{"id":10115,"date":"2021-06-28T13:50:58","date_gmt":"2021-06-28T11:50:58","guid":{"rendered":"http:\/\/bernstein.mylapo.de\/?page_id=10115"},"modified":"2026-03-05T11:40:58","modified_gmt":"2026-03-05T10:40:58","slug":"bernstein-conference-2018","status":"publish","type":"page","link":"https:\/\/bernstein-network.de\/en\/bernstein-conference\/past-future-bernstein-conferences\/bernstein-conference-2018\/","title":{"rendered":"Bernstein Conference 2018"},"content":{"rendered":"<div id='av_section_1'  class='avia-section av-b4ovel-06213fab716bb7bdfbbdc021fdd14c9b 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-10115'><div class='entry-content-wrapper clearfix'>\n\n<style type=\"text\/css\" data-created_by=\"avia_inline_auto\" id=\"style-css-av-9286rx-1689bf803501c4a123fe3a9ff3202e98\">\n.flex_column.av-9286rx-1689bf803501c4a123fe3a9ff3202e98{\nborder-radius:0px 0px 0px 0px;\npadding:0px 0px 0px 0px;\n}\n<\/style>\n<div  class='flex_column av-9286rx-1689bf803501c4a123fe3a9ff3202e98 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-pdyqjm-11ed7dfbfa4009e3c433b806ba5165a2\">\n#top .av-special-heading.av-pdyqjm-11ed7dfbfa4009e3c433b806ba5165a2{\npadding-bottom:10px;\n}\nbody .av-special-heading.av-pdyqjm-11ed7dfbfa4009e3c433b806ba5165a2 .av-special-heading-tag .heading-char{\nfont-size:25px;\n}\n.av-special-heading.av-pdyqjm-11ed7dfbfa4009e3c433b806ba5165a2 .av-subheading{\nfont-size:15px;\n}\n<\/style>\n<div  class='av-special-heading av-pdyqjm-11ed7dfbfa4009e3c433b806ba5165a2 av-special-heading-h1 blockquote modern-quote  avia-builder-el-2  el_before_av_image  avia-builder-el-first '><h1 class='av-special-heading-tag '  >Bernstein Conference 2018<\/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-gramt-956ba75ede02e350da566b155ab870f3\">\n.avia-image-container.av-gramt-956ba75ede02e350da566b155ab870f3 img.avia_image{\nbox-shadow:none;\n}\n.avia-image-container.av-gramt-956ba75ede02e350da566b155ab870f3 .av-image-caption-overlay-center{\ncolor:#ffffff;\n}\n<\/style>\n<div  class='avia-image-container av-gramt-956ba75ede02e350da566b155ab870f3 av-styling-no-styling avia-align-center  avia-builder-el-3  el_after_av_heading  avia-builder-el-last '  ><div class=\"avia-image-container-inner\"><div class=\"avia-image-overlay-wrap\"><img decoding=\"async\" fetchpriority=\"high\" class='wp-image-10090 avia-img-lazy-loading-not-10090 avia_image ' src=\"https:\/\/bernstein-network.de\/wp-content\/uploads\/2021\/06\/BC18-1030x730.jpg\" alt='' title='BC18'  height=\"730\" width=\"1030\" srcset=\"https:\/\/bernstein-network.de\/wp-content\/uploads\/2021\/06\/BC18-1030x730.jpg 1030w, https:\/\/bernstein-network.de\/wp-content\/uploads\/2021\/06\/BC18-300x213.jpg 300w, https:\/\/bernstein-network.de\/wp-content\/uploads\/2021\/06\/BC18-768x545.jpg 768w, https:\/\/bernstein-network.de\/wp-content\/uploads\/2021\/06\/BC18-260x185.jpg 260w, https:\/\/bernstein-network.de\/wp-content\/uploads\/2021\/06\/BC18-705x500.jpg 705w, https:\/\/bernstein-network.de\/wp-content\/uploads\/2021\/06\/BC18.jpg 1128w\" sizes=\"(max-width: 1030px) 100vw, 1030px\" \/><\/div><\/div><\/div><\/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-27i0jy-cf09d2d6f81b87422a4cec92229a0caf\">\n.avia-section.av-27i0jy-cf09d2d6f81b87422a4cec92229a0caf{\nbackground-color:#f5f7fa;\nbackground-image:unset;\n}\n<\/style>\n<div id='conferenceiconbox'  class='avia-section av-27i0jy-cf09d2d6f81b87422a4cec92229a0caf main_color avia-section-large avia-no-border-styling  avia-builder-el-4  el_after_av_section  el_before_av_section  avia-bg-style-scroll av-minimum-height av-minimum-height-25 av-height-25  container_wrap fullsize'   data-av_minimum_height_pc='25' data-av_min_height_opt='25'><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-10115'><div class='entry-content-wrapper clearfix'>\n\n<style type=\"text\/css\" data-created_by=\"avia_inline_auto\" id=\"style-css-av-5dt4eq-c445819d45749bf7f4effb421acb348e\">\n.flex_column.av-5dt4eq-c445819d45749bf7f4effb421acb348e{\nborder-radius:0px 0px 0px 0px;\npadding:0px 0px 0px 0px;\n}\n<\/style>\n<div  class='flex_column av-5dt4eq-c445819d45749bf7f4effb421acb348e av_one_third  avia-builder-el-5  el_before_av_one_third  avia-builder-el-first  first flex_column_div av-zero-column-padding  '     ><p>\n<style type=\"text\/css\" data-created_by=\"avia_inline_auto\" id=\"style-css-av-kn64gdx5-a2d7f3078262234f13ba648fe01a42bd\">\n.iconbox.av-kn64gdx5-a2d7f3078262234f13ba648fe01a42bd .iconbox_icon{\nbackground-color:#f5f7fa;\nborder:1px solid #f5f7fa;\ncolor:#004c93;\n}\n.iconbox.av-kn64gdx5-a2d7f3078262234f13ba648fe01a42bd .iconbox_icon.avia-svg-icon svg:first-child{\nfill:#004c93;\nstroke:#004c93;\n}\n<\/style>\n<article  class='iconbox iconbox_top av-kn64gdx5-a2d7f3078262234f13ba648fe01a42bd av-no-box  avia-builder-el-6  el_before_av_textblock  avia-builder-el-first ' ><div class=\"iconbox_content\"><header class=\"entry-content-header\" aria-label=\"Icon: Location\"><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 ' >Location<\/h3><\/header><div class='iconbox_content_container ' ><\/div><\/div><footer class=\"entry-footer\"><\/footer><\/article><br \/>\n<section  class='av_textblock_section av-lvar93ni-a5953731c889c2bdc81fe6b131fa2edb '  ><div class='avia_textblock' ><p style=\"text-align: center;\">Berlin<\/p>\n<\/div><\/section><\/p><\/div>\n<style type=\"text\/css\" data-created_by=\"avia_inline_auto\" id=\"style-css-av-3hkf1u-a829b70166e31c1577401c6d67bfe860\">\n.flex_column.av-3hkf1u-a829b70166e31c1577401c6d67bfe860{\nborder-radius:0px 0px 0px 0px;\npadding:0px 0px 0px 0px;\n}\n<\/style>\n<div  class='flex_column av-3hkf1u-a829b70166e31c1577401c6d67bfe860 av_one_third  avia-builder-el-8  el_after_av_one_third  el_before_av_one_third  flex_column_div av-zero-column-padding  '     ><p>\n<style type=\"text\/css\" data-created_by=\"avia_inline_auto\" id=\"style-css-av-kn64gdx5-31-5815657ff537aba50aebfa6117f8dfbc\">\n.iconbox.av-kn64gdx5-31-5815657ff537aba50aebfa6117f8dfbc .iconbox_icon{\nbackground-color:#f5f7fa;\nborder:1px solid #f5f7fa;\ncolor:#004c93;\n}\n.iconbox.av-kn64gdx5-31-5815657ff537aba50aebfa6117f8dfbc .iconbox_icon.avia-svg-icon svg:first-child{\nfill:#004c93;\nstroke:#004c93;\n}\n<\/style>\n<article  class='iconbox iconbox_top av-kn64gdx5-31-5815657ff537aba50aebfa6117f8dfbc av-no-box  avia-builder-el-9  el_before_av_textblock  avia-builder-el-first ' ><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 ' ><\/div><\/div><footer class=\"entry-footer\"><\/footer><\/article><br \/>\n<section  class='av_textblock_section av-lvar9rnv-f239dbc4ef6d572c537770ca0d289378 '  ><div class='avia_textblock' ><p style=\"text-align: center;\">Sep 25 &#8211; 28<\/p>\n<\/div><\/section><\/p><\/div>\n<style type=\"text\/css\" data-created_by=\"avia_inline_auto\" id=\"style-css-av-20pc8y-d712744ab496fcc5f5739fb1578b74a5\">\n.flex_column.av-20pc8y-d712744ab496fcc5f5739fb1578b74a5{\nborder-radius:0px 0px 0px 0px;\npadding:0px 0px 0px 0px;\n}\n<\/style>\n<div  class='flex_column av-20pc8y-d712744ab496fcc5f5739fb1578b74a5 av_one_third  avia-builder-el-11  el_after_av_one_third  avia-builder-el-last  flex_column_div av-zero-column-padding  '     ><p>\n<style type=\"text\/css\" data-created_by=\"avia_inline_auto\" id=\"style-css-av-kn64gdx5-30-8a1b7494e0558a6518a9bb524b944aa9\">\n.iconbox.av-kn64gdx5-30-8a1b7494e0558a6518a9bb524b944aa9 .iconbox_icon{\nbackground-color:#f5f7fa;\nborder:1px solid #f5f7fa;\ncolor:#004c93;\n}\n.iconbox.av-kn64gdx5-30-8a1b7494e0558a6518a9bb524b944aa9 .iconbox_icon.avia-svg-icon svg:first-child{\nfill:#004c93;\nstroke:#004c93;\n}\n<\/style>\n<article  class='iconbox iconbox_top av-kn64gdx5-30-8a1b7494e0558a6518a9bb524b944aa9 av-no-box  avia-builder-el-12  el_before_av_button  avia-builder-el-first ' ><div class=\"iconbox_content\"><header class=\"entry-content-header\" aria-label=\"Icon: Abstracts\"><div class='iconbox_icon heading-color avia-iconfont avia-font-entypo-fontello' data-av_icon='\ue84d' data-av_iconfont='entypo-fontello'  ><\/div><h3 class='iconbox_content_title ' >Abstracts<\/h3><\/header><div class='iconbox_content_container ' ><\/div><\/div><footer class=\"entry-footer\"><\/footer><\/article><br \/>\n<div  class='avia-button-wrap av-lvarajtn-a1661c906cafdab2914510533d6f729f-wrap avia-button-center  avia-builder-el-13  el_after_av_icon_box  avia-builder-el-last '><a href='https:\/\/abstracts.g-node.org\/conference\/BC18\/abstracts#\/'  class='avia-button av-lvarajtn-a1661c906cafdab2914510533d6f729f av-link-btn avia-icon_select-no avia-size-large avia-position-center avia-color-theme-color'  target=\"_blank\"  rel=\"noopener noreferrer\"  aria-label=\"Repository\"><span class='avia_iconbox_title' >Repository<\/span><\/a><\/div><\/p><\/div>\n\n<\/div><\/div><\/div><!-- close content main div --><\/div><\/div><div id='av_section_3'  class='avia-section av-6bh0nx-d282bfbd0edf21892c9a9a222289b804 main_color avia-section-default avia-no-border-styling  avia-builder-el-14  el_after_av_section  el_before_av_one_full  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-10115'><div class='entry-content-wrapper clearfix'>\n\n<style type=\"text\/css\" data-created_by=\"avia_inline_auto\" id=\"style-css-av-ctlxp-6e17afa75c0f372244aff424e0878c4e\">\n.flex_column.av-ctlxp-6e17afa75c0f372244aff424e0878c4e{\nborder-radius:0px 0px 0px 0px;\npadding:0px 0px 0px 0px;\n}\n<\/style>\n<div  class='flex_column av-ctlxp-6e17afa75c0f372244aff424e0878c4e av_one_full  avia-builder-el-15  el_before_av_one_half  avia-builder-el-first  first flex_column_div av-zero-column-padding  '     ><style type=\"text\/css\" data-created_by=\"avia_inline_auto\" id=\"style-css-av-35b082-58436b7cbc6324a9248aac354c94718b\">\n#top .av-special-heading.av-35b082-58436b7cbc6324a9248aac354c94718b{\npadding-bottom:10px;\n}\nbody .av-special-heading.av-35b082-58436b7cbc6324a9248aac354c94718b .av-special-heading-tag .heading-char{\nfont-size:25px;\n}\n.av-special-heading.av-35b082-58436b7cbc6324a9248aac354c94718b .av-subheading{\nfont-size:15px;\n}\n<\/style>\n<div  class='av-special-heading av-35b082-58436b7cbc6324a9248aac354c94718b av-special-heading-h2 blockquote modern-quote  avia-builder-el-16  avia-builder-el-no-sibling '><h2 class='av-special-heading-tag '  >Invited Lectures<\/h2><div class=\"special-heading-border\"><div class=\"special-heading-inner-border\"><\/div><\/div><\/div><\/div>\n<style type=\"text\/css\" data-created_by=\"avia_inline_auto\" id=\"style-css-av-ctlxp-28-c9dd0aa8e9fa7f9089985f6b69c9aaa6\">\n.flex_column.av-ctlxp-28-c9dd0aa8e9fa7f9089985f6b69c9aaa6{\nborder-radius:0px 0px 0px 0px;\npadding:0px 0px 0px 0px;\n}\n<\/style>\n<div  class='flex_column av-ctlxp-28-c9dd0aa8e9fa7f9089985f6b69c9aaa6 av_one_half  avia-builder-el-17  el_after_av_one_full  el_before_av_one_half  first flex_column_div av-zero-column-padding  column-top-margin'     ><p><section  class='av_textblock_section av-kn7alib6-146530f79059173e17fd83877639027d '  ><div class='avia_textblock' ><p><strong>James Di Carlo<\/strong> | McGovern Institute for Brain Research at MIT, USA<br \/>\n<em>Reverse