CINXE.COM

<!DOCTYPE html> <html lang="en" xmlns:fb="http://www.facebook.com/2008/fbml" class="wf-loading"> <head prefix="og: https://ogp.me/ns# fb: https://ogp.me/ns/fb# academia: https://ogp.me/ns/fb/academia#"> <meta charset="utf-8"> <meta name=viewport content="width=device-width, initial-scale=1"> <meta rel="search" type="application/opensearchdescription+xml" href="/open_search.xml" title="Academia.edu"> <title>Micah Richert - Academia.edu</title>  <link href="//a.academia-assets.com/images/favicons/favicon-production.ico" rel="shortcut icon" type="image/vnd.microsoft.icon"> <link rel="apple-touch-icon" sizes="57x57" href="//a.academia-assets.com/images/favicons/apple-touch-icon-57x57.png"> <link rel="apple-touch-icon" sizes="60x60" href="//a.academia-assets.com/images/favicons/apple-touch-icon-60x60.png"> <link rel="apple-touch-icon" sizes="72x72" href="//a.academia-assets.com/images/favicons/apple-touch-icon-72x72.png"> <link rel="apple-touch-icon" sizes="76x76" href="//a.academia-assets.com/images/favicons/apple-touch-icon-76x76.png"> <link rel="apple-touch-icon" sizes="114x114" href="//a.academia-assets.com/images/favicons/apple-touch-icon-114x114.png"> <link rel="apple-touch-icon" sizes="120x120" href="//a.academia-assets.com/images/favicons/apple-touch-icon-120x120.png"> <link rel="apple-touch-icon" sizes="144x144" href="//a.academia-assets.com/images/favicons/apple-touch-icon-144x144.png"> <link rel="apple-touch-icon" sizes="152x152" href="//a.academia-assets.com/images/favicons/apple-touch-icon-152x152.png"> <link rel="apple-touch-icon" sizes="180x180" href="//a.academia-assets.com/images/favicons/apple-touch-icon-180x180.png"> <link rel="icon" type="image/png" href="//a.academia-assets.com/images/favicons/favicon-32x32.png" sizes="32x32"> <link rel="icon" type="image/png" href="//a.academia-assets.com/images/favicons/favicon-194x194.png" sizes="194x194"> <link rel="icon" type="image/png" href="//a.academia-assets.com/images/favicons/favicon-96x96.png" sizes="96x96"> <link rel="icon" type="image/png" href="//a.academia-assets.com/images/favicons/android-chrome-192x192.png" sizes="192x192"> <link rel="icon" type="image/png" href="//a.academia-assets.com/images/favicons/favicon-16x16.png" sizes="16x16"> <link rel="manifest" href="//a.academia-assets.com/images/favicons/manifest.json"> <meta name="msapplication-TileColor" content="#2b5797"> <meta name="msapplication-TileImage" content="//a.academia-assets.com/images/favicons/mstile-144x144.png"> <meta name="theme-color" content="#ffffff"> <script> window.performance && window.performance.measure && window.performance.measure("Time To First Byte", "requestStart", "responseStart"); </script> <script> (function() { if (!window.URLSearchParams || !window.history || !window.history.replaceState) { return; } var searchParams = new URLSearchParams(window.location.search); var paramsToDelete = [ 'fs', 'sm', 'swp', 'iid', 'nbs', 'rcc', // related content category 'rcpos', // related content carousel position 'rcpg', // related carousel page 'rchid', // related content hit id 'f_ri', // research interest id, for SEO tracking 'f_fri', // featured research interest, for SEO tracking (param key without value) 'f_rid', // from research interest directory for SEO tracking 'f_loswp', // from research interest pills on LOSWP sidebar for SEO tracking 'rhid', // referrring hit id ]; if (paramsToDelete.every((key) => searchParams.get(key) === null)) { return; } paramsToDelete.forEach((key) => { searchParams.delete(key); }); var cleanUrl = new URL(window.location.href); cleanUrl.search = searchParams.toString(); history.replaceState({}, document.title, cleanUrl); })(); </script> <script async src="https://www.googletagmanager.com/gtag/js?id=G-5VKX33P2DS"></script> <script> window.dataLayer = window.dataLayer || []; function gtag(){dataLayer.push(arguments);} gtag('js', new Date()); gtag('config', 'G-5VKX33P2DS', { cookie_domain: 'academia.edu', send_page_view: false, }); gtag('event', 'page_view', { 'controller': "profiles/works", 'action': "summary", 'controller_action': 'profiles/works#summary', 'logged_in': 'false', 'edge': 'unknown', // Send nil if there is no A/B test bucket, in case some records get logged // with missing data - that way we can distinguish between the two cases. // ab_test_bucket should be of the form <ab_test_name>:<bucket> 'ab_test_bucket': null, }) </script> <script type="text/javascript"> window.sendUserTiming = function(timingName) { if (!(window.performance && window.performance.measure)) return; var entries = window.performance.getEntriesByName(timingName, "measure"); if (entries.length !== 1) return; var timingValue = Math.round(entries[0].duration); gtag('event', 'timing_complete', { name: timingName, value: timingValue, event_category: 'User-centric', }); }; window.sendUserTiming("Time To First Byte"); </script> <meta name="csrf-param" content="authenticity_token" /> <meta name="csrf-token" content="6lQUge0m05_Bws6zvWJDQJ5Nc3mAJwPVA2xukBquCfhFZyp9ZEA0sPt94QxRktSCK8XjySfalg2EjutHcyQIgw" /> <link rel="stylesheet" media="all" href="//a.academia-assets.com/assets/wow-3d36c19b4875b226bfed0fcba1dcea3f2fe61148383d97c0465c016b8c969290.css" /><link rel="stylesheet" media="all" href="//a.academia-assets.com/assets/social/home-79e78ce59bef0a338eb6540ec3d93b4a7952115b56c57f1760943128f4544d42.css" /><link rel="stylesheet" media="all" href="//a.academia-assets.com/assets/design_system/heading-95367dc03b794f6737f30123738a886cf53b7a65cdef98a922a98591d60063e3.css" /><link rel="stylesheet" media="all" href="//a.academia-assets.com/assets/design_system/button-bfbac2a470372e2f3a6661a65fa7ff0a0fbf7aa32534d9a831d683d2a6f9e01b.css" /><link rel="stylesheet" media="all" href="//a.academia-assets.com/assets/design_system/body-170d1319f0e354621e81ca17054bb147da2856ec0702fe440a99af314a6338c5.css" /><link crossorigin="" href="https://fonts.gstatic.com/" rel="preconnect" /><link href="https://fonts.googleapis.com/css2?family=DM+Sans:ital,opsz,wght@0,9..40,100..1000;1,9..40,100..1000&family=Gupter:wght@400;500;700&family=IBM+Plex+Mono:wght@300;400&family=Material+Symbols+Outlined:opsz,wght,FILL,GRAD@20,400,0,0&display=swap" rel="stylesheet" /><link rel="stylesheet" media="all" href="//a.academia-assets.com/assets/design_system/common-2b6f90dbd75f5941bc38f4ad716615f3ac449e7398313bb3bc225fba451cd9fa.css" /> <meta name="author" content="micah richert" /> <meta name="description" content="Micah Richert: 12 Followers, 4 Following, 18 Research papers. Research interests: Motion Analysis, GPU Computing, and Retina (Ophthalmology)." /> <meta name="google-site-verification" content="bKJMBZA7E43xhDOopFZkssMMkBRjvYERV-NaN4R6mrs" /> <script> var $controller_name = 'works'; var $action_name = "summary"; var $rails_env = 'production'; var $app_rev = '4fa2af9c4f363dba70f41e510d9d86aae76f6ed9'; var $domain = 'academia.edu'; var $app_host = "academia.edu"; var $asset_host = "academia-assets.com"; var $start_time = new Date().getTime(); var $recaptcha_key = "6LdxlRMTAAAAADnu_zyLhLg0YF9uACwz78shpjJB"; var $recaptcha_invisible_key = "6Lf3KHUUAAAAACggoMpmGJdQDtiyrjVlvGJ6BbAj"; var $disableClientRecordHit = false; </script> <script> window.Aedu = { hit_data: null }; window.Aedu.SiteStats = {"premium_universities_count":14023,"monthly_visitors":"102 million","monthly_visitor_count":102584291,"monthly_visitor_count_in_millions":102,"user_count":283374718,"paper_count":55203019,"paper_count_in_millions":55,"page_count":432000000,"page_count_in_millions":432,"pdf_count":16500000,"pdf_count_in_millions":16}; window.Aedu.serverRenderTime = new Date(1740155489000); window.Aedu.timeDifference = new Date().getTime() - 1740155489000; window.Aedu.isUsingCssV1 = false; window.Aedu.enableLocalization = true; window.Aedu.activateFullstory = false; window.Aedu.serviceAvailability = { status: {"attention_db":"on","bibliography_db":"on","contacts_db":"on","email_db":"on","indexability_db":"on","mentions_db":"on","news_db":"on","notifications_db":"on","offsite_mentions_db":"on","redshift":"on","redshift_exports_db":"on","related_works_db":"on","ring_db":"on","user_tests_db":"on"}, serviceEnabled: function(service) { return this.status[service] === "on"; }, readEnabled: function(service) { return this.serviceEnabled(service) || this.status[service] === "read_only"; }, }; window.Aedu.viewApmTrace = function() { // Check if x-apm-trace-id meta tag is set, and open the trace in APM // in a new window if it is. var apmTraceId = document.head.querySelector('meta[name="x-apm-trace-id"]'); if (apmTraceId) { var traceId = apmTraceId.content; // Use trace ID to construct URL, an example URL looks like: // https://app.datadoghq.com/apm/traces?query=trace_id%31298410148923562634 var apmUrl = 'https://app.datadoghq.com/apm/traces?query=trace_id%3A' + traceId; window.open(apmUrl, '_blank'); } }; </script>  <link href="https://fonts.googleapis.com/css?family=Roboto:100,100i,300,300i,400,400i,500,500i,700,700i,900,900i" rel="stylesheet"> <link rel="preload" href="//maxcdn.bootstrapcdn.com/font-awesome/4.3.0/css/font-awesome.min.css" as="style" onload="this.rel='stylesheet'"> <link rel="stylesheet" media="all" href="//a.academia-assets.com/assets/libraries-a9675dcb01ec4ef6aa807ba772c7a5a00c1820d3ff661c1038a20f80d06bb4e4.css" /> <link rel="stylesheet" media="all" href="//a.academia-assets.com/assets/academia-1eb081e01ca8bc0c1b1d866df79d9eb4dd2c484e4beecf76e79a7806c72fee08.css" /> <link rel="stylesheet" media="all" href="//a.academia-assets.com/assets/design_system_legacy-056a9113b9a0f5343d013b29ee1929d5a18be35fdcdceb616600b4db8bd20054.css" /> <script src="//a.academia-assets.com/assets/webpack_bundles/runtime-bundle-005434038af4252ca37c527588411a3d6a0eabb5f727fac83f8bbe7fd88d93bb.js"></script> <script src="//a.academia-assets.com/assets/webpack_bundles/webpack_libraries_and_infrequently_changed.wjs-bundle-b42d48e01e9cb3bf5ae55ea154ab7133fe47240c1d7c59b0249065c67d6f65ab.js"></script> <script src="//a.academia-assets.com/assets/webpack_bundles/core_webpack.wjs-bundle-f3fdf92d2e211a3fd7a717a75adb7bf4af30f5d7651964d9f223129f218112cd.js"></script> <script src="//a.academia-assets.com/assets/webpack_bundles/sentry.wjs-bundle-5fe03fddca915c8ba0f7edbe64c194308e8ce5abaed7bffe1255ff37549c4808.js"></script> <script> jade = window.jade || {}; jade.helpers = window.$h; jade._ = window._; </script>  <script id="tag-manager-head-root">(function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start': new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0], j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src= 'https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f); })(window,document,'script','dataLayer_old','GTM-5G9JF7Z');</script>  <script> window.gptadslots = []; window.googletag = window.googletag || {}; window.googletag.cmd = window.googletag.cmd || []; </script> <script type="text/javascript"> // TODO(jacob): This should be defined, may be rare load order problem. // Checking if null is just a quick fix, will default to en if unset. // Better fix is to run this immedietely after I18n is set. if (window.I18n != null) { I18n.defaultLocale = "en"; I18n.locale = "en"; I18n.fallbacks = true; } </script> <link rel="canonical" href="https://independent.academia.edu/MicahRichert" /> </head>   <body class='c-profiles/works a-summary logged_out'>  <div id="fb-root"></div><script>window.fbAsyncInit = function() { FB.init({ appId: "2369844204", version: "v8.0", status: true, cookie: true, xfbml: true }); // Additional initialization code. if (window.InitFacebook) { // facebook.ts already loaded, set it up. window.InitFacebook(); } else { // Set a flag for facebook.ts to find when it loads. window.academiaAuthReadyFacebook = true; } };</script><script>window.fbAsyncLoad = function() { // Protection against double calling of this function if (window.FB) { return; } (function(d, s, id){ var js, fjs = d.getElementsByTagName(s)[0]; if (d.getElementById(id)) {return;} js = d.createElement(s); js.id = id; js.src = "//connect.facebook.net/en_US/sdk.js"; fjs.parentNode.insertBefore(js, fjs); }(document, 'script', 'facebook-jssdk')); } if (!window.defer_facebook) { // Autoload if not deferred window.fbAsyncLoad(); } else { // Defer loading by 5 seconds setTimeout(function() { window.fbAsyncLoad(); }, 5000); }</script> <div id="google-root"></div><script>window.loadGoogle = function() { if (window.InitGoogle) { // google.ts already loaded, set it up. window.InitGoogle("331998490334-rsn3chp12mbkiqhl6e7lu2q0mlbu0f1b"); } else { // Set a flag for google.ts to use when it loads. window.GoogleClientID = "331998490334-rsn3chp12mbkiqhl6e7lu2q0mlbu0f1b"; } };</script><script>window.googleAsyncLoad = function() { // Protection against double calling of this function (function(d) { var js; var id = 'google-jssdk'; var ref = d.getElementsByTagName('script')[0]; if (d.getElementById(id)) { return; } js = d.createElement('script'); js.id = id; js.async = true; js.onload = loadGoogle; js.src = "https://accounts.google.com/gsi/client" ref.parentNode.insertBefore(js, ref); }(document)); } if (!window.defer_google) { // Autoload if not deferred window.googleAsyncLoad(); } else { // Defer loading by 5 seconds setTimeout(function() { window.googleAsyncLoad(); }, 5000); }</script> <div id="tag-manager-body-root">  <noscript><iframe src="https://www.googletagmanager.com/ns.html?id=GTM-5G9JF7Z" height="0" width="0" style="display:none;visibility:hidden"></iframe></noscript>   <script> window.addEventListener('load', function() { if (document.querySelector('input[name="commit"]')) { document.querySelector('input[name="commit"]').addEventListener('click', function() { gtag('event', 'click', { event_category: 'button', event_label: 'Log In' }) }) } }); </script> </div> <script>var _comscore = _comscore || []; _comscore.push({ c1: "2", c2: "26766707" }); (function() { var s = document.createElement("script"), el = document.getElementsByTagName("script")[0]; s.async = true; s.src = (document.location.protocol == "https:" ? "https://sb" : "http://b") + ".scorecardresearch.com/beacon.js"; el.parentNode.insertBefore(s, el); })();</script><img src="https://sb.scorecardresearch.com/p?c1=2&c2=26766707&cv=2.0&cj=1" style="position: absolute; visibility: hidden" /> <div id='react-modal'></div> <div class='DesignSystem'> <a class='u-showOnFocus' href='#site'> Skip to main content </a> </div> <div id="upgrade_ie_banner" style="display: none;"><p>Academia.edu no longer supports Internet Explorer.