<\/td>\n<td width=\"441\"><em>Introduction<\/em><\/td>\n<\/tr>\n<tr>\n<td width=\"94\"><strong>14:10<\/strong><\/td>\n<td width=\"425\">Daniel Takahashi<br \/>\n(Federal University of Rio Grande do Norte (UFRN Natal), Brazil)<\/td>\n<td width=\"441\"><em>Multiscale embedding of neural signal for generative analysis of natural behavior<\/em><\/td>\n<\/tr>\n<tr>\n<td width=\"94\"><strong>14:50<\/strong><\/td>\n<td width=\"425\">Irene Lacal<br \/>\n(German Primate Center &#8211; Leibniz Institute for Primate Research (DPZ), Germany)<\/td>\n<td width=\"441\"><em>Dynamics and strategies of primate group foraging in field and lab<\/em><\/td>\n<\/tr>\n<tr>\n<td width=\"94\"><strong>15:25<\/strong><\/td>\n<td width=\"425\">Cory Miller<br \/>\n(University of California, San Diego, USA)<\/td>\n<td width=\"441\"><em>Active vision in marmoset arboreal prey capture<\/em><\/td>\n<\/tr>\n<tr>\n<td width=\"94\"><strong>16:00<\/strong><\/td>\n<td colspan=\"2\" width=\"865\"><em>\u00a0Coffee break<\/em><\/td>\n<\/tr>\n<tr>\n<td width=\"94\"><strong>16:30<\/strong><\/td>\n<td width=\"425\">Kerstin Schmidt<br \/>\n(Federal University of Rio Grande do Norte (UFRN Natal), Brazil)<\/td>\n<td width=\"441\"><em>Adapting the visual cortex to the natural world: Organization and diversity<\/em><\/td>\n<\/tr>\n<tr>\n<td width=\"94\"><strong>17:00<\/strong><\/td>\n<td width=\"425\">Ilia Leonov<br \/>\n(Max Planck Institute for Dynamics and Self-Organization &amp; University of G\u00f6ttingen, Germany)<\/td>\n<td width=\"441\"><em>Decoding the dynamics of free goal-directed behavior<\/em><\/td>\n<\/tr>\n<tr>\n<td width=\"94\"><strong>17:30<\/strong><\/td>\n<td width=\"425\">Ahmed El Hady<br \/>\n(Max Planck Institute for Animal Behavior, Germany)<\/td>\n<td width=\"441\"><em>Computational principles of foraging decision making in the wild<\/em><\/td>\n<\/tr>\n<tr>\n<td width=\"94\"><strong>18:00<\/strong><\/td>\n<td width=\"425\">Ali Nourizonoz<br \/>\n(University of Geneva, Switzerland)<\/td>\n<td width=\"441\"><em>Optimized planning of a high-risk behavior in a miniature nocturnal primate<\/em><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div><\/div><\/div><\/section>\n<section class='av_toggle_section av-7plpsi-399f4711154223505708e21af74eb90d' ><div role=\"tablist\" class=\"single_toggle\" data-tags=\"{All} \"  ><p id='toggle-toggle-id-40' data-fake-id='#toggle-id-40' class='toggler  av-title-above '  role='tab' tabindex='0' aria-controls='toggle-id-40' data-slide-speed=\"200\" data-title=\"Schedule: Tuesday, Sept 29\" data-title-open=\"\" data-aria_collapsed=\"Click to expand: Schedule: Tuesday, Sept 29\" data-aria_expanded=\"Click to collapse: Schedule: Tuesday, Sept 29\">Schedule: Tuesday, Sept 29<span class=\"toggle_icon\"><span class=\"vert_icon\"><\/span><span class=\"hor_icon\"><\/span><\/span><\/p><div id='toggle-id-40' aria-labelledby='toggle-toggle-id-40' role='region' class='toggle_wrap  av-title-above'  ><div class='toggle_content invers-color ' ><table width=\"959\">\n<tbody>\n<tr>\n<td width=\"94\"><strong>8:30<\/strong><\/td>\n<td width=\"425\">Jason Kerr<br \/>\n(Max Planck Institute for Neurobiology of Behavior &#8211; caesar, Germany)<\/td>\n<td width=\"441\"><em>Vision for action in hunting Harris hawks<\/em><\/td>\n<\/tr>\n<tr>\n<td width=\"94\"><strong>9:15<\/strong><\/td>\n<td width=\"425\">Jerome Baron<br \/>\n(Federal University of Minas Gerais, Brazil)<\/td>\n<td width=\"441\"><em>Evolution of skilled hindlimb movements in birds<\/em><\/td>\n<\/tr>\n<tr>\n<td width=\"94\"><strong>10:00<\/strong><\/td>\n<td colspan=\"2\" width=\"865\"><em>\u00a0Coffee break<\/em><\/td>\n<\/tr>\n<tr>\n<td width=\"94\"><strong>10:30<\/strong><\/td>\n<td width=\"425\">David de Santana<br \/>\n(Museu Paraense Em\u00edlio Goeldi, Brazil)<\/td>\n<td width=\"441\"><em>Adaptive radiation without anatomical radiation in electric eels<\/em><\/td>\n<\/tr>\n<tr>\n<td width=\"94\"><strong>11:10<\/strong><\/td>\n<td width=\"425\">Lena St\u00f6ckl &amp; Jan Benda<br \/>\n(University of T\u00fcbingen, Germany)<\/td>\n<td width=\"441\"><em>Musical electric fish &#8211; from neotropical rivers into the lab<\/em><\/td>\n<\/tr>\n<tr>\n<td width=\"94\"><strong>11:50<\/strong><\/td>\n<td colspan=\"2\" width=\"865\"><em>General discussion<\/em><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div><\/div><\/div><\/section>\n<\/div><\/p><\/div><div  class='hr av-60c7n6-96969458a2d6be73541f840a7a7be8c3 hr-default  avia-builder-el-72  el_after_av_one_full  el_before_av_one_full '><span class='hr-inner '><span class=\"hr-inner-style\"><\/span><\/span><\/div>\n<div  class='flex_column av-3yttky-fb46a97e5aa55ae9f57a0960c66caa20 av_one_full  avia-builder-el-73  el_after_av_hr  avia-builder-el-last  first flex_column_div  '     ><section  class='av_textblock_section av-34wd02-0671f2432266cc29a7acded886623666 '  ><div class='avia_textblock' ><p>For further questions, please contact us at: <a href=\"mailto:bernstein.conference@fz-juelich.de\">bernstein.conference@fz-juelich.de<\/a>.<\/p>\n<\/div><\/section><\/div>\n","protected":false},"excerpt":{"rendered":"","protected":false},"author":1,"featured_media":0,"parent":6843,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"class_list":["post-6837","page","type-page","status-publish","hentry"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v25.9 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Satellite Workshops &#8211; 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