engineering human visual intelligence<\/em><\/p>\n<\/div><\/section><br \/>\n<section  class='av_textblock_section av-kn7alib6-26-26d81a2f96e0089dced885e8b427918a '  ><div class='avia_textblock' ><p><strong>Brent Doiron<\/strong> | University of Pittsburgh, USA<br \/>\n<em>Old jobs for new inhibition: gain and stability in cortical networks with distinct inhibitory cell classes<\/em><\/p>\n<\/div><\/section><br \/>\n<section  class='av_textblock_section av-kn7alib6-25-d656425f5e6bcf75dc97cfd0de5516fc '  ><div class='avia_textblock' ><p><strong>Tatiana Engel<\/strong> | Cold Spring Harbour Laboratory, USA<br \/>\n<em>Dynamics of cortical states during selective attention<\/em><\/p>\n<\/div><\/section><br \/>\n<section  class='av_textblock_section av-kn7alib6-24-d190a3eea519404ea4c5ad166553f474 '  ><div class='avia_textblock' ><p><strong>Surya Ganguli<\/strong> | Stanford University, USA<br \/>\nEmergent elasticity in the neural code for space<\/p>\n<\/div><\/section><br \/>\n<section  class='av_textblock_section av-kn7alib6-23-764bc53e2fea0a3267b1d2317b9fe82e '  ><div class='avia_textblock' ><p><strong>Julijana Gjorgjieva <\/strong>| Max Planck Institute for Brain Research, Frankfurt, Germany<br \/>\nShaping developing circuits by patterned spontaneous and early sensory activity<\/p>\n<\/div><\/section><br \/>\n<section  class='av_textblock_section av-kn7alib6-22-62bd3a5374a5b382cc928140c4c23f57 '  ><div class='avia_textblock' ><p><strong>Vivek Jayaraman<\/strong> | Janelia Research Campus, Ashburn, USA<br \/>\n<em>Towards a mechanistic understanding of navigational neural dynamics<\/em><\/p>\n<\/div><\/section><\/p><\/div>\n<style type=\"text\/css\" data-created_by=\"avia_inline_auto\" id=\"style-css-av-ctlxp-27-50ccdc42db2aa73e038f12d149ccfd42\">\n.flex_column.av-ctlxp-27-50ccdc42db2aa73e038f12d149ccfd42{\nborder-radius:0px 0px 0px 0px;\npadding:0px 0px 0px 0px;\n}\n<\/style>\n<div  class='flex_column av-ctlxp-27-50ccdc42db2aa73e038f12d149ccfd42 av_one_half  avia-builder-el-24  el_after_av_one_half  avia-builder-el-last  flex_column_div av-zero-column-padding  column-top-margin'     ><p><section  class='av_textblock_section av-kn7alib6-21-0bf40cdc1552e1e79133302643d79cbf '  ><div class='avia_textblock' ><p><strong>Simon Laughlin <\/strong>| Cambridge University, UK<br \/>\nPushing the limits<\/p>\n<\/div><\/section><br \/>\n<section  class='av_textblock_section av-kn7alib6-19-852fbcecaa04ac6efa2bff753107eee7 '  ><div class='avia_textblock' ><p><strong>Sukbin Lim<\/strong> | NYU Shanghai, China<br \/>\n<em>Inferring synaptic plasticity rules in cortical circuits from in vivo data<\/em><\/p>\n<\/div><\/section><br \/>\n<section  class='av_textblock_section av-kn7alib6-18-91cfd1e97d5fbf0d4e6508940e6f466d '  ><div class='avia_textblock' ><p><strong>Timothy O\u2019Leary <\/strong>| Cambridge University, UK<br \/>\nBigger is better but too big is bad: how learning performance scales with neural circuit size<\/p>\n<\/div><\/section><br \/>\n<section  class='av_textblock_section av-kn7alib6-17-0860a70794f29208a37495e40ee1fcac '  ><div class='avia_textblock' ><p><strong>Eric Shea-Brown <\/strong>| University of Washington, Seattle, USA<br \/>\nWhat makes high-dimensional networks produce low-dimensional activity?<\/p>\n<\/div><\/section><br \/>\n<section  class='av_textblock_section av-kn7alib6-16-14e2c66e22c0a2ba753ed4239e3de4c8 '  ><div class='avia_textblock' ><p><strong>Tatjana Tchumatchenko<\/strong> | Max Planck Institute for Brain Research, Frankfurt, Germany<br \/>\n<em>How to understand neural network dynamics via intracellular dynamics<\/em><\/p>\n<\/div><\/section><\/p><\/div>\n<\/div><\/div><\/div><!-- close content main div --><\/div><\/div><div id='after_section_3'  class='main_color av_default_container_wrap 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-10115'><div class='entry-content-wrapper clearfix'>\n\n<style type=\"text\/css\" data-created_by=\"avia_inline_auto\" id=\"style-css-av-ctlxp-20-c363306e14574b227ab6270be62c0f7b\">\n.flex_column.av-ctlxp-20-c363306e14574b227ab6270be62c0f7b{\nborder-radius:0px 0px 0px 0px;\npadding:0px 0px 0px 0px;\n}\n<\/style>\n<div  class='flex_column av-ctlxp-20-c363306e14574b227ab6270be62c0f7b av_one_full  avia-builder-el-30  el_after_av_section  el_before_av_one_half  avia-builder-el-first  first flex_column_div av-zero-column-padding  '     ><style type=\"text\/css\" data-created_by=\"avia_inline_auto\" id=\"style-css-av-kqgkb61k-752030a366a9bcc60d2066a6ea5db301\">\n#top .av-special-heading.av-kqgkb61k-752030a366a9bcc60d2066a6ea5db301{\npadding-bottom:10px;\n}\nbody .av-special-heading.av-kqgkb61k-752030a366a9bcc60d2066a6ea5db301 .av-special-heading-tag .heading-char{\nfont-size:25px;\n}\n.av-special-heading.av-kqgkb61k-752030a366a9bcc60d2066a6ea5db301 .av-subheading{\nfont-size:15px;\n}\n<\/style>\n<div  class='av-special-heading av-kqgkb61k-752030a366a9bcc60d2066a6ea5db301 av-special-heading-h2 blockquote modern-quote  avia-builder-el-31  avia-builder-el-no-sibling '><h2 class='av-special-heading-tag '  >Valentin Braitenberg Award Winner <\/h2><div class=\"special-heading-border\"><div class=\"special-heading-inner-border\"><\/div><\/div><\/div><\/div>\n\n<style type=\"text\/css\" data-created_by=\"avia_inline_auto\" id=\"style-css-av-ctlxp-14-21f53dfd968b225f2385309084e439aa\">\n.flex_column.av-ctlxp-14-21f53dfd968b225f2385309084e439aa{\nborder-radius:0px 0px 0px 0px;\npadding:0px 0px 0px 0px;\n}\n<\/style>\n<div  class='flex_column av-ctlxp-14-21f53dfd968b225f2385309084e439aa av_one_half  avia-builder-el-32  el_after_av_one_full  el_before_av_section  avia-builder-el-last  first flex_column_div av-zero-column-padding  column-top-margin'     ><section  class='av_textblock_section av-kn7alib6-15-a415abbcbc2c179168d8fd7dbbcc7973 '  ><div class='avia_textblock' ><p><strong>Wulfram Gerstner<\/strong> | \u00c9cole Polytechnique F\u00e9d\u00e9ral de Lausanne, Switzerland<\/p>\n<\/div><\/section><\/div>\n<\/div><\/div><\/div><!-- close content main div --><\/div><\/div><div id='av_section_4'  class='avia-section av-6bh0nx-29-e1b6df7e8a766e007d54bc25a72a97be main_color avia-section-default avia-no-border-styling  avia-builder-el-34  el_after_av_one_half  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-10115'><div class='entry-content-wrapper clearfix'>\n\n<style type=\"text\/css\" data-created_by=\"avia_inline_auto\" id=\"style-css-av-ctlxp-13-23dcc9efb4fe25626231eaf0c450f95a\">\n.flex_column.av-ctlxp-13-23dcc9efb4fe25626231eaf0c450f95a{\nborder-radius:0px 0px 0px 0px;\npadding:0px 0px 0px 0px;\n}\n<\/style>\n<div  class='flex_column av-ctlxp-13-23dcc9efb4fe25626231eaf0c450f95a av_one_full  avia-builder-el-35  el_before_av_one_half  avia-builder-el-first  first flex_column_div av-zero-column-padding  '     ><style type=\"text\/css\" data-created_by=\"avia_inline_auto\" id=\"style-css-av-sf9x7m-0d66ef68816310071ee8525fc0491ad9\">\n#top .av-special-heading.av-sf9x7m-0d66ef68816310071ee8525fc0491ad9{\npadding-bottom:10px;\n}\nbody .av-special-heading.av-sf9x7m-0d66ef68816310071ee8525fc0491ad9 .av-special-heading-tag .heading-char{\nfont-size:25px;\n}\n.av-special-heading.av-sf9x7m-0d66ef68816310071ee8525fc0491ad9 .av-subheading{\nfont-size:15px;\n}\n<\/style>\n<div  class='av-special-heading av-sf9x7m-0d66ef68816310071ee8525fc0491ad9 av-special-heading-h2 blockquote modern-quote  avia-builder-el-36  avia-builder-el-no-sibling '><h2 class='av-special-heading-tag '  >Contributed Talks<\/h2><div class=\"special-heading-border\"><div class=\"special-heading-inner-border\"><\/div><\/div><\/div><\/div>\n<style type=\"text\/css\" data-created_by=\"avia_inline_auto\" id=\"style-css-av-ctlxp-10-32d57840715ab39a9782b07d28e92aa4\">\n.flex_column.av-ctlxp-10-32d57840715ab39a9782b07d28e92aa4{\nborder-radius:0px 0px 0px 0px;\npadding:0px 0px 0px 0px;\n}\n<\/style>\n<div  class='flex_column av-ctlxp-10-32d57840715ab39a9782b07d28e92aa4 av_one_half  avia-builder-el-37  el_after_av_one_full  el_before_av_one_half  first flex_column_div av-zero-column-padding  column-top-margin'     ><p><section  class='av_textblock_section av-kn7alib6-12-b4f505a1c6de35ceea268317784486e1 '  ><div class='avia_textblock' ><p><strong>Armin Bahl<\/strong> | Harvard University, USA<br \/>\n<em>Neuronal mechanisms of evidence accumulation and decision making in the larval zebrafish<\/em><\/p>\n<\/div><\/section><br \/>\n<section  class='av_textblock_section av-kn7alib6-8-63d292f96adf50f4493cadd3169fe62e '  ><div class='avia_textblock' ><p><strong>Alon Rubin<\/strong> | Weizmann Institute of Science, Rehovot, Israel<br \/>\n<em>Revealing neural correlates of behavior without behavioral measurements<\/em><\/p>\n<\/div><\/section><br \/>\n<section  class='av_textblock_section av-kn7alib6-7-68a290e6f11702f24080a6e83717ab68 '  ><div class='avia_textblock' ><p><strong>Louis Kang<\/strong> | University of California, Berkeley, USA<br \/>\n<em>Replay arises naturally as a traveling wavefront in an entorhinal attractor network<\/em><\/p>\n<\/div><\/section><br \/>\n<section  class='av_textblock_section av-kn7alib6-6-c09e7e6e667731a936f1831f81eed479 '  ><div class='avia_textblock' ><p><strong>Evelyn Tang<\/strong> | Max Planck Institute for Dynamics and Self-Organization, G\u00f6ttingen<br \/>\n<em>Effective learning is accompanied by high dimensional and efficient representations of neural activity<\/em><\/p>\n<\/div><\/section><\/p><\/div>\n<style type=\"text\/css\" data-created_by=\"avia_inline_auto\" id=\"style-css-av-ctlxp-9-ec24183971fede8b3f40a0430697a911\">\n.flex_column.av-ctlxp-9-ec24183971fede8b3f40a0430697a911{\nborder-radius:0px 0px 0px 0px;\npadding:0px 0px 0px 0px;\n}\n<\/style>\n<div  class='flex_column av-ctlxp-9-ec24183971fede8b3f40a0430697a911 av_one_half  avia-builder-el-42  el_after_av_one_half  avia-builder-el-last  flex_column_div av-zero-column-padding  column-top-margin'     ><p><section  class='av_textblock_section av-kn7alib6-5-41c02c7fe6859add72e9bba46260661c '  ><div class='avia_textblock' ><p><strong>Johannes Zierenberg<\/strong> | Max Planck Institute for Dynamics and Self-Organization, G\u00f6ttingen, Germany<br \/>\n<em>Homeostatic plasticity and external input explain difference in neural spiking activity in vitro and in vivo<\/em><\/p>\n<\/div><\/section><br \/>\n<section  class='av_textblock_section av-kn7alib6-3-852ec7059895438ac83de8f0a08efdad '  ><div class='avia_textblock' ><p><strong>Giulio Bondanelli<\/strong> | \u00c9cole Normale Sup\u00e9rieure de Paris, France<br \/>\n<em>Coding with transient trajectories in recurrent neural networks<\/em><\/p>\n<\/div><\/section><br \/>\n<section  class='av_textblock_section av-kn7alib6-2-aa3d20edb49093f60a73a0447d77c147 '  ><div class='avia_textblock' ><p><strong>Genis Prat-Ortega<\/strong> | Institut d&#8217;investigacions Biom\u00e8diques August Pi i Sunyer, Barcelona, Spain)<br \/>\n<em>Flexible categorization in perceptual decision making<\/em><\/p>\n<\/div><\/section><\/p><\/div>\n\n<\/div><\/div><\/div><!