</p><p>To browse Academia.edu and the wider internet faster and more securely, please take a few seconds to <a href="https://www.academia.edu/upgrade-browser">upgrade your browser</a>.</p></div><script>// Show this banner for all versions of IE if (!!window.MSInputMethodContext || /(MSIE)/.test(navigator.userAgent)) { document.getElementById('upgrade_ie_banner').style.display = 'block'; }</script> <div class="DesignSystem bootstrap ShrinkableNav"><div class="navbar navbar-default main-header"><div class="container-wrapper" id="main-header-container"><div class="container"><div class="navbar-header"><div class="nav-left-wrapper u-mt0x"><div class="nav-logo"><a data-main-header-link-target="logo_home" href="https://www.academia.edu/"><img class="visible-xs-inline-block" style="height: 24px;" alt="Academia.edu" src="//a.academia-assets.com/images/academia-logo-redesign-2015-A.svg" width="24" height="24" /><img width="145.2" height="18" class="hidden-xs" style="height: 24px;" alt="Academia.edu" src="//a.academia-assets.com/images/academia-logo-redesign-2015.svg" /></a></div><div class="nav-search"><div class="SiteSearch-wrapper select2-no-default-pills"><form class="js-SiteSearch-form DesignSystem" action="https://www.academia.edu/search" accept-charset="UTF-8" method="get"><i class="SiteSearch-icon fa fa-search u-fw700 u-positionAbsolute u-tcGrayDark"></i><input class="js-SiteSearch-form-input SiteSearch-form-input form-control" data-main-header-click-target="search_input" name="q" placeholder="Search" type="text" value="" /></form></div></div></div><div class="nav-right-wrapper pull-right"><ul class="NavLinks js-main-nav list-unstyled"><li class="NavLinks-link"><a class="js-header-login-url Button Button--inverseGray Button--sm u-mb4x" id="nav_log_in" rel="nofollow" href="https://www.academia.edu/login">Log In</a></li><li class="NavLinks-link u-p0x"><a class="Button Button--inverseGray Button--sm u-mb4x" rel="nofollow" href="https://www.academia.edu/signup">Sign Up</a></li></ul><button class="hidden-lg hidden-md hidden-sm u-ml4x navbar-toggle collapsed" data-target=".js-mobile-header-links" data-toggle="collapse" type="button"><span class="icon-bar"></span><span class="icon-bar"></span><span class="icon-bar"></span></button></div></div><div class="collapse navbar-collapse js-mobile-header-links"><ul class="nav navbar-nav"><li class="u-borderColorGrayLight u-borderBottom1"><a rel="nofollow" href="https://www.academia.edu/login">Log In</a></li><li class="u-borderColorGrayLight u-borderBottom1"><a rel="nofollow" href="https://www.academia.edu/signup">Sign Up</a></li><li class="u-borderColorGrayLight u-borderBottom1 js-mobile-nav-expand-trigger"><a href="#">more&nbsp<span class="caret"></span></a></li><li><ul class="js-mobile-nav-expand-section nav navbar-nav u-m0x collapse"><li class="u-borderColorGrayLight u-borderBottom1"><a rel="false" href="https://www.academia.edu/about">About</a></li><li class="u-borderColorGrayLight u-borderBottom1"><a rel="nofollow" href="https://www.academia.edu/press">Press</a></li><li class="u-borderColorGrayLight u-borderBottom1"><a rel="false" href="https://www.academia.edu/documents">Papers</a></li><li class="u-borderColorGrayLight u-borderBottom1"><a rel="nofollow" href="https://www.academia.edu/terms">Terms</a></li><li class="u-borderColorGrayLight u-borderBottom1"><a rel="nofollow" href="https://www.academia.edu/privacy">Privacy</a></li><li class="u-borderColorGrayLight u-borderBottom1"><a rel="nofollow" href="https://www.academia.edu/copyright">Copyright</a></li><li class="u-borderColorGrayLight u-borderBottom1"><a rel="nofollow" href="https://www.academia.edu/hiring"><i class="fa fa-briefcase"></i> We're Hiring!</a></li><li class="u-borderColorGrayLight u-borderBottom1"><a rel="nofollow" href="https://support.academia.edu/hc/en-us"><i class="fa fa-question-circle"></i> Help Center</a></li><li class="js-mobile-nav-collapse-trigger u-borderColorGrayLight u-borderBottom1 dropup" style="display:none"><a href="#">less&nbsp<span class="caret"></span></a></li></ul></li></ul></div></div></div><script>(function(){ var $moreLink = $(".js-mobile-nav-expand-trigger"); var $lessLink = $(".js-mobile-nav-collapse-trigger"); var $section = $('.js-mobile-nav-expand-section'); $moreLink.click(function(ev){ ev.preventDefault(); $moreLink.hide(); $lessLink.show(); $section.collapse('show'); }); $lessLink.click(function(ev){ ev.preventDefault(); $moreLink.show(); $lessLink.hide(); $section.collapse('hide'); }); })() if ($a.is_logged_in() || false) { new Aedu.NavigationController({ el: '.js-main-nav', showHighlightedNotification: false }); } else { $(".js-header-login-url").attr("href", $a.loginUrlWithRedirect()); } Aedu.autocompleteSearch = new AutocompleteSearch({el: '.js-SiteSearch-form'});</script></div></div> <div id='site' class='fixed'> <div id="content" class="clearfix"> <script>document.addEventListener('DOMContentLoaded', function(){ var $dismissible = $(".dismissible_banner"); $dismissible.click(function(ev) { $dismissible.hide(); }); });</script> <script src="//a.academia-assets.com/assets/webpack_bundles/profile.wjs-bundle-fd736d66704990dee9f52cdbd5e7b1c7d1b031c0bb5a20dd676d1a6b7d5fea79.js" defer="defer"></script><script>$viewedUser = Aedu.User.set_viewed( {"id":57694469,"first_name":"Micah","middle_initials":null,"last_name":"Richert","page_name":"MicahRichert","domain_name":"independent","created_at":"2016-12-05T19:18:13.684-08:00","display_name":"Micah Richert","url":"https://independent.academia.edu/MicahRichert","photo":"/images/s65_no_pic.png","has_photo":false,"is_analytics_public":false,"interests":[{"id":13766,"name":"Motion Analysis","url":"https://www.academia.edu/Documents/in/Motion_Analysis"},{"id":12225,"name":"GPU Computing","url":"https://www.academia.edu/Documents/in/GPU_Computing"},{"id":29804,"name":"Retina (Ophthalmology)","url":"https://www.academia.edu/Documents/in/Retina_Ophthalmology_"},{"id":2950,"name":"Computational Modeling","url":"https://www.academia.edu/Documents/in/Computational_Modeling"},{"id":317562,"name":"Spiking Neural Networks","url":"https://www.academia.edu/Documents/in/Spiking_Neural_Networks"}]} ); if ($a.is_logged_in() && $viewedUser.is_current_user()) { $('body').addClass('profile-viewed-by-owner'); } $socialProfiles = []</script><div id="js-react-on-rails-context" style="display:none" data-rails-context="{"inMailer":false,"i18nLocale":"en","i18nDefaultLocale":"en","href":"https://independent.academia.edu/MicahRichert","location":"/MicahRichert","scheme":"https","host":"independent.academia.edu","port":null,"pathname":"/MicahRichert","search":null,"httpAcceptLanguage":null,"serverSide":false}"></div> <div class="js-react-on-rails-component" style="display:none" data-component-name="ProfileCheckPaperUpdate" data-props="{}" data-trace="false" data-dom-id="ProfileCheckPaperUpdate-react-component-0d4010eb-7787-493c-b9d3-daa2440f55fe"></div> <div id="ProfileCheckPaperUpdate-react-component-0d4010eb-7787-493c-b9d3-daa2440f55fe"></div> <div class="DesignSystem"><div class="onsite-ping" id="onsite-ping"></div></div><div class="profile-user-info DesignSystem"><div class="social-profile-container"><div class="left-panel-container"><div class="user-info-component-wrapper"><div class="user-summary-cta-container"><div class="user-summary-container"><div class="social-profile-avatar-container"><img class="profile-avatar u-positionAbsolute" border="0" alt="" src="//a.academia-assets.com/images/s200_no_pic.png" /></div><div class="title-container"><h1 class="ds2-5-heading-sans-serif-sm">Micah Richert</h1><div class="affiliations-container fake-truncate js-profile-affiliations"></div></div></div><div class="sidebar-cta-container"><button class="ds2-5-button hidden profile-cta-button grow js-profile-follow-button" data-broccoli-component="user-info.follow-button" data-click-track="profile-user-info-follow-button" data-follow-user-fname="Micah" data-follow-user-id="57694469" data-follow-user-source="profile_button" data-has-google="false"><span class="material-symbols-outlined" style="font-size: 20px" translate="no">add</span>Follow</button><button class="ds2-5-button hidden profile-cta-button grow js-profile-unfollow-button" data-broccoli-component="user-info.unfollow-button" data-click-track="profile-user-info-unfollow-button" data-unfollow-user-id="57694469"><span class="material-symbols-outlined" style="font-size: 20px" translate="no">done</span>Following</button></div></div><div class="user-stats-container"><a><div class="stat-container js-profile-followers"><p class="label">Followers</p><p class="data">12</p></div></a><a><div class="stat-container js-profile-followees" data-broccoli-component="user-info.followees-count" data-click-track="profile-expand-user-info-following"><p class="label">Following</p><p class="data">4</p></div></a><a><div class="stat-container js-profile-coauthors" data-broccoli-component="user-info.coauthors-count" data-click-track="profile-expand-user-info-coauthors"><p class="label">Co-authors</p><p class="data">4</p></div></a><div class="js-mentions-count-container" style="display: none;"><a href="/MicahRichert/mentions"><div class="stat-container"><p class="label">Mentions</p><p class="data"></p></div></a></div><span><div class="stat-container"><p class="label"><span class="js-profile-total-view-text">Public Views</span></p><p class="data"><span class="js-profile-view-count"></span></p></div></span></div><div class="suggested-academics-container"><div class="suggested-academics--header"><p class="ds2-5-body-md-bold">Related Authors</p></div><ul class="suggested-user-card-list"><div class="suggested-user-card"><div class="suggested-user-card__avatar social-profile-avatar-container"><a href="https://cornell.academia.edu/MortenHChristiansen"><img class="profile-avatar u-positionAbsolute" alt="Morten H. Christiansen" border="0" onerror="if (this.src != '//a.academia-assets.com/images/s200_no_pic.png') this.src = '//a.academia-assets.com/images/s200_no_pic.png';" width="200" height="200" src="https://0.academia-photos.com/12058/4028/18892583/s200_morten_h..christiansen.jpg" /></a></div><div class="suggested-user-card__user-info"><a class="suggested-user-card__user-info__header ds2-5-body-sm-bold ds2-5-body-link" href="https://cornell.academia.edu/MortenHChristiansen">Morten H. Christiansen</a><p class="suggested-user-card__user-info__subheader ds2-5-body-xs">Cornell University</p></div></div><div class="suggested-user-card"><div class="suggested-user-card__avatar social-profile-avatar-container"><a href="https://ucmerced.academia.edu/MichaelSpivey"><img class="profile-avatar u-positionAbsolute" alt="Michael Spivey" border="0" onerror="if (this.src != '//a.academia-assets.com/images/s200_no_pic.png') this.src = '//a.academia-assets.com/images/s200_no_pic.png';" width="200" height="200" src="https://0.academia-photos.com/20720/6987/32622142/s200_michael.spivey.jpg" /></a></div><div class="suggested-user-card__user-info"><a class="suggested-user-card__user-info__header ds2-5-body-sm-bold ds2-5-body-link" href="https://ucmerced.academia.edu/MichaelSpivey">Michael Spivey</a><p class="suggested-user-card__user-info__subheader ds2-5-body-xs">University of California, Merced</p></div></div><div class="suggested-user-card"><div class="suggested-user-card__avatar social-profile-avatar-container"><a href="https://northwestern.academia.edu/NoshirContractor"><img class="profile-avatar u-positionAbsolute" alt="Noshir Contractor" border="0" onerror="if (this.src != '//a.academia-assets.com/images/s200_no_pic.png') this.src = '//a.academia-assets.com/images/s200_no_pic.png';" width="200" height="200" src="https://0.academia-photos.com/36848/12227/11474/s200_noshir.contractor.jpg" /></a></div><div class="suggested-user-card__user-info"><a class="suggested-user-card__user-info__header ds2-5-body-sm-bold ds2-5-body-link" href="https://northwestern.academia.edu/NoshirContractor">Noshir Contractor</a><p class="suggested-user-card__user-info__subheader ds2-5-body-xs">Northwestern University</p></div></div><div class="suggested-user-card"><div class="suggested-user-card__avatar social-profile-avatar-container"><a href="https://ucg.academia.edu/Fl%C3%A1vioParanhos"><img class="profile-avatar u-positionAbsolute" alt="Fl谩vio Paranhos" border="0" onerror="if (this.src != '//a.academia-assets.com/images/s200_no_pic.png') this.src = '//a.academia-assets.com/images/s200_no_pic.png';" width="200" height="200" src="https://0.academia-photos.com/116596/31328/56029/s200_fl_vio.paranhos.116596" /></a></div><div class="suggested-user-card__user-info"><a class="suggested-user-card__user-info__header