-- close content main div --><\/div><\/div><div id='av_section_5'  class='avia-section av-6bh0nx-11-d714998000bcb915fbd8dcbaad249d87 main_color avia-section-default avia-no-border-styling  avia-builder-el-46  el_after_av_section  el_before_av_submenu  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-10115'><div class='entry-content-wrapper clearfix'>\n\n<style type=\"text\/css\" data-created_by=\"avia_inline_auto\" id=\"style-css-av-ctlxp-4-6f7e26f21bf0e00d7ecf52a67722311c\">\n.flex_column.av-ctlxp-4-6f7e26f21bf0e00d7ecf52a67722311c{\nborder-radius:0px 0px 0px 0px;\npadding:0px 0px 0px 0px;\n}\n<\/style>\n<div  class='flex_column av-ctlxp-4-6f7e26f21bf0e00d7ecf52a67722311c av_one_full  avia-builder-el-47  el_before_av_one_full  avia-builder-el-first  first flex_column_div av-zero-column-padding  '     ><style type=\"text\/css\" data-created_by=\"avia_inline_auto\" id=\"style-css-av-1qrzizm-43af9964c17f126434ce9679aa873492\">\n#top .av-special-heading.av-1qrzizm-43af9964c17f126434ce9679aa873492{\npadding-bottom:10px;\n}\nbody .av-special-heading.av-1qrzizm-43af9964c17f126434ce9679aa873492 .av-special-heading-tag .heading-char{\nfont-size:25px;\n}\n.av-special-heading.av-1qrzizm-43af9964c17f126434ce9679aa873492 .av-subheading{\nfont-size:15px;\n}\n<\/style>\n<div  class='av-special-heading av-1qrzizm-43af9964c17f126434ce9679aa873492 av-special-heading-h2 blockquote modern-quote  avia-builder-el-48  avia-builder-el-no-sibling '><h2 class='av-special-heading-tag '  >Satellite Workshops<\/h2><div class=\"special-heading-border\"><div class=\"special-heading-inner-border\"><\/div><\/div><\/div><\/div>\n<style type=\"text\/css\" data-created_by=\"avia_inline_auto\" id=\"style-css-av-ctlxp-1-a11808ce1732335eb7afde3d09c5d763\">\n.flex_column.av-ctlxp-1-a11808ce1732335eb7afde3d09c5d763{\nborder-radius:0px 0px 0px 0px;\npadding:0px 0px 0px 0px;\n}\n<\/style>\n<div  class='flex_column av-ctlxp-1-a11808ce1732335eb7afde3d09c5d763 av_one_full  avia-builder-el-49  el_after_av_one_full  avia-builder-el-last  first flex_column_div av-zero-column-padding  column-top-margin'     ><style type=\"text\/css\" data-created_by=\"avia_inline_auto\" id=\"style-css-av-kd4m4nu0-627f9b87a879b2f4ce68ddf2fcc9123d\">\n#top .togglecontainer.av-kd4m4nu0-627f9b87a879b2f4ce68ddf2fcc9123d p.toggler{\nborder-color:#ebebeb;\n}\n#top .togglecontainer.av-kd4m4nu0-627f9b87a879b2f4ce68ddf2fcc9123d .toggle_wrap .toggle_content{\nborder-color:#ebebeb;\n}\n<\/style>\n<div  class='togglecontainer av-kd4m4nu0-627f9b87a879b2f4ce68ddf2fcc9123d av-elegant-toggle  avia-builder-el-50  avia-builder-el-no-sibling ' >\n<section class='av_toggle_section av-77oxfia-5b2ff456dd9247fe9f60d72eb470585c' ><div role=\"tablist\" class=\"single_toggle\" data-tags=\"{All} \"  ><p id='toggle-crossing-scales-understanding-collective-neural-activity' data-fake-id='#crossing-scales-understanding-collective-neural-activity' class='toggler  av-title-above av-inherit-border-color'  role='tab' tabindex='0' aria-controls='crossing-scales-understanding-collective-neural-activity' data-slide-speed=\"200\" data-title=\"Sensory neurons: \u2018predictive coding\u2019 or \u2018coding for predictions\u2019?&lt;br \/&gt;\n&lt;span class=&quot;workshop-subtitle&quot;&gt;&lt;strong&gt;Organizers:&lt;\/strong&gt; Matthew Chalk&lt;\/span&gt;\" data-title-open=\"\" data-aria_collapsed=\"Click to expand: Sensory neurons: \u2018predictive coding\u2019 or \u2018coding for predictions\u2019?&lt;br \/&gt;\n&lt;span class=&quot;workshop-subtitle&quot;&gt;&lt;strong&gt;Organizers:&lt;\/strong&gt; Matthew Chalk&lt;\/span&gt;\" data-aria_expanded=\"Click to collapse: Sensory neurons: \u2018predictive coding\u2019 or \u2018coding for predictions\u2019?&lt;br \/&gt;\n&lt;span class=&quot;workshop-subtitle&quot;&gt;&lt;strong&gt;Organizers:&lt;\/strong&gt; Matthew Chalk&lt;\/span&gt;\">Sensory neurons: \u2018predictive coding\u2019 or \u2018coding for predictions\u2019?<br \/>\n<span class=\"workshop-subtitle\"><strong>Organizers:<\/strong> Matthew Chalk<\/span><span class=\"toggle_icon\"><span class=\"vert_icon\"><\/span><span class=\"hor_icon\"><\/span><\/span><\/p><div id='crossing-scales-understanding-collective-neural-activity' aria-labelledby='toggle-crossing-scales-understanding-collective-neural-activity' role='region' class='toggle_wrap  av-title-above'  ><div class='toggle_content invers-color av-inherit-border-color' ><p><strong>Description:<\/strong><\/p>\n<p class=\"p1\">The notion of sensory prediction has a long history in theories of neural coding. For example, the influential &#8216;predictive coding&#8217; hypothesis posits that to spare resources, rather than encoding all sensory inputs, neurons encode a prediction error, equal to the difference between their received and expected sensory inputs. An alternative, recent, idea is that sensory neurons instead &#8216;code for predictions&#8217;, by preferentially encoding sensory signals that are informative about the future while discarding other, non-predictive, signals.<\/p>\n<p class=\"p1\">Despite decades of research on predictive coding, many questions remain unanswered. For example, are sensory predictions important in determining what is encoded (i.e. do neurons selectively encode &#8216;predictive&#8217; stimuli?). In addition, do sensory predictions play a role in determining how sensory signals are encoded (e.g. do neurons encode a &#8216;prediction error&#8217; signal?). Finally, how, and over what timescales, do sensory circuits adjust their predictions based on experience and changes in the environment?<\/p>\n<p class=\"p1\">This workshop will bring together a broad group of theoretical and experimental researchers to discuss and debate the role of sensory predictions in neural coding. It will aim to build bridges between current theories of sensory prediction, clarifying what they have in common, and when they are opposed. Further, it will address how these different theories are supported by experiments. It is hoped that the resulting discussion will stimulate new ideas about how to test these theories, to uncover how sensory predictions are used by the brain to shape neural coding.<\/p>\n<p><strong>Speakers:<\/strong><\/p>\n<ul>\n<li>Audrey Sederberg<\/li>\n<li>Bernhard Englitz<\/li>\n<li>Nicol Harper<\/li>\n<li>Wiktor Mlynarski<\/li>\n<li>George Keller<\/li>\n<li>Dirk Jancke<\/li>\n<li>Michael Berry<\/li>\n<\/ul>\n<\/div><\/div><\/div><\/section>\n<section class='av_toggle_section av-15evrqa-21be71ff7803c0ae1246d5ab4f1cf48a' ><div role=\"tablist\" class=\"single_toggle\" data-tags=\"{All} \"  ><p id='toggle-dynamic-probabilistic-inference-in-the-brain' data-fake-id='#dynamic-probabilistic-inference-in-the-brain' class='toggler  av-title-above av-inherit-border-color'  role='tab' tabindex='0' aria-controls='dynamic-probabilistic-inference-in-the-brain' data-slide-speed=\"200\" data-title=\"Offline hippocampal activity - Neural sequences and sharp-wave ripples&lt;br \/&gt;\n&lt;span class=&quot;workshop-subtitle&quot;&gt;&lt;strong&gt;Organizers:&lt;\/strong&gt; Sen Cheng, Jos\u00e9 Donoso&lt;\/span&gt;\" data-title-open=\"\" data-aria_collapsed=\"Click to expand: Offline hippocampal activity - Neural sequences and sharp-wave ripples&lt;br \/&gt;\n&lt;span class=&quot;workshop-subtitle&quot;&gt;&lt;strong&gt;Organizers:&lt;\/strong&gt; Sen Cheng, Jos\u00e9 Donoso&lt;\/span&gt;\" data-aria_expanded=\"Click to collapse: Offline hippocampal activity - Neural sequences and sharp-wave ripples&lt;br \/&gt;\n&lt;span class=&quot;workshop-subtitle&quot;&gt;&lt;strong&gt;Organizers:&lt;\/strong&gt; Sen Cheng, Jos\u00e9 Donoso&lt;\/span&gt;\">Offline hippocampal activity - Neural sequences and sharp-wave ripples<br \/>\n<span class=\"workshop-subtitle\"><strong>Organizers:<\/strong> Sen Cheng, Jos\u00e9 Donoso<\/span><span class=\"toggle_icon\"><span class=\"vert_icon\"><\/span><span class=\"hor_icon\"><\/span><\/span><\/p><div id='dynamic-probabilistic-inference-in-the-brain' aria-labelledby='toggle-dynamic-probabilistic-inference-in-the-brain' role='region' class='toggle_wrap  av-title-above'  ><div class='toggle_content invers-color av-inherit-border-color' ><p><strong>Description:<\/strong><\/p>\n<p class=\"p1\">The replay of neural sequences during hippocampal sharp waves\/ripples (SWR) has been implicated in several cognitive functions (e.g., memory consolidation, working memory, navigation, planning, etc.), but the mechanisms by which the hippocampal network can acquire, sustain, and regenerate those sequences are still not clear.<\/p>\n<p class=\"p1\">With a few exceptions, both theoretical and experimental studies addressing this question focus on one of two aspects of the phenomenon, namely the local field potential (LFP) signature (i.e., the SWR complexes), or the underlying sequential activity. However, since different classes of LFP models impose different constraints on the mechanisms of replay, and vice-versa, it is important to address these two aspects of the phenomenon within a common ground. Thus, the main purpose of this workshop is to bring together the dynamical and computational aspects of off-line hippocampal activity, and thereby provide a common framework to study the phenomenon from a wider perspective. In particular, we aim at understanding the relationship between the oscillatory behavior of the hippocampal network and the different types of sequential activities it can support.<\/p>\n<p><strong>Speakers:<\/strong><\/p>\n<ul>\n<li>Ole Paulsen<\/li>\n<li>Attila Guly\u00e1s<\/li>\n<li>Paola Malerba<\/li>\n<li>David Foster<\/li>\n<li>Amir Azizi<\/li>\n<li>Jos\u00e9 R. Donoso<\/li>\n<li>Joszef Csicsvari<\/li>\n<\/ul>\n<\/div><\/div><\/div><\/section>\n<section class='av_toggle_section av-62fahpe-0026d036b46c5f5ced596bb71c5e4fc0' ><div role=\"tablist\" class=\"single_toggle\" data-tags=\"{All} \"  ><p id='toggle-frontiers-in-the-evolution-of-neuronal-computation' data-fake-id='#frontiers-in-the-evolution-of-neuronal-computation' class='toggler  av-title-above av-inherit-border-color'  role='tab' tabindex='0' aria-controls='frontiers-in-the-evolution-of-neuronal-computation' data-slide-speed=\"200\" data-title=\"The diversity of dynamical states in recurrent neural circuits&lt;br \/&gt;\n&lt;span class=&quot;workshop-subtitle&quot;&gt;&lt;strong&gt;Organizers:&lt;\/strong&gt; David Dahmen, Viola Priesemann, Moritz Helias, Rainer Engelken&lt;\/span&gt;\" data-title-open=\"\" data-aria_collapsed=\"Click to expand: The diversity of dynamical states in recurrent neural circuits&lt;br \/&gt;\n&lt;span class=&quot;workshop-subtitle&quot;&gt;&lt;strong&gt;Organizers:&lt;\/strong&gt; David Dahmen, Viola Priesemann, Moritz Helias, Rainer Engelken&lt;\/span&gt;\" data-aria_expanded=\"Click to collapse: The diversity of dynamical states in recurrent neural circuits&lt;br \/&gt;\n&lt;span class=&quot;workshop-subtitle&quot;&gt;&lt;strong&gt;Organizers:&lt;\/strong&gt; David Dahmen, Viola Priesemann, Moritz Helias, Rainer Engelken&lt;\/span&gt;\">The diversity of dynamical states in recurrent neural circuits<br \/>\n<span class=\"workshop-subtitle\"><strong>Organizers:<\/strong> David Dahmen, Viola Priesemann, Moritz Helias, Rainer Engelken<\/span><span class=\"toggle_icon\"><span class=\"vert_icon\"><\/span><span class=\"hor_icon\"><\/span><\/span><\/p><div id='frontiers-in-the-evolution-of-neuronal-computation' aria-labelledby='toggle-frontiers-in-the-evolution-of-neuronal-computation' role='region' class='toggle_wrap  av-title-above'  ><div class='toggle_content invers-color av-inherit-border-color' ><p><strong>Description:<\/strong><\/p>\n<p class=\"p1\">Experiments suggest a wealth of dynamical states in the brain. These range from asynchronous irregular activity to synchronizations, oscillations, activity waves or avalanches. Understanding the mechanisms that can give rise to such a diversity of network states in the healthy and diseased brain is a challenge both for experimentalists and theoreticians.