ds2-5-body-sm-bold ds2-5-body-link" href="https://ucg.academia.edu/Fl%C3%A1vioParanhos">Fl谩vio Paranhos</a><p class="suggested-user-card__user-info__subheader ds2-5-body-xs">PUC Goi谩s</p></div></div><div class="suggested-user-card"><div class="suggested-user-card__avatar social-profile-avatar-container"><a href="https://manchester.academia.edu/RupertFord"><img class="profile-avatar u-positionAbsolute" border="0" alt="" src="//a.academia-assets.com/images/s200_no_pic.png" /></a></div><div class="suggested-user-card__user-info"><a class="suggested-user-card__user-info__header ds2-5-body-sm-bold ds2-5-body-link" href="https://manchester.academia.edu/RupertFord">Rupert Ford</a><p class="suggested-user-card__user-info__subheader ds2-5-body-xs">The University of Manchester</p></div></div><div class="suggested-user-card"><div class="suggested-user-card__avatar social-profile-avatar-container"><a href="https://uppsala.academia.edu/ZeeshanKhaliq"><img class="profile-avatar u-positionAbsolute" alt="Zeeshan Khaliq" border="0" onerror="if (this.src != '//a.academia-assets.com/images/s200_no_pic.png') this.src = '//a.academia-assets.com/images/s200_no_pic.png';" width="200" height="200" src="https://0.academia-photos.com/484559/164357/191535/s200_zeeshan.khaliq.jpg" /></a></div><div class="suggested-user-card__user-info"><a class="suggested-user-card__user-info__header ds2-5-body-sm-bold ds2-5-body-link" href="https://uppsala.academia.edu/ZeeshanKhaliq">Zeeshan Khaliq</a><p class="suggested-user-card__user-info__subheader ds2-5-body-xs">Uppsala University</p></div></div><div class="suggested-user-card"><div class="suggested-user-card__avatar social-profile-avatar-container"><a href="https://gdufs.academia.edu/PhoenixWattanachote"><img class="profile-avatar u-positionAbsolute" alt="Phoenix Wattanachote" border="0" onerror="if (this.src != '//a.academia-assets.com/images/s200_no_pic.png') this.src = '//a.academia-assets.com/images/s200_no_pic.png';" width="200" height="200" src="https://0.academia-photos.com/805591/276671/18391068/s200_phoenix.wattanachote.jpg" /></a></div><div class="suggested-user-card__user-info"><a class="suggested-user-card__user-info__header ds2-5-body-sm-bold ds2-5-body-link" href="https://gdufs.academia.edu/PhoenixWattanachote">Phoenix Wattanachote</a><p class="suggested-user-card__user-info__subheader ds2-5-body-xs">Guangdong University of Foreign Studies</p></div></div><div class="suggested-user-card"><div class="suggested-user-card__avatar social-profile-avatar-container"><a href="https://unime.academia.edu/MaxTalanov"><img class="profile-avatar u-positionAbsolute" alt="Max Talanov" border="0" onerror="if (this.src != '//a.academia-assets.com/images/s200_no_pic.png') this.src = '//a.academia-assets.com/images/s200_no_pic.png';" width="200" height="200" src="https://0.academia-photos.com/6267643/2932530/3432322/s200_max.talanov.png" /></a></div><div class="suggested-user-card__user-info"><a class="suggested-user-card__user-info__header ds2-5-body-sm-bold ds2-5-body-link" href="https://unime.academia.edu/MaxTalanov">Max Talanov</a><p class="suggested-user-card__user-info__subheader ds2-5-body-xs">University of Messina</p></div></div><div class="suggested-user-card"><div class="suggested-user-card__avatar social-profile-avatar-container"><a href="https://uoguelph.academia.edu/AndrewPeregrine"><img class="profile-avatar u-positionAbsolute" alt="Andrew Peregrine" border="0" onerror="if (this.src != '//a.academia-assets.com/images/s200_no_pic.png') this.src = '//a.academia-assets.com/images/s200_no_pic.png';" width="200" height="200" src="https://0.academia-photos.com/33334601/9866059/10997586/s200_andrew.peregrine.jpg" /></a></div><div class="suggested-user-card__user-info"><a class="suggested-user-card__user-info__header ds2-5-body-sm-bold ds2-5-body-link" href="https://uoguelph.academia.edu/AndrewPeregrine">Andrew Peregrine</a><p class="suggested-user-card__user-info__subheader ds2-5-body-xs">University of Guelph</p></div></div><div class="suggested-user-card"><div class="suggested-user-card__avatar social-profile-avatar-container"><a href="https://unimelb.academia.edu/ShantonChang"><img class="profile-avatar u-positionAbsolute" border="0" alt="" src="//a.academia-assets.com/images/s200_no_pic.png" /></a></div><div class="suggested-user-card__user-info"><a class="suggested-user-card__user-info__header ds2-5-body-sm-bold ds2-5-body-link" href="https://unimelb.academia.edu/ShantonChang">Shanton Chang</a><p class="suggested-user-card__user-info__subheader ds2-5-body-xs">University of Melbourne</p></div></div></ul></div><div class="ri-section"><div class="ri-section-header"><span>Interests</span></div><div class="ri-tags-container"><a data-click-track="profile-user-info-expand-research-interests" data-has-card-for-ri-list="57694469" href="https://www.academia.edu/Documents/in/Motion_Analysis"><div id="js-react-on-rails-context" style="display:none" data-rails-context="{"inMailer":false,"i18nLocale":"en","i18nDefaultLocale":"en","href":"https://independent.academia.edu/MicahRichert","location":"/MicahRichert","scheme":"https","host":"independent.academia.edu","port":null,"pathname":"/MicahRichert","search":null,"httpAcceptLanguage":null,"serverSide":false}"></div> <div class="js-react-on-rails-component" style="display:none" data-component-name="Pill" data-props="{"color":"gray","children":["Motion Analysis"]}" data-trace="false" data-dom-id="Pill-react-component-b3252693-ddb2-47d8-8b54-d1d4ec3e2af1"></div> <div id="Pill-react-component-b3252693-ddb2-47d8-8b54-d1d4ec3e2af1"></div> </a><a data-click-track="profile-user-info-expand-research-interests" data-has-card-for-ri-list="57694469" href="https://www.academia.edu/Documents/in/GPU_Computing"><div class="js-react-on-rails-component" style="display:none" data-component-name="Pill" data-props="{"color":"gray","children":["GPU Computing"]}" data-trace="false" data-dom-id="Pill-react-component-cf5786bd-aa19-476c-8b75-8cc8784b035c"></div> <div id="Pill-react-component-cf5786bd-aa19-476c-8b75-8cc8784b035c"></div> </a><a data-click-track="profile-user-info-expand-research-interests" data-has-card-for-ri-list="57694469" href="https://www.academia.edu/Documents/in/Retina_Ophthalmology_"><div class="js-react-on-rails-component" style="display:none" data-component-name="Pill" data-props="{"color":"gray","children":["Retina (Ophthalmology)"]}" data-trace="false" data-dom-id="Pill-react-component-79d83665-3c08-4ce8-babb-bd18a98f7920"></div> <div id="Pill-react-component-79d83665-3c08-4ce8-babb-bd18a98f7920"></div> </a><a data-click-track="profile-user-info-expand-research-interests" data-has-card-for-ri-list="57694469" href="https://www.academia.edu/Documents/in/Computational_Modeling"><div