<\/p>\n<p class=\"p1\">Also the implications of these different operating regimes of the network dynamics on the ability to encode, process and transmit information is not well understood. In recurrent network models, the sensitivity of neural dynamics to small perturbations or noise can reveal features that are governing the microscopic phase space organization. Optimal computational performance of neuronal networks was hypothesized to be found close to phase transitions, where the dynamics exhibits universal behavior that is characterized by strong concerted fluctuations between neurons. The diversity of possible states and state transitions in a high dimensional system such as cortex, however, permits a multitude of hypotheses on the \u201cground state\u201d of different cortical regions.<\/p>\n<p class=\"p1\">In this workshop, we bring together experts working on theories to characterize the different dynamical states of recurrent neural networks and identify synaptic, neuronal, and network properties that shape the collective dynamics. We want to relate dynamical states to features of observed neural activity in different cortical regions, work out possibilities to test theoretical predictions by experiments, and discuss functional implications of the dynamics.<\/p>\n<p><strong>Speakers:<\/strong><\/p>\n<ul>\n<li>Doiron B.<\/li>\n<li>Logiaco L.<\/li>\n<li>Brinkman B.<\/li>\n<li>Mastrogiuseppe F.<\/li>\n<li>Kadmon J.<\/li>\n<li>Pereira Obilinovic U.<\/li>\n<li>Shriki O.<\/li>\n<li>Mante V.<\/li>\n<li>di Santo S.<\/li>\n<li>Kushnir L.<\/li>\n<li>Kriener B.<\/li>\n<\/ul>\n<\/div><\/div><\/div><\/section>\n<section class='av_toggle_section av-j0g4ua-5fc194d9a3cd30661873be69b0e5a119' ><div role=\"tablist\" class=\"single_toggle\" data-tags=\"{All} \"  ><p id='toggle-inferring-and-testing-optimality-in-perception-and-neurons' data-fake-id='#inferring-and-testing-optimality-in-perception-and-neurons' class='toggler  av-title-above av-inherit-border-color'  role='tab' tabindex='0' aria-controls='inferring-and-testing-optimality-in-perception-and-neurons' data-slide-speed=\"200\" data-title=\"Practical approaches to research data management and reproducibility&lt;br \/&gt;\n&lt;span class=&quot;workshop-subtitle&quot;&gt;&lt;strong&gt;Organizers:&lt;\/strong&gt; Michael Denker, Thomas Wachtler&lt;\/span&gt;\" data-title-open=\"\" data-aria_collapsed=\"Click to expand: Practical approaches to research data management and reproducibility&lt;br \/&gt;\n&lt;span class=&quot;workshop-subtitle&quot;&gt;&lt;strong&gt;Organizers:&lt;\/strong&gt; Michael Denker, Thomas Wachtler&lt;\/span&gt;\" data-aria_expanded=\"Click to collapse: Practical approaches to research data management and reproducibility&lt;br \/&gt;\n&lt;span class=&quot;workshop-subtitle&quot;&gt;&lt;strong&gt;Organizers:&lt;\/strong&gt; Michael Denker, Thomas Wachtler&lt;\/span&gt;\">Practical approaches to research data management and reproducibility<br \/>\n<span class=\"workshop-subtitle\"><strong>Organizers:<\/strong> Michael Denker, Thomas Wachtler<\/span><span class=\"toggle_icon\"><span class=\"vert_icon\"><\/span><span class=\"hor_icon\"><\/span><\/span><\/p><div id='inferring-and-testing-optimality-in-perception-and-neurons' aria-labelledby='toggle-inferring-and-testing-optimality-in-perception-and-neurons' role='region' class='toggle_wrap  av-title-above'  ><div class='toggle_content invers-color av-inherit-border-color' ><p><strong>Description:<\/strong><\/p>\n<p class=\"p1\">Advances in neuroscience technology and methodology have dramatically increased our abilities to generate data with unprecedented volume and complexity, and increasing complexity of experimental paradigms or models pose increasing demands on data management to ensure reproducibility. As we use more and more powerful experimental, analytical, and modeling techniques, we also require sophisticated methods supporting data handling, reproducibility, and collaboration. Although various tools have started to emerge that address some of these challenges, we must ask how these tools are best combined synergistically to form complete digitized and documented workflows for data acquisition and analysis.<\/p>\n<p class=\"p1\">This workshop will present practical examples of methods and tools that enable the researcher to keep track and keep in control of their data and analysis workflows. Lyuba Zehl and Hiroaki Wagatsuma will demonstrate how metadata and data of highly complex experiments can be organized and integrated to enable automated and reproducible data processing. Andrew Davison, Julia Sprenger, Johannes Koester, and Sharon Crook will present tools for reproducible analysis and modeling workflows. Solutions for data access, collaborative sharing and data publication will be presented by Roman Moucek, Michael Hanke and Christian Garbers. Finally, in a joint session several of the presenters will give a hands-on tutorial of combining tools for reproducible workflows and efficient collaboration, and provide the opportunity for workshop participants to directly explore possibilities how the tools can benefit their own work.<\/p>\n<p><strong>Speakers:<\/strong><\/p>\n<ul>\n<li>Zehl L.<\/li>\n<li>Wagatsuma H.<\/li>\n<li>Legou\u00e9e E.<\/li>\n<li>Sprenger J.<\/li>\n<li>Crook S.<\/li>\n<li>Koester J.<\/li>\n<li>Moucek R.<\/li>\n<li>Hanke M.<\/li>\n<li>Garbers C.<\/li>\n<\/ul>\n<\/div><\/div><\/div><\/section>\n<section class='av_toggle_section av-584ons2-16fe598b9f2fa21196cd263d3853b35d' ><div role=\"tablist\" class=\"single_toggle\" data-tags=\"{All} \"  ><p id='toggle-leveraging-open-datasets-from-the-allen-brain-observatory-for-computational-neuroscience' data-fake-id='#leveraging-open-datasets-from-the-allen-brain-observatory-for-computational-neuroscience' class='toggler  av-title-above av-inherit-border-color'  role='tab' tabindex='0' aria-controls='leveraging-open-datasets-from-the-allen-brain-observatory-for-computational-neuroscience' data-slide-speed=\"200\" data-title=\"Adaptivity and Inhomogeneity in Neuronal Networks&lt;br \/&gt;\n&lt;span class=&quot;workshop-subtitle&quot;&gt;&lt;strong&gt;Organizers:&lt;\/strong&gt; Ulrich Egert, Stefan Rotter&lt;\/span&gt;\" data-title-open=\"\" data-aria_collapsed=\"Click to expand: Adaptivity and Inhomogeneity in Neuronal Networks&lt;br \/&gt;\n&lt;span class=&quot;workshop-subtitle&quot;&gt;&lt;strong&gt;Organizers:&lt;\/strong&gt; Ulrich Egert, Stefan Rotter&lt;\/span&gt;\" data-aria_expanded=\"Click to collapse: Adaptivity and Inhomogeneity in Neuronal Networks&lt;br \/&gt;\n&lt;span class=&quot;workshop-subtitle&quot;&gt;&lt;strong&gt;Organizers:&lt;\/strong&gt; Ulrich Egert, Stefan Rotter&lt;\/span&gt;\">Adaptivity and Inhomogeneity in Neuronal Networks<br \/>\n<span class=\"workshop-subtitle\"><strong>Organizers:<\/strong> Ulrich Egert, Stefan Rotter<\/span><span class=\"toggle_icon\"><span class=\"vert_icon\"><\/span><span class=\"hor_icon\"><\/span><\/span><\/p><div id='leveraging-open-datasets-from-the-allen-brain-observatory-for-computational-neuroscience' aria-labelledby='toggle-leveraging-open-datasets-from-the-allen-brain-observatory-for-computational-neuroscience' role='region' class='toggle_wrap  av-title-above'  ><div class='toggle_content invers-color av-inherit-border-color' ><p><strong>Description:<\/strong><\/p>\n<p class=\"p1\">We hereby propose a workshop to discuss recent experimental findings and new theoretical concepts concerning the interaction between activity-dependent growth processes and structural inhomogeneity in neuronal networks of the brain.<\/p>\n<p class=\"p1\">A prevailing property of neuronal networks in the brain is that they are inhomogeneous in structure and in composition. However, what appears homogeneous on one scale of observation may appear inhomogeneous on another: parameters like neuron density, patchy connectivity patterns, inhomogeneous distribution of neuron types, synaptic connections between them, etc., can be organized on local or global scales (e.g. Schmidt et al. \u201818, Brain Struct Funct. 223:1409; Okujeni et al. \u201817, J. Neurosci. 37:3972, Ocker et al. \u201817, PLoS Comp. Bio. 13:e1005583). No doubt do such inhomogeneities have an impact on network activity dynamics (Litwin-Kumar &amp; Doiron \u201812, Nat Neurosci 15:1498; Pernice et al. \u201811, PLOS Comp Bio 7:e100\u201959; Pernice et al. \u201813, Front Comput Neurosci 7:72) and, consequently, on the interpretation of experimental data. In addition to unavoidable statistical variability, pathological conditions, be it after peripheral amputations, stroke, dysplasia, epilepsy, etc., induce further changes to network structure. These changes, in turn, provoke adaptive responses on many levels, from synaptic plasticity to neurogenesis (Janz et al. \u201817, Cereb Cortex 27:2348). Pathological changes inherently have local aspects, which may lead to another type of inhomogeneity in the form of gradients. Examples are the periphery of the infarct zone, glial scars, or borders between sclerotic and healthy areas round epileptic foci (H\u00e4ussler et al. \u201812, Cereb Cortex 22:26).<\/p>\n<p class=\"p1\">As a result, the dynamics of internal and external interaction as well as overall function may diverge considerably across networks of a particular general type with the same average properties.<\/p>\n<p class=\"p1\">Such inhomogeneities would have significant consequences from several perspectives. The statistical distribution of neuron types, synapses, connectivity motifs and recurrent connectivity would become highly variable across space. Synaptic plasticity and homeostatic processes would lead to inhomogeneous distributions of synaptic weights, excitability, excitation\/inhibition balance and structural dynamics. These would further affect information processing and network stability (Gallinaro &amp; Rotter \u201818, Sci Rep 8:3754; Landau et al. \u201816, Neuron 92:1106; Ostojic \u201814; Nat Neurosci 17:594; Teller et al. \u201814, PLoS Comp Bio 10:e1003796; Pernice et al. \u201811, \u201813; Jarvis et al \u201810, Neuroinform 4:11, Effenberger et al. \u201815, PLoS Comp Bio 11:e10044\u2019; Nagler et al. \u201911, Nat. Physics 7:265). On the other hand, adaptive processes may counteract inhomogeneity (Okujeni et al. \u201817). If this is the case, whether it is an advantageous goal, or under which boundary conditions it might be successful, is currently not known.