class="js-react-on-rails-component" style="display:none" data-component-name="Pill" data-props="{"color":"gray","children":["Computational Modeling"]}" data-trace="false" data-dom-id="Pill-react-component-1d9c9d1a-9175-4c58-8939-d52ae4f87f76"></div> <div id="Pill-react-component-1d9c9d1a-9175-4c58-8939-d52ae4f87f76"></div> </a><a data-click-track="profile-user-info-expand-research-interests" data-has-card-for-ri-list="57694469" href="https://www.academia.edu/Documents/in/Spiking_Neural_Networks"><div class="js-react-on-rails-component" style="display:none" data-component-name="Pill" data-props="{"color":"gray","children":["Spiking Neural Networks"]}" data-trace="false" data-dom-id="Pill-react-component-c6d6828c-17a4-4e1f-b1fc-b9dd8922c443"></div> <div id="Pill-react-component-c6d6828c-17a4-4e1f-b1fc-b9dd8922c443"></div> </a></div></div></div></div><div class="right-panel-container"><div class="user-content-wrapper"><div class="uploads-container" id="social-redesign-work-container"><div class="upload-header"><h2 class="ds2-5-heading-sans-serif-xs">Uploads</h2></div><div class="documents-container backbone-social-profile-documents" style="width: 100%;"><div class="u-taCenter"></div><div class="profile--tab_content_container js-tab-pane tab-pane active" id="all"><div class="profile--tab_heading_container js-section-heading" data-section="Papers" id="Papers"><h3 class="profile--tab_heading_container">Papers by Micah Richert</h3></div><div class="js-work-strip profile--work_container" data-work-id="30267602"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/30267602/Rate_Stabilization_Through_Plasticity_in_Spiking_Neuron_Network"><img alt="Research paper thumbnail of Rate Stabilization Through Plasticity in Spiking Neuron Network" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" rel="nofollow" href="https://www.academia.edu/30267602/Rate_Stabilization_Through_Plasticity_in_Spiking_Neuron_Network">Rate Stabilization Through Plasticity in Spiking Neuron Network</a></div><div class="wp-workCard_item wp-workCard--coauthors"><span>by </span><span><a class="" data-click-track="profile-work-strip-authors" href="https://independent.academia.edu/MicahRichert">Micah Richert</a> and <a class="" data-click-track="profile-work-strip-authors" href="https://independent.academia.edu/DimitryFisher">Dimitry Fisher</a></span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="30267602"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="30267602"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 30267602; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=30267602]").text(description); $(".js-view-count[data-work-id=30267602]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 30267602; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='30267602']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=30267602]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":30267602,"title":"Rate Stabilization Through Plasticity in Spiking Neuron Network","internal_url":"https://www.academia.edu/30267602/Rate_Stabilization_Through_Plasticity_in_Spiking_Neuron_Network","owner_id":57694469,"coauthors_can_edit":true,"owner":{"id":57694469,"first_name":"Micah","middle_initials":null,"last_name":"Richert","page_name":"MicahRichert","domain_name":"independent","created_at":"2016-12-05T19:18:13.684-08:00","display_name":"Micah Richert","url":"https://independent.academia.edu/MicahRichert"},"attachments":[]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="30267601"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/30267601/Spiking_Network_Apparatus_and_Method_with_Bimodal_Spike_Timing_Dependent_Plasticity"><img alt="Research paper thumbnail of Spiking Network Apparatus and Method with Bimodal Spike-Timing Dependent Plasticity" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" rel="nofollow" href="https://www.academia.edu/30267601/Spiking_Network_Apparatus_and_Method_with_Bimodal_Spike_Timing_Dependent_Plasticity">Spiking Network Apparatus and Method with Bimodal Spike-Timing Dependent Plasticity</a></div><div class="wp-workCard_item wp-workCard--coauthors"><span>by </span><span><a class="" data-click-track="profile-work-strip-authors" href="https://independent.academia.edu/MicahRichert">Micah Richert</a> and <a class="" data-click-track="profile-work-strip-authors" href="https://independent.academia.edu/SachSokol">Sach Sokol</a></span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="30267601"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="30267601"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 30267601; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=30267601]").text(description); $(".js-view-count[data-work-id=30267601]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 30267601; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='30267601']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=30267601]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":30267601,"title":"Spiking Network Apparatus and Method with Bimodal Spike-Timing Dependent Plasticity","internal_url":"https://www.academia.edu/30267601/Spiking_Network_Apparatus_and_Method_with_Bimodal_Spike_Timing_Dependent_Plasticity","owner_id":57694469,"coauthors_can_edit":true,"owner":{"id":57694469,"first_name":"Micah","middle_initials":null,"last_name":"Richert","page_name":"MicahRichert","domain_name":"independent","created_at":"2016-12-05T19:18:13.684-08:00","display_name":"Micah Richert","url":"https://independent.academia.edu/MicahRichert"},"attachments":[]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="30267600"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/30267600/Adaptive_Plasticity_Apparatus_and_Methods_for_Spiking_Neuron_Network"><img alt="Research paper thumbnail of Adaptive Plasticity Apparatus and Methods for Spiking Neuron Network" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" rel="nofollow" href="https://www.academia.edu/30267600/Adaptive_Plasticity_Apparatus_and_Methods_for_Spiking_Neuron_Network">Adaptive Plasticity Apparatus and Methods for Spiking Neuron Network</a></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="30267600"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="30267600"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 30267600; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=30267600]").text(description); $(".js-view-count[data-work-id=30267600]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 30267600; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='30267600']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=30267600]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":30267600,"title":"Adaptive Plasticity Apparatus and Methods for Spiking Neuron