<\/p>\n<p><strong>Speakers:<\/strong><\/p>\n<ul>\n<li>Okujeni S.<\/li>\n<li>Levina A.<\/li>\n<li>Gallinaro J.<\/li>\n<li>Soriano J.<\/li>\n<li>Safavieh E.<\/li>\n<li>Hoffmann F.<\/li>\n<li>H\u00e4ussler U.<\/li>\n<li>Ostojic S.<\/li>\n<li>Litwin-Kumar A.<\/li>\n<li>Merkt B.<\/li>\n<li>van Albada S.<\/li>\n<\/ul>\n<\/div><\/div><\/div><\/section>\n<section class='av_toggle_section av-4ry8ao2-9625e3cd1ab992733d3fc9a0b6058e76' ><div role=\"tablist\" class=\"single_toggle\" data-tags=\"{All} \"  ><p id='toggle-neural-computations-learning-and-dynamics-in-recurrent-networks' data-fake-id='#neural-computations-learning-and-dynamics-in-recurrent-networks' class='toggler  av-title-above av-inherit-border-color'  role='tab' tabindex='0' aria-controls='neural-computations-learning-and-dynamics-in-recurrent-networks' data-slide-speed=\"200\" data-title=\"Learning and recalling sequences of actions at the neuronal level and beyond&lt;br \/&gt;\n&lt;span class=&quot;workshop-subtitle&quot;&gt;&lt;strong&gt;Organizers:&lt;\/strong&gt; Daniel Miner, Christian Tetzlaff&lt;\/span&gt;\" data-title-open=\"\" data-aria_collapsed=\"Click to expand: Learning and recalling sequences of actions at the neuronal level and beyond&lt;br \/&gt;\n&lt;span class=&quot;workshop-subtitle&quot;&gt;&lt;strong&gt;Organizers:&lt;\/strong&gt; Daniel Miner, Christian Tetzlaff&lt;\/span&gt;\" data-aria_expanded=\"Click to collapse: Learning and recalling sequences of actions at the neuronal level and beyond&lt;br \/&gt;\n&lt;span class=&quot;workshop-subtitle&quot;&gt;&lt;strong&gt;Organizers:&lt;\/strong&gt; Daniel Miner, Christian Tetzlaff&lt;\/span&gt;\">Learning and recalling sequences of actions at the neuronal level and beyond<br \/>\n<span class=\"workshop-subtitle\"><strong>Organizers:<\/strong> Daniel Miner, Christian Tetzlaff<\/span><span class=\"toggle_icon\"><span class=\"vert_icon\"><\/span><span class=\"hor_icon\"><\/span><\/span><\/p><div id='neural-computations-learning-and-dynamics-in-recurrent-networks' aria-labelledby='toggle-neural-computations-learning-and-dynamics-in-recurrent-networks' role='region' class='toggle_wrap  av-title-above'  ><div class='toggle_content invers-color av-inherit-border-color' ><p><strong>Description:<\/strong><\/p>\n<p class=\"p1\">Living organisms rely on repeated sequences of actions to execute many of the important functions of their day-to-day lives. Some are stereotyped over nearly the entire lifespan of organisms, others are learned and recalled on a much more rapid timescale. Numerous studies of the ways in which basic neural network models can learn and recall simple sequences of stimuli or patterns of activity exist, as do behavioral and applied studies examining the psychophysical process of learning sequences of actions. However, the gap between these two levels has not yet been bridged. We aim to bring together experts on neural network modeling, neural plasticity phenomenology, and analysis and implementation of this sort of learning in applied scenarios to begin a discussion that will help to close this gap in levels of abstraction.<\/p>\n<p class=\"p1\">Specifically, we aim to first examine the mechanisms of neural plasticity (and associated homeostatic processes) and network structures that allow sequences of stimuli or actions to be learned. We will then examine the network dynamics that allow the recall and executions of such patterns of action. We will then consider larger-scale designs that can integrate this in more than a simplest-test-case scenario. Subsequently, we will engage with sicentists engaged in neuro-inspired robotics who are beginning to integrate such systems into their platforms, potentially in a closed-loop fashion, in order to form a cohesive chain of how these concepts arise from the most fundamental to the fully applied levels.<\/p>\n<p><strong>Speakers:<\/strong><\/p>\n<ul>\n<li>Clopath C.<\/li>\n<li>Gerstner W.<\/li>\n<li>Triesch J.<\/li>\n<li>Miner D.<\/li>\n<li>Herpich S.<\/li>\n<li>Kempter R.<\/li>\n<li>Larsen J.<\/li>\n<li>Del Papa B.<\/li>\n<li>Sandamirskaya Y.<\/li>\n<li>Tani J.<\/li>\n<\/ul>\n<\/div><\/div><\/div><\/section>\n<section class='av_toggle_section av-4drkt42-3bec6e489b50e9e65b6caa5d955adeba' ><div role=\"tablist\" class=\"single_toggle\" data-tags=\"{All} \"  ><p id='toggle-neurons-are-cells-the-role-of-cellular-properties-in-neural-circuit-computations' data-fake-id='#neurons-are-cells-the-role-of-cellular-properties-in-neural-circuit-computations' class='toggler  av-title-above av-inherit-border-color'  role='tab' tabindex='0' aria-controls='neurons-are-cells-the-role-of-cellular-properties-in-neural-circuit-computations' data-slide-speed=\"200\" data-title=\"Internally generated network dynamics: experiment and theory&lt;br \/&gt;\n&lt;span class=&quot;workshop-subtitle&quot;&gt;&lt;strong&gt;Organizers:&lt;\/strong&gt; Anton Sirota, Arne Meyer&lt;\/span&gt;\" data-title-open=\"\" data-aria_collapsed=\"Click to expand: Internally generated network dynamics: experiment and theory&lt;br \/&gt;\n&lt;span class=&quot;workshop-subtitle&quot;&gt;&lt;strong&gt;Organizers:&lt;\/strong&gt; Anton Sirota, Arne Meyer&lt;\/span&gt;\" data-aria_expanded=\"Click to collapse: Internally generated network dynamics: experiment and theory&lt;br \/&gt;\n&lt;span class=&quot;workshop-subtitle&quot;&gt;&lt;strong&gt;Organizers:&lt;\/strong&gt; Anton Sirota, Arne Meyer&lt;\/span&gt;\">Internally generated network dynamics: experiment and theory<br \/>\n<span class=\"workshop-subtitle\"><strong>Organizers:<\/strong> Anton Sirota, Arne Meyer<\/span><span class=\"toggle_icon\"><span class=\"vert_icon\"><\/span><span class=\"hor_icon\"><\/span><\/span><\/p><div id='neurons-are-cells-the-role-of-cellular-properties-in-neural-circuit-computations' aria-labelledby='toggle-neurons-are-cells-the-role-of-cellular-properties-in-neural-circuit-computations' role='region' class='toggle_wrap  av-title-above'  ><div class='toggle_content invers-color av-inherit-border-color' ><p><strong>Description:<\/strong><\/p>\n<p class=\"p1\">Many brain functions rely upon coordinated activity in neural circuits and even across multiple brain areas. Yet, how neurons and brain areas communicate and interact to represent sensory input, perform computations, and guide behaviour remains poorly understood. Recent advances in experimental tools allow monitoring and manipulation of neural activity at large scale and with an unprecedented level of detail in animals performing complex behaviors. One of the major challenges in computational neuroscience is to unravel the dynamical computations performed by neural circuits manifested in the emergent collective dynamics.<\/p>\n<p class=\"p1\">The workshop seeks to bring together experimental and computational work to discuss recent advances and to promote interaction between the two fields. Specifically, the workshop will focus on (1) experimental work combining large scale recordings and optogenetic perturbations to investigate internally-generated activity patterns, (2) data-driven computational models of low-dimensional neural dynamics, and (3) biologically-grounded models. We will discuss the utility and the challenges of the modern approaches to internal dynamics associated with motor-related and memory-related processes assessed using large scale neural population activity or field potential recordings. What are the biological insights that can be gained from data-driven models? How can the dynamics of a network-based model be compared to recorded brain activity? And how can we apply perturbations to uncover properties of the internal neural dynamics? These are some of the questions that we aim to address during the workshop.<\/p>\n<p><strong>Speakers:<\/strong><\/p>\n<ul>\n<li>Sirota A.<\/li>\n<li>Einevoll G.<\/li>\n<li>Schaefer A.<\/li>\n<li>Luczak A.<\/li>\n<li>Barry C.<\/li>\n<li>Stark E.<\/li>\n<li>Echeveste R.<\/li>\n<li>Durstewitz D.<\/li>\n<li>Duncker L.<\/li>\n<li>Jazayeri M.<\/li>\n<\/ul>\n<\/div><\/div><\/div><\/section>\n<section class='av_toggle_section av-42vcfs2-9e8fde043fd6c38c66f544b8d07a50d6' ><div role=\"tablist\" class=\"single_toggle\" data-tags=\"{All} \"  ><p id='toggle-the-making-and-breaking-of-ei-balance' data-fake-id='#the-making-and-breaking-of-ei-balance' class='toggler  av-title-above av-inherit-border-color'  role='tab' tabindex='0' aria-controls='the-making-and-breaking-of-ei-balance' data-slide-speed=\"200\" data-title=\"Emergent function in non-random neural networks&lt;br \/&gt;\n&lt;span class=&quot;workshop-subtitle&quot;&gt;&lt;strong&gt;Organizers:&lt;\/strong&gt; Friedemann Zenke, Guillaume Hennequin, Tim Vogels&lt;\/span&gt;\" data-title-open=\"\" data-aria_collapsed=\"Click to expand: Emergent function in non-random neural networks&lt;br \/&gt;\n&lt;span class=&quot;workshop-subtitle&quot;&gt;&lt;strong&gt;Organizers:&lt;\/strong&gt; Friedemann Zenke, Guillaume Hennequin, Tim Vogels&lt;\/span&gt;\" data-aria_expanded=\"Click to collapse: Emergent function in non-random neural networks&lt;br \/&gt;\n&lt;span class=&quot;workshop-subtitle&quot;&gt;&lt;strong&gt;Organizers:&lt;\/strong&gt; Friedemann Zenke, Guillaume Hennequin, Tim Vogels&lt;\/span&gt;\">Emergent function in non-random neural networks<br \/>\n<span class=\"workshop-subtitle\"><strong>Organizers:<\/strong> Friedemann Zenke, Guillaume Hennequin, Tim Vogels<\/span><span class=\"toggle_icon\"><span class=\"vert_icon\"><\/span><span class=\"hor_icon\"><\/span><\/span><\/p><div id='the-making-and-breaking-of-ei-balance' aria-labelledby='toggle-the-making-and-breaking-of-ei-balance' role='region' class='toggle_wrap  av-title-above'  ><div class='toggle_content invers-color av-inherit-border-color' ><p><strong>Description:<\/strong><\/p>\n<p>Computation in the brain occurs through complex interactions in highly structured, non-random networks. Moving beyond traditional approaches based on statistical physics, engineering-based approaches are bringing new vistas on circuit computation, by providing novel ways of i) building artificial yet fully functional model circuits, ii) dissecting their dynamics to identify new circuit mechanisms, and iii) reasoning about population recordings made in diverse brain areas across a range of sensory, motor, and cognitive tasks. Thus, the same &#8220;science of real-world problems&#8221; that is behind the accumulation of increasingly rich neural datasets is now also being recognized as a vast and useful set of tools for their analysis.<\/p>\n<p>This workshop aims at bringing together researchers who build and study structured network models, spiking or otherwise, that serve specific functions. Our speakers will present their neuroscientific work at the confluence of machine learning, optimization, control theory, dynamical systems, and other engineering fields, to help us understand these recent developments, critically evaluate their scope and limitations, and discuss their use for elucidating the neural basis of intelligent behaviour.<\/p>\n<p><strong>Speakers:<\/strong><\/p>\n<ul>\n<li>Kraynyukova N.<\/li>\n<li>Stroud J.<\/li>\n<li>Mejias J.<\/li>\n<li>Ostojic S.<\/li>\n<li>Stock C.<\/li>\n<li>Hennequin G.<\/li>\n<li>Gilra A.<\/li>\n<li>Guetig R., Clopath C.<\/li>\n<li>Zenke F.<\/li>\n<li>Marton C.<\/li>\n<\/ul>\n<\/div><\/div><\/div><\/section>\n<section class='av_toggle_section av-3hfmdgi-73a27843403d4e91d5f98d481fdc9ae2' ><div role=\"tablist\" class=\"single_toggle\" data-tags=\"{All} \"  ><p id='toggle-understanding-computations-of-basal-nervous-systems-from-paramecium-to-jellyfish' data-fake-id='#understanding-computations-of-basal-nervous-systems-from-paramecium-to-jellyfish' class='toggler  av-title-above av-inherit-border-color'  role='tab' tabindex='0' aria-controls='understanding-computations-of-basal-nervous-systems-from-paramecium-to-jellyfish' data-slide-speed=\"200\" data-title=\"Neural computation of behaviorally relevant stimuli&lt;br \/&gt;\n&lt;span class=&quot;workshop-subtitle&quot;&gt;&lt;strong&gt;Organizers:&lt;\/strong&gt; Jan Benda, R\u00fcdiger Krahe&lt;\/span&gt;\" data-title-open=\"\" data-aria_collapsed=\"Click to expand: Neural computation of behaviorally relevant stimuli&lt;br \/&gt;\n&lt;span class=&quot;workshop-subtitle&quot;&gt;&lt;strong&gt;Organizers:&lt;\/strong&gt; Jan Benda, R\u00fcdiger Krahe&lt;\/span&gt;\" data-aria_expanded=\"Click to collapse: Neural computation of behaviorally relevant stimuli&lt;br \/&gt;\n&lt;span class=&quot;workshop-subtitle&quot;&gt;&lt;strong&gt;Organizers:&lt;\/strong&gt; Jan Benda, R\u00fcdiger Krahe&lt;\/span&gt;\">Neural computation of behaviorally relevant stimuli<br \/>\n<span class=\"workshop-subtitle\"><strong>Organizers:<\/strong> Jan Benda, R\u00fcdiger Krahe<\/span><span class=\"toggle_icon\"><span class=\"vert_icon\"><\/span><span class=\"hor_icon\"><\/span><\/span><\/p><div id='understanding-computations-of-basal-nervous-systems-from-paramecium-to-jellyfish' aria-labelledby='toggle-understanding-computations-of-basal-nervous-systems-from-paramecium-to-jellyfish' role='region' class='toggle_wrap  av-title-above'  ><div class='toggle_content invers-color av-inherit-border-color' ><p><strong>Description:<\/strong><\/p>\n<p class=\"p1\">One influential contribution of computational neuroscience has been to emphasize the connection between the statistics of sensory stimuli and the design of the respective sensory systems. Although the importance of using natural stimuli for probing neural responses is well recognized, the term is often used too broadly to include all stimuli that are not entirely artificial. On a closer look, the specific way an animal moves and interacts with its specific environment creates natural stimuli with statistics that are species-specific. Obviously, only a subset of stimuli of the full species-specific stimulus set is relevant for behavioral decisions of the animal. Determining what parts of the full stimulus set are behaviorally relevant is a highly non-trivial task that requires interrogating the animals under natural conditions. The results of this quest can then be used to probe the nervous system and try to understand mechanisms of sensory processing as well as their evolution.<\/p>\n<p class=\"p1\">Our symposium showcases a number of different approaches and sensory systems in which behaviorally relevant natural stimuli have been quantified, and their implications for neural processing have been investigated. The three speakers of the first block address behaviors related to object detection. Paul Szyszka introduces a rapid coding scheme used by insect olfactory systems for tracking odor plumes. Jacob Engelmann shows how sensory flow in the context of active electrolocation can be used to optimize stimulus detection. Active echolocation is the topic of the contribution by Yossi Yovel. He investigates how bats sample their environment prior to complex flight maneuvers. After the coffee break, we continue with communication signals. Julie Elie sheds light on the meaning and processing of the different song elements of zebra finches. J\u00f6rg Henninger obtained data on electrosensory scenes experienced by courting weakly electric fish in their natural habitats in the Central American rainforest that raise disturbing questions about our understanding of sensory processing, not only in this well-studied system.<\/p>\n<p><strong>Speakers:<\/strong><\/p>\n<ul>\n<li>Szyszka P.<\/li>\n<li>Yovel Y.<\/li>\n<li>St\u00f6ckl A.<\/li>\n<li>Engelmann J.<\/li>\n<li>Elie J.<\/li>\n<li>Henninger J.<\/li>\n<\/ul>\n<\/div><\/div><\/div><\/section>\n<section class='av_toggle_section av-35v7syq-1933c9228e6c2ca8efa1019bdea4924c' ><div role=\"tablist\" class=\"single_toggle\" data-tags=\"{All} \"  ><p id='toggle-visuomotor-coordination-from-physiology-to-control-systems' data-fake-id='#visuomotor-coordination-from-physiology-to-control-systems' class='toggler  av-title-above av-inherit-border-color'  role='tab' tabindex='0' aria-controls='visuomotor-coordination-from-physiology-to-control-systems' data-slide-speed=\"200\" data-title=\"Representational dynamics - How can we understand the temporal evolution of distributed brain activity patterns?&lt;br \/&gt;\n&lt;span class=&quot;workshop-subtitle&quot;&gt;&lt;strong&gt;Organizers:&lt;\/strong&gt; Tim Kietzmann, Niko Kriegeskorte&lt;\/span&gt;\" data-title-open=\"\" data-aria_collapsed=\"Click to expand: Representational dynamics - How can we understand the temporal evolution of distributed brain activity patterns?&lt;br \/&gt;\n&lt;span class=&quot;workshop-subtitle&quot;&gt;&lt;strong&gt;Organizers:&lt;\/strong&gt; Tim Kietzmann, Niko Kriegeskorte&lt;\/span&gt;\" data-aria_expanded=\"Click to collapse: Representational dynamics - How can we understand the temporal evolution of distributed brain activity patterns?&lt;br \/&gt;\n&lt;span class=&quot;workshop-subtitle&quot;&gt;&lt;strong&gt;Organizers:&lt;\/strong&gt; Tim Kietzmann, Niko Kriegeskorte&lt;\/span&gt;\">Representational dynamics - How can we understand the temporal evolution of distributed brain activity patterns?<br \/>\n<span class=\"workshop-subtitle\"><strong>Organizers:<\/strong> Tim Kietzmann, Niko Kriegeskorte<\/span><span class=\"toggle_icon\"><span class=\"vert_icon\"><\/span><span class=\"hor_icon\"><\/span><\/span><\/p><div id='visuomotor-coordination-from-physiology-to-control-systems' aria-labelledby='toggle-visuomotor-coordination-from-physiology-to-control-systems' role='region' class='toggle_wrap  av-title-above'  ><div class='toggle_content invers-color av-inherit-border-color' ><p><strong>Description:<\/strong><\/p>\n<p class=\"p1\">Brain information processing is inherently multivariate and highly dynamic. Perception, cognition, and motor control all rely on rapid recurrent computations, with representations emerging, transmuting, and waning according to the brain&#8217;s own rhythm as information flows continually and bidirectionally between interacting areas. The field is increasingly acquiring multichannel measurements with high temporal resolution in humans (using MEG, EEG and ECog) and animals (using multi-electrode recordings and ECog). Advances in technologies for measuring brain activity will further increase the spatial and temporal resolution at which we can observe brain activity. The challenge is how to make sense of the detailed signatures of brain information processing that lie latent in such data. A number of studies have engaged the complexity of spatiotemporal brain-activity patterns by analysing patterns of activity as a function of time. Established methods include temporal-window pattern decoding and representational similarity analysis, as well as visualisations of dynamic representational trajectories using dimensionality reduction methods. Dynamic multivariate analysis is going to be a key element of systems neuroscience in humans and nonhuman models. However, it is unclear how to best characterise and visualise dynamic representations, what features to focus on (evoked, induced, frequency-dependent), how to analyse causal interactions and information exchange between brain areas, and how to perform inference comparing alternative models of brain information processing. This workshop brings together leading researchers in the field for a set of highly interactive talks that communicate particular novel neuroscientific insights along with a detailed explanation of the more generally applicable analyses that enabled the insights.<\/p>\n<p><strong>Speakers:<\/strong><\/p>\n<ul>\n<li>Woolgar A.<\/li>\n<li>Cichy R.<\/li>\n<li>Isik L.<\/li>\n<li>Kietzmann T.<\/li>\n<li>DiCarlo J.<\/li>\n<li>Ganguli S.<\/li>\n<\/ul>\n<\/div><\/div><\/div><\/section>\n<section class='av_toggle_section av-8u69he-5fcf7ebddad4df8e8d0ad394895f9578' ><div role=\"tablist\" class=\"single_toggle\" data-tags=\"{All} \"  ><p id='toggle-neural-computation-through-recurrent-dynamics-from-theory-to-experiment-and-back' data-fake-id='#neural-computation-through-recurrent-dynamics-from-theory-to-experiment-and-back' class='toggler  av-title-above av-inherit-border-color'  role='tab' tabindex='0' aria-controls='neural-computation-through-recurrent-dynamics-from-theory-to-experiment-and-back' data-slide-speed=\"200\" data-title=\"Neural dynamics underlying cognitive processing in humans&lt;br \/&gt;\n&lt;span class=&quot;workshop-subtitle&quot;&gt;&lt;strong&gt;Organizers:&lt;\/strong&gt; Johannes Sarnthein, Bryan Strange&lt;\/span&gt;\" data-title-open=\"\" data-aria_collapsed=\"Click to expand: Neural dynamics underlying cognitive processing in humans&lt;br \/&gt;\n&lt;span class=&quot;workshop-subtitle&quot;&gt;&lt;strong&gt;Organizers:&lt;\/strong&gt; Johannes Sarnthein, Bryan Strange&lt;\/span&gt;\" data-aria_expanded=\"Click to collapse: Neural dynamics underlying cognitive processing in humans&lt;br \/&gt;\n&lt;span class=&quot;workshop-subtitle&quot;&gt;&lt;strong&gt;Organizers:&lt;\/strong&gt; Johannes Sarnthein, Bryan Strange&lt;\/span&gt;\">Neural dynamics underlying cognitive processing in humans<br \/>\n<span class=\"workshop-subtitle\"><strong>Organizers:<\/strong> Johannes Sarnthein, Bryan Strange<\/span><span class=\"toggle_icon\"><span class=\"vert_icon\"><\/span><span class=\"hor_icon\"><\/span><\/span><\/p><div id='neural-computation-through-recurrent-dynamics-from-theory-to-experiment-and-back' aria-labelledby='toggle-neural-computation-through-recurrent-dynamics-from-theory-to-experiment-and-back' role='region' class='toggle_wrap  av-title-above'  ><div class='toggle_content invers-color av-inherit-border-color' ><p><strong>Description:<\/strong><\/p>\n<p>Human brains can perform well in complex tasks. Fine-grained electrophysiological correlates of cognitive performance have become available with the help of patients that were implanted with electrodes. The electrodes record activity ranging from single neuron action potentials to neuronal assembly activity in local field potentials. Compared to non-invasive methods, these recordings provide a much more detailed picture of neuronal computations.