Network","internal_url":"https://www.academia.edu/30267600/Adaptive_Plasticity_Apparatus_and_Methods_for_Spiking_Neuron_Network","owner_id":57694469,"coauthors_can_edit":true,"owner":{"id":57694469,"first_name":"Micah","middle_initials":null,"last_name":"Richert","page_name":"MicahRichert","domain_name":"independent","created_at":"2016-12-05T19:18:13.684-08:00","display_name":"Micah Richert","url":"https://independent.academia.edu/MicahRichert"},"attachments":[]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="30267599"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/30267599/Apparatus_and_Methods_for_Activity_Based_Plasticity_in_a_Spiking_Neuron_Network"><img alt="Research paper thumbnail of Apparatus and Methods for Activity-Based Plasticity in a Spiking Neuron Network" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" rel="nofollow" href="https://www.academia.edu/30267599/Apparatus_and_Methods_for_Activity_Based_Plasticity_in_a_Spiking_Neuron_Network">Apparatus and Methods for Activity-Based Plasticity in a Spiking Neuron Network</a></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="30267599"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="30267599"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 30267599; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=30267599]").text(description); $(".js-view-count[data-work-id=30267599]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 30267599; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='30267599']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=30267599]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":30267599,"title":"Apparatus and Methods for Activity-Based Plasticity in a Spiking Neuron Network","internal_url":"https://www.academia.edu/30267599/Apparatus_and_Methods_for_Activity_Based_Plasticity_in_a_Spiking_Neuron_Network","owner_id":57694469,"coauthors_can_edit":true,"owner":{"id":57694469,"first_name":"Micah","middle_initials":null,"last_name":"Richert","page_name":"MicahRichert","domain_name":"independent","created_at":"2016-12-05T19:18:13.684-08:00","display_name":"Micah Richert","url":"https://independent.academia.edu/MicahRichert"},"attachments":[]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="30267598"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/30267598/Apparatus_and_Methods_for_Event_Based_Communication_in_a_Spiking_Neuron_Networks"><img alt="Research paper thumbnail of Apparatus and Methods for Event-Based Communication in a Spiking Neuron Networks" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" rel="nofollow" href="https://www.academia.edu/30267598/Apparatus_and_Methods_for_Event_Based_Communication_in_a_Spiking_Neuron_Networks">Apparatus and Methods for Event-Based Communication in a Spiking Neuron Networks</a></div><div class="wp-workCard_item wp-workCard--coauthors"><span>by </span><span><a class="" data-click-track="profile-work-strip-authors" href="https://independent.academia.edu/MicahRichert">Micah Richert</a> and <a class="" data-click-track="profile-work-strip-authors" href="https://braincorporation.academia.edu/OlegSinyavskiy">Oleg Sinyavskiy</a></span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="30267598"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="30267598"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 30267598; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=30267598]").text(description); $(".js-view-count[data-work-id=30267598]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 30267598; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='30267598']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=30267598]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":30267598,"title":"Apparatus and Methods for Event-Based Communication in a Spiking Neuron Networks","internal_url":"https://www.academia.edu/30267598/Apparatus_and_Methods_for_Event_Based_Communication_in_a_Spiking_Neuron_Networks","owner_id":57694469,"coauthors_can_edit":true,"owner":{"id":57694469,"first_name":"Micah","middle_initials":null,"last_name":"Richert","page_name":"MicahRichert","domain_name":"independent","created_at":"2016-12-05T19:18:13.684-08:00","display_name":"Micah Richert","url":"https://independent.academia.edu/MicahRichert"},"attachments":[]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="30267597"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/30267597/Apparatus_and_Methods_for_Rate_Modulated_Plasticity_in_a_Spiking_Neuron_Network"><img alt="Research paper thumbnail of Apparatus and Methods for Rate-Modulated Plasticity in a Spiking Neuron Network" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" rel="nofollow" href="https://www.academia.edu/30267597/Apparatus_and_Methods_for_Rate_Modulated_Plasticity_in_a_Spiking_Neuron_Network">Apparatus and Methods for Rate-Modulated Plasticity in a Spiking Neuron Network</a></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="30267597"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="30267597"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 30267597; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=30267597]").text(description); $(".js-view-count[data-work-id=30267597]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 30267597; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='30267597']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=30267597]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":30267597,"title":"Apparatus and Methods for Rate-Modulated Plasticity in a Spiking Neuron Network","internal_url":"https://www.academia.edu/30267597/Apparatus_and_Methods_for_Rate_Modulated_Plasticity_in_a_Spiking_Neuron_Network","owner_id":57694469,"coauthors_can_edit":true,"owner":{"id":57694469,"first_name":"Micah","middle_initials":null,"last_name":"Richert","page_name":"MicahRichert","domain_name":"independent","created_at":"2016-12-05T19:18:13.684-08:00","display_name":"Micah Richert","url":"https://independent.academia.edu/MicahRichert"},"attachments":[]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="30267596"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/30267596/Beyond_inhibition_lateral_modulation_of_plasticity_of_feedforward_synapses_in_a_spiking_model_of_V1"><img alt="Research paper thumbnail of Beyond inhibition: lateral modulation of plasticity of feedforward synapses in a spiking model of V1" class="work-thumbnail" src="https://attachments.academia-assets.com/50734126/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/30267596/Beyond_inhibition_lateral_modulation_of_plasticity_of_feedforward_synapses_in_a_spiking_model_of_V1">Beyond inhibition: lateral modulation of plasticity of feedforward synapses in a spiking model of V1</a></div><div class="wp-workCard_item"><span>BMC