<\/p>\n<p>In our workshop, Florian Mormann will first present how long-term memory of complex items is reflected in long-term recordings of concept cells in the hippocampus. Bryan Strange will present how neuronal assembly activity in the amygdala explains emotional processing. Johannes Sarnthein will show how verbal working memory is mediated by both hippocampal neuronal spiking as well as long-range synchrony in hippocampal-cortical oscillations. Finally, Leila Reddy will present how single neurons in the human hippocampus encode associations between related stimuli.<\/p>\n<p><strong>Speakers:<\/strong><\/p>\n<ul>\n<li>Mormann F.<\/li>\n<li>\u00a0Strange B.<\/li>\n<li>Sarnthein J.<\/li>\n<li>Reddy L.<\/li>\n<\/ul>\n<\/div><\/div><\/div><\/section>\n<section class='av_toggle_section av-29v86ki-82a8856bfb378b897a85c7c9044e1362' ><div role=\"tablist\" class=\"single_toggle\" data-tags=\"{All} \"  ><p id='toggle-spikes-in-a-haystack-dimensionality-reduction-for-neural-data-and-unsupervised-detection-of-spiking-patterns-and-sequences' data-fake-id='#spikes-in-a-haystack-dimensionality-reduction-for-neural-data-and-unsupervised-detection-of-spiking-patterns-and-sequences' class='toggler  av-title-above av-inherit-border-color'  role='tab' tabindex='0' aria-controls='spikes-in-a-haystack-dimensionality-reduction-for-neural-data-and-unsupervised-detection-of-spiking-patterns-and-sequences' data-slide-speed=\"200\" data-title=\"Dimensions of Neural Coding, Computation and Communication&lt;br \/&gt;\n&lt;span class=&quot;workshop-subtitle&quot;&gt;&lt;strong&gt;Organizers:&lt;\/strong&gt; Andreas Herz, Alon Rubin&lt;\/span&gt;\" data-title-open=\"\" data-aria_collapsed=\"Click to expand: Dimensions of Neural Coding, Computation and Communication&lt;br \/&gt;\n&lt;span class=&quot;workshop-subtitle&quot;&gt;&lt;strong&gt;Organizers:&lt;\/strong&gt; Andreas Herz, Alon Rubin&lt;\/span&gt;\" data-aria_expanded=\"Click to collapse: Dimensions of Neural Coding, Computation and Communication&lt;br \/&gt;\n&lt;span class=&quot;workshop-subtitle&quot;&gt;&lt;strong&gt;Organizers:&lt;\/strong&gt; Andreas Herz, Alon Rubin&lt;\/span&gt;\">Dimensions of Neural Coding, Computation and Communication<br \/>\n<span class=\"workshop-subtitle\"><strong>Organizers:<\/strong> Andreas Herz, Alon Rubin<\/span><span class=\"toggle_icon\"><span class=\"vert_icon\"><\/span><span class=\"hor_icon\"><\/span><\/span><\/p><div id='spikes-in-a-haystack-dimensionality-reduction-for-neural-data-and-unsupervised-detection-of-spiking-patterns-and-sequences' aria-labelledby='toggle-spikes-in-a-haystack-dimensionality-reduction-for-neural-data-and-unsupervised-detection-of-spiking-patterns-and-sequences' role='region' class='toggle_wrap  av-title-above'  ><div class='toggle_content invers-color av-inherit-border-color' ><p><strong>Description:<\/strong><\/p>\n<p class=\"p1\">Recent advances in multi-electrode and optical imaging technologies enable simultaneous recordings of hundreds or even thousands of neurons. Since neural coding, computation, and communication likely rely on coordinated activity patterns across large cell populations, such data facilitate the study of the global structure of neural function, which cannot be revealed by analyzing functional attributes at the single-neuron level.<\/p>\n<p class=\"p1\">While the activity of a population with N neurons can be pictured in an N-dimensional space, their behaviorally relevant dynamics under natural conditions may reside within much lower-dimensional manifolds. Methods for dimensionality estimation and dimensionality reduction can help to identify these manifolds, study the collective neural dynamics and understand population codes and their intrinsic correlation structures and trial-to-trial variability. This approach is relevant for exploratory research and when testing hypotheses, from neural connectivity to overall neural functionality.<\/p>\n<p class=\"p1\">As concepts and methods related to this topic are rapidly developing, this workshop cannot (and should not try to) provide a single polished view but will rather present complementary views about how to define effective spaces of neural activity and how to interpret the experimentally observed phenomena. We will do so from various angles and for different (sensory, motor, and cognitive) neuronal systems &#8211; and hope to trigger discussions amongst and between theorists and experimentalists.<\/p>\n<p><strong>Speakers:<\/strong><\/p>\n<ul>\n<li>Bittner S.<\/li>\n<li>Rubin A.<\/li>\n<li>Lewallen S.<\/li>\n<li>Low R.<\/li>\n<li>Engel T.<\/li>\n<li>Machens C.<\/li>\n<\/ul>\n<\/div><\/div><\/div><\/section>\n<section class='av_toggle_section av-1kj8e4i-24d6ec80ea77f3e19453d61083300b5a' ><div role=\"tablist\" class=\"single_toggle\" data-tags=\"{All} \"  ><p id='toggle-neuronal-processing-of-social-cues' data-fake-id='#neuronal-processing-of-social-cues' class='toggler  av-title-above av-inherit-border-color'  role='tab' tabindex='0' aria-controls='neuronal-processing-of-social-cues' data-slide-speed=\"200\" data-title=\"Resonance in neurons and neural networks: theoretical and experimental approaches&lt;br \/&gt;\n&lt;span class=&quot;workshop-subtitle&quot;&gt;&lt;strong&gt;Organizers:&lt;\/strong&gt; Antonio Carlos Roque, Rodrigo Felipe de Oliveira Pena&lt;\/span&gt;\" data-title-open=\"\" data-aria_collapsed=\"Click to expand: Resonance in neurons and neural networks: theoretical and experimental approaches&lt;br \/&gt;\n&lt;span class=&quot;workshop-subtitle&quot;&gt;&lt;strong&gt;Organizers:&lt;\/strong&gt; Antonio Carlos Roque, Rodrigo Felipe de Oliveira Pena&lt;\/span&gt;\" data-aria_expanded=\"Click to collapse: Resonance in neurons and neural networks: theoretical and experimental approaches&lt;br \/&gt;\n&lt;span class=&quot;workshop-subtitle&quot;&gt;&lt;strong&gt;Organizers:&lt;\/strong&gt; Antonio Carlos Roque, Rodrigo Felipe de Oliveira Pena&lt;\/span&gt;\">Resonance in neurons and neural networks: theoretical and experimental approaches<br \/>\n<span class=\"workshop-subtitle\"><strong>Organizers:<\/strong> Antonio Carlos Roque, Rodrigo Felipe de Oliveira Pena<\/span><span class=\"toggle_icon\"><span class=\"vert_icon\"><\/span><span class=\"hor_icon\"><\/span><\/span><\/p><div id='neuronal-processing-of-social-cues' aria-labelledby='toggle-neuronal-processing-of-social-cues' role='region' class='toggle_wrap  av-title-above'  ><div class='toggle_content invers-color av-inherit-border-color' ><p><strong>Description:<\/strong><\/p>\n<p class=\"p1\">Neurons can exhibit subthreshold voltage resonance to oscillatory input current. For specific frequencies of input current oscillations, the response membrane voltage displays enhanced amplitude of oscillations. This can have functional implications for neuronal selectivity in cases of resonant neurons embedded in networks that support oscillatory states of different frequencies. The resonance properties of a neuron depend on its intrinsic passive and active characteristics, e.g. morphology and ionic current properties. To unveil the roles of these characteristics on resonance and their impact at network level, several theoretical and experimental studies are being currently undertaken.<\/p>\n<p class=\"p1\">The objective of this workshop is to gather theoreticians and experimentalists who work on this subject to present and discuss their recent work. The range of topics will cover from dynamical systems-based mathematical analysis, through biophysical neuron modeling, and neurophysiological experiments. The workshop contents will involve: role of specific ionic currents, how to determine resonance by means of physiologically measurable parameters, stochastic resonance, relation between subthreshold resonance and spiking characteristics, bifurcation diagrams, and influence of single neuron resonance on network behavior.<\/p>\n<p><strong>Speakers:<\/strong><\/p>\n<ul>\n<li>Pena R.F.O.<\/li>\n<li>Lindner B.<\/li>\n<li>Roth A.<\/li>\n<li>Canavier C.<\/li>\n<li>Rotstein H.G.<\/li>\n<li>Nadim F.<\/li>\n<li>Palmigiano A.<\/li>\n<\/ul>\n<\/div><\/div><\/div><\/section>\n<section class='av_toggle_section av-180poo2-1661b750d754416ba327257d1fb7bc09' ><div role=\"tablist\" class=\"single_toggle\" data-tags=\"{All} \"  ><p id='toggle-neural-oscillations-in-memory-and-navigation' data-fake-id='#neural-oscillations-in-memory-and-navigation' class='toggler  av-title-above av-inherit-border-color'  role='tab' tabindex='0' aria-controls='neural-oscillations-in-memory-and-navigation' data-slide-speed=\"200\" data-title=\"Deciphering neural circuits for innate behaviors: experimental and theoretical approaches in model organisms&lt;br \/&gt;\n&lt;span class=&quot;workshop-subtitle&quot;&gt;&lt;strong&gt;Organizers:&lt;\/strong&gt; Marina Wosniack&lt;\/span&gt;\" data-title-open=\"\" data-aria_collapsed=\"Click to expand: Deciphering neural circuits for innate behaviors: experimental and theoretical approaches in model organisms&lt;br \/&gt;\n&lt;span class=&quot;workshop-subtitle&quot;&gt;&lt;strong&gt;Organizers:&lt;\/strong&gt; Marina Wosniack&lt;\/span&gt;\" data-aria_expanded=\"Click to collapse: Deciphering neural circuits for innate behaviors: experimental and theoretical approaches in model organisms&lt;br \/&gt;\n&lt;span class=&quot;workshop-subtitle&quot;&gt;&lt;strong&gt;Organizers:&lt;\/strong&gt; Marina Wosniack&lt;\/span&gt;\">Deciphering neural circuits for innate behaviors: experimental and theoretical approaches in model organisms<br \/>\n<span class=\"workshop-subtitle\"><strong>Organizers:<\/strong> Marina Wosniack<\/span><span class=\"toggle_icon\"><span class=\"vert_icon\"><\/span><span class=\"hor_icon\"><\/span><\/span><\/p><div id='neural-oscillations-in-memory-and-navigation' aria-labelledby='toggle-neural-oscillations-in-memory-and-navigation' role='region' class='toggle_wrap  av-title-above'  ><div class='toggle_content invers-color av-inherit-border-color' ><p><strong>Description:<\/strong><\/p>\n<p class=\"p1\">Model organisms, such as fruit flies and nematode worms, have been essential to our understanding of nervous system function. Their relatively small and simple nervous system has allowed us to identify neurons and circuits across animals. Additionally, a wealth of genetic tools is available to label specific sets of neurons and manipulate them in intricate ways. In combination with cutting-edge imaging and electrophysiology techniques, we can examine the computations performed by individual neurons and neuronal populations. Thus, it is not a surprise that works on model organisms have unraveled milestone discoveries in nervous system development and function.<\/p>\n<p class=\"p1\">Understanding the neural circuit basis of behavior is a challenging goal in modern neuroscience. In fact, such a complex problem requires a multidisciplinary approach involving genetics, molecular biology, optics, ethology, neurobiology, and mathematical modeling. This strategy is most efficient when using model organisms, as they can produce complex motor behaviors and sophisticated imaging techniques can now record neuronal activity and the individual behavior simultaneously.<\/p>\n<p><strong>Speakers:<\/strong><\/p>\n<ul>\n<li>Berni J.<\/li>\n<li>Jayaraman V.<\/li>\n<li>Hermundstad, A.<\/li>\n<li>Straw A.<\/li>\n<li>Wosniack M.<\/li>\n<li>Silies M.<\/li>\n<\/ul>\n<\/div><\/div><\/div><\/section>\n<section class='av_toggle_section av-nmwxky-4127e7bbe3ab7a78556abaa4833a1dee' ><div role=\"tablist\" class=\"single_toggle\" data-tags=\"{All} \"  ><p id='toggle-neuronal-intelligence-narrowing-the-gap-between-neuroscience-and-ai' data-fake-id='#neuronal-intelligence-narrowing-the-gap-between-neuroscience-and-ai' class='toggler  av-title-above av-inherit-border-color'  role='tab' tabindex='0' aria-controls='neuronal-intelligence-narrowing-the-gap-between-neuroscience-and-ai' data-slide-speed=\"200\" data-title=\"Neuronal Intelligence: Narrowing the gap between neuroscience and AI&lt;br \/&gt;\n&lt;span class=&quot;workshop-subtitle&quot;&gt;&lt;strong&gt;Organizers:&lt;\/strong&gt; Fabian Sinz, Matthias Bethge&lt;\/span&gt;\" data-title-open=\"\" data-aria_collapsed=\"Click to expand: Neuronal Intelligence: Narrowing the gap between neuroscience and AI&lt;br \/&gt;\n&lt;span class=&quot;workshop-subtitle&quot;&gt;&lt;strong&gt;Organizers:&lt;\/strong&gt; Fabian Sinz, Matthias Bethge&lt;\/span&gt;\" data-aria_expanded=\"Click to collapse: Neuronal Intelligence: Narrowing the gap between neuroscience and AI&lt;br \/&gt;\n&lt;span class=&quot;workshop-subtitle&quot;&gt;&lt;strong&gt;Organizers:&lt;\/strong&gt; Fabian Sinz, Matthias Bethge&lt;\/span&gt;\">Neuronal Intelligence: Narrowing the gap between neuroscience and AI<br \/>\n<span class=\"workshop-subtitle\"><strong>Organizers:<\/strong> Fabian Sinz, Matthias Bethge<\/span><span class=\"toggle_icon\"><span class=\"vert_icon\"><\/span><span class=\"hor_icon\"><\/span><\/span><\/p><div id='neuronal-intelligence-narrowing-the-gap-between-neuroscience-and-ai' aria-labelledby='toggle-neuronal-intelligence-narrowing-the-gap-between-neuroscience-and-ai' role='region' class='toggle_wrap  av-title-above'  ><div class='toggle_content invers-color av-inherit-border-color' ><p><strong>Description:<\/strong><\/p>\n<p class=\"p1\">While advances in deep learning methods have enabled impressive strides in artificial intelligence (AI) and changed our everyday lives, they still lack a fundamental feature of biological intelligence: robustness and generalization beyond the immediate data it was trained on. Current models in AI derive their power from the ability to fit almost any arbitrary function. However, the capability for universal approximation is as much a blessing as it is a curse, since it is hard to control the behavior of the models outside the domain of training examples the model was exposed to. In stark contrast, most vertebrate brains operate well under extreme changes in signal reliability (e.g., night vs. day) and statistics (e.g., rainforest vs. desert). What are the implicit assumptions and computational principles that the brain uses to achieve this level of robustness?<\/p>\n<p><strong>Speakers:<\/strong><\/p>\n<ul>\n<li>Sinz, F.<\/li>\n<li>Reimer, J.<\/li>\n<li>Ecker, A.<\/li>\n<li>Funke, C.<\/li>\n<li>Vasudeva Raju R.<\/li>\n<li>Tolias, A.<\/li>\n<\/ul>\n<\/div><\/div><\/div><\/section>\n<\/div><\/div><\/div><\/div><\/div><!-- close content main div --><\/div><\/div><div id='sub_menu1'  class='av-submenu-container av-kn79ec8n-0555ab7e047a3deee38881aa7f3245e5 main_color  avia-builder-el-51  el_after_av_section  avia-builder-el-last  medienecho-menu submenu-not-first container_wrap fullsize' style='z-index:301' ><div class='container av-menu-mobile-disabled av-submenu-pos-center'><ul id=\"menu-main-menu\" class=\"av-subnav-menu\" role=\"menu\"><li role=\"menuitem\" id=\"menu-item-39248\" class=\"menu-item menu-item-type-post_type menu-item-object-page menu-item-top-level menu-item-top-level-1\"><a href=\"https:\/\/bernstein-network.de\/en\/bernstein-conference\/past-future-bernstein-conferences\/bernstein-conference-2025\/\" tabindex=\"0\"><span class=\"avia-bullet\"><\/span><span class=\"avia-menu-text\">2025<\/span><span class=\"avia-menu-fx\"><span class=\"avia-arrow-wrap\"><span class=\"avia-arrow\"><\/span><\/span><\/span><\/a><\/li>\n<li role=\"menuitem\" id=\"menu-item-31288\" class=\"menu-item menu-item-type-post_type menu-item-object-page menu-item-top-level menu-item-top-level-2\"><a href=\"https:\/\/bernstein-network.de\/en\/bernstein-conference\/past-future-bernstein-conferences\/bernstein-conference-2024\/\" tabindex=\"0\"><span class=\"avia-bullet\"><\/span><span class=\"avia-menu-text\">2024<\/span><span class=\"avia-menu-fx\"><span class=\"avia-arrow-wrap\"><span class=\"avia-arrow\"><\/span><\/span><\/span><\/a><\/li>\n<li role=\"menuitem\" id=\"menu-item-26733\" class=\"menu-item menu-item-type-post_type menu-item-object-page menu-item-top-level menu-item-top-level-3\"><a href=\"https:\/\/bernstein-network.de\/en\/bernstein-conference\/past-future-bernstein-conferences\/bernstein-conference-2023\/\" tabindex=\"0\"><span class=\"avia-bullet\"><\/span><span class=\"avia-menu-text\">2023<\/span><span class=\"avia-menu-fx\"><span class=\"avia-arrow-wrap\"><span class=\"avia-arrow\"><\/span><\/span><\/span><\/a><\/li>\n<li role=\"menuitem\" id=\"menu-item-27879\" class=\"menu-item menu-item-type-post_type menu-item-object-page menu-item-top-level menu-item-top-level-4\"><a href=\"https:\/\/bernstein-network.de\/en\/bernstein-conference\/past-future-bernstein-conferences\/bernstein-conference-2022-2\/\" tabindex=\"0\"><span class=\"avia-bullet\"><\/span><span class=\"avia-menu-text\">2022<\/span><span class=\"avia-menu-fx\"><span class=\"avia-arrow-wrap\"><span class=\"avia-arrow\"><\/span><\/span><\/span><\/a><\/li>\n<li role=\"menuitem\" id=\"menu-item-16159\" class=\"menu-item menu-item-type-custom menu-item-object-custom menu-item-top-level menu-item-top-level-5\"><a href=\"https:\/\/bernstein-network.de\/en\/bernstein-conference\/past-future-bernstein-conferences\/bernstein-conference-2021\/\" tabindex=\"0\"><span class=\"avia-bullet\"><\/span><span class=\"avia-menu-text\">2021<\/span><span class=\"avia-menu-fx\"><span class=\"avia-arrow-wrap\"><span class=\"avia-arrow\"><\/span><\/span><\/span><\/a><\/li>\n<li role=\"menuitem\" id=\"menu-item-9772\" class=\"menu-item menu-item-type-custom menu-item-object-custom menu-item-top-level menu-item-top-level-6\"><a href=\"https:\/\/bernstein-network.de\/en\/bernstein-conference\/past-future-bernstein-conferences\/bernstein-conference-2020\/\" tabindex=\"0\"><span class=\"avia-bullet\"><\/span><span class=\"avia-menu-text\">2020<\/span><span class=\"avia-menu-fx\"><span class=\"avia-arrow-wrap\"><span class=\"avia-arrow\"><\/span><\/span><\/span><\/a><\/li>\n<li role=\"menuitem\" id=\"menu-item-9773\" class=\"menu-item menu-item-type-custom menu-item-object-custom menu-item-top-level menu-item-top-level-7\"><a href=\"https:\/\/bernstein-network.de\/en\/bernstein-conference\/past-future-bernstein-conferences\/bernstein-conference-2019\/\" tabindex=\"0\"><span class=\"avia-bullet\"><\/span><span class=\"avia-menu-text\">2019<\/span><span class=\"avia-menu-fx\"><span class=\"avia-arrow-wrap\"><span class=\"avia-arrow\"><\/span><\/span><\/span><\/a><\/li>\n<li role=\"menuitem\" id=\"menu-item-9774\" class=\"menu-item menu-item-type-custom menu-item-object-custom menu-item-top-level menu-item-top-level-8\"><a href=\"https:\/\/bernstein-network.de\/en\/bernstein-conference\/past-future-bernstein-conferences\/bernstein-conference-2018\/\" tabindex=\"0\"><span class=\"avia-bullet\"><\/span><span class=\"avia-menu-text\">2018<\/span><span class=\"avia-menu-fx\"><span class=\"avia-arrow-wrap\"><span class=\"avia-arrow\"><\/span><\/span><\/span><\/a><\/li>\n<li role=\"menuitem\" id=\"menu-item-9775\" class=\"menu-item menu-item-type-custom menu-item-object-custom menu-item-top-level menu-item-top-level-9\"><a href=\"https:\/\/bernstein-network.de\/en\/bernstein-conference\/past-future-bernstein-conferences\/bernstein-conference-2017\/\" tabindex=\"0\"><span class=\"avia-bullet\"><\/span><span class=\"avia-menu-text\">2017<\/span><span class=\"avia-menu-fx\"><span class=\"avia-arrow-wrap\"><span class=\"avia-arrow\"><\/span><\/span><\/span><\/a><\/li>\n<li role=\"menuitem\" id=\"menu-item-9776\" class=\"menu-item menu-item-type-custom menu-item-object-custom menu-item-top-level menu-item-top-level-10\"><a href=\"https:\/\/bernstein-network.de\/en\/bernstein-conference\/past-future-bernstein-conferences\/bernstein-conference-2016\" tabindex=\"0\"><span class=\"avia-bullet\"><\/span><span class=\"avia-menu-text\">2016<\/span><span class=\"avia-menu-fx\"><span class=\"avia-arrow-wrap\"><span class=\"avia-arrow\"><\/span><\/span><\/span><\/a><\/li>\n<li role=\"menuitem\" id=\"menu-item-9777\" class=\"menu-item menu-item-type-custom menu-item-object-custom menu-item-top-level menu-item-top-level-11\"><a href=\"https:\/\/bernstein-network.de\/en\/bernstein-conference\/past-future-bernstein-conferences\/bernstein-conference-2015\/\" tabindex=\"0\"><span class=\"avia-bullet\"><\/span><span class=\"avia-menu-text\">2015<\/span><span class=\"avia-menu-fx\"><span class=\"avia-arrow-wrap\"><span class=\"avia-arrow\"><\/span><\/span><\/span><\/a><\/li>\n<li role=\"menuitem\" id=\"menu-item-9778\" class=\"menu-item menu-item-type-custom menu-item-object-custom menu-item-top-level menu-item-top-level-12\"><a href=\"https:\/\/bernstein-network.de\/en\/bernstein-conference\/past-future-bernstein-conferences\/bernstein-conference-2014\/\" tabindex=\"0\"><span class=\"avia-bullet\"><\/span><span class=\"avia-menu-text\">2014<\/span><span class=\"avia-menu-fx\"><span class=\"avia-arrow-wrap\"><span class=\"avia-arrow\"><\/span><\/span><\/span><\/a><\/li>\n<li role=\"menuitem\" id=\"menu-item-9779\" class=\"menu-item menu-item-type-custom menu-item-object-custom menu-item-top-level menu-item-top-level-13\"><a href=\"https:\/\/bernstein-network.de\/en\/bernstein-conference\/past-future-bernstein-conferences\/bernstein-conference-2013\/\" tabindex=\"0\"><span class=\"avia-bullet\"><\/span><span class=\"avia-menu-text\">2013<\/span><span class=\"avia-menu-fx\"><span class=\"avia-arrow-wrap\"><span class=\"avia-arrow\"><\/span><\/span><\/span><\/a><\/li>\n<li role=\"menuitem\" id=\"menu-item-9780\" class=\"menu-item menu-item-type-custom menu-item-object-custom menu-item-top-level menu-item-top-level-14\"><a href=\"https:\/\/bernstein-network.de\/en\/bernstein-conference\/past-future-bernstein-conferences\/bernstein-conference-2012\" tabindex=\"0\"><span class=\"avia-bullet\"><\/span><span class=\"avia-menu-text\">2012<\/span><span class=\"avia-menu-fx\"><span class=\"avia-arrow-wrap\"><span class=\"avia-arrow\"><\/span><\/span><\/span><\/a><\/li>\n<li 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