Neuroscience</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="fca7860d674f818f954e2e69666fba70" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":50734126,"asset_id":30267596,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/50734126/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="30267596"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="30267596"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 30267596; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=30267596]").text(description); $(".js-view-count[data-work-id=30267596]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 30267596; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='30267596']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "fca7860d674f818f954e2e69666fba70" } } $('.js-work-strip[data-work-id=30267596]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":30267596,"title":"Beyond inhibition: lateral modulation of plasticity of feedforward synapses in a spiking model of V1","internal_url":"https://www.academia.edu/30267596/Beyond_inhibition_lateral_modulation_of_plasticity_of_feedforward_synapses_in_a_spiking_model_of_V1","owner_id":57694469,"coauthors_can_edit":true,"owner":{"id":57694469,"first_name":"Micah","middle_initials":null,"last_name":"Richert","page_name":"MicahRichert","domain_name":"independent","created_at":"2016-12-05T19:18:13.684-08:00","display_name":"Micah Richert","url":"https://independent.academia.edu/MicahRichert"},"attachments":[{"id":50734126,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/50734126/thumbnails/1.jpg","file_name":"Beyond_inhibition_lateral_modulation_of_20161205-16376-x1dt2i.pdf","download_url":"https://www.academia.edu/attachments/50734126/download_file","bulk_download_file_name":"Beyond_inhibition_lateral_modulation_of.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/50734126/Beyond_inhibition_lateral_modulation_of_20161205-16376-x1dt2i-libre.pdf?1480994608=\u0026response-content-disposition=attachment%3B+filename%3DBeyond_inhibition_lateral_modulation_of.pdf\u0026Expires=1740159089\u0026Signature=SVVNIXIGzH9MnwbF8LJNjHj-DmpdhYBUzMhWGncNzFUSi5437Sr2IkkinM4V8sVqLwKlm85zXpqDhSGsquRzJ4YdiBzVYkJUznXO8XP1tQD78fUy~296cQOIWAQL3ua31eyD04s8h-4HD-yS70XI~y6~p8CvRO8U-Gvfs807dL~PfnlGpkunKoV2aY3YZuklxlenTGvt4sLDB-AHzXnySZqJSadYvVdcTIuDAUT~UaHa6jlwlsfAAqt-rEuAGfFDlW6pyn2Fbh07g9xbRo07q5ZeFUEo2iFukU84jmXdJB4aBFW1jxIJT2hF2UZB~6jXSaHwXTJXTMZHlSdBDQ0K6g__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="30267595"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/30267595/Neuromorphic_modeling_abstractions_and_simulation_of_large_scale_cortical_networks"><img alt="Research paper thumbnail of Neuromorphic modeling abstractions and simulation of large-scale cortical networks" class="work-thumbnail" src="https://attachments.academia-assets.com/50734129/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/30267595/Neuromorphic_modeling_abstractions_and_simulation_of_large_scale_cortical_networks">Neuromorphic modeling abstractions and simulation of large-scale cortical networks</a></div><div class="wp-workCard_item"><span>2011 IEEE/ACM International Conference on Computer-Aided Design (ICCAD)</span><span>, 2011</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Biological neural systems are well known for their robust and power-efficient operation in highly...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Biological neural systems are well known for their robust and power-efficient operation in highly noisy environments. We outline key modeling abstractions for the brain and focus on spiking neural network models. We discuss aspects of neuronal processing and computational issues related to modeling these processes. Although many of these algorithms can be efficiently realized in specialized hardware, we present a case study of simulation of the visual cortex using a GPU based simulation environment that is readily usable by neuroscientists and computer scientists and efficient enough to construct very large networks comparable to brain networks.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="f671eb612caf6c0985a19871ca067271" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":50734129,"asset_id":30267595,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/50734129/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="30267595"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="30267595"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 30267595; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=30267595]").text(description); $(".js-view-count[data-work-id=30267595]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 30267595; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='30267595']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "f671eb612caf6c0985a19871ca067271" } } $('.js-work-strip[data-work-id=30267595]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":30267595,"title":"Neuromorphic modeling abstractions and simulation of large-scale cortical networks","internal_url":"https://www.academia.edu/30267595/Neuromorphic_modeling_abstractions_and_simulation_of_large_scale_cortical_networks","owner_id":57694469,"coauthors_can_edit":true,"owner":{"id":57694469,"first_name":"Micah","middle_initials":null,"last_name":"Richert","page_name":"MicahRichert","domain_name":"independent","created_at":"2016-12-05T19:18:13.684-08:00","display_name":"Micah Richert","url":"https://independent.academia.edu/MicahRichert"},"attachments":[{"id":50734129,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/50734129/thumbnails/1.jpg","file_name":"Neuromorphic_modeling_abstractions_and_s20161205-16376-6bcmov.pdf","download_url":"https://www.academia.edu/attachments/50734129/download_file","bulk_download_file_name":"Neuromorphic_modeling_abstractions_and_s.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/50734129/Neuromorphic_modeling_abstractions_and_s20161205-16376-6bcmov-libre.pdf?1480994614=\u0026response-content-disposition=attachment%3B+filename%3DNeuromorphic_modeling_abstractions_and_s.pdf\u0026Expires=1740159089\u0026Signature=M~DpuCrKf~fufINBuACt8DNRe5-RKTEKiefpK0MswkQ6fEBIpjPXTB-a~QSis5QhHox~0U23bo-k4s4GiyqQ9t5EKUV36VIJn9Zp8STvSRpFxs3IT62ATbIYcaZPK3oVP-YzFHq7RXyAqkR45b55CQiT6NDLVOcSS7xB-GMM~3fp3~rP1F67zqKnqQnHObKC9n2FfqDY-b07jQe8P3iuvW2AIF5xtnaZL9YLv5S4ujKuUbiBAU~MqjeCLOdafnwSJAWy2QIpahYAw0zAdCA5XM2YiKA6Yi~j2ZiLIG~pMfSTR2DRfuiafTjkkQ2FH2XdSvJmrdFLNVRtrN-DVljcEA__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="30267594"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/30267594/A_spiking_model_of_superior_colliculus_for_bottom_up_saliency"><img alt="Research paper thumbnail of A spiking model of superior colliculus for bottom-up saliency" class="work-thumbnail" src="https://attachments.academia-assets.com/50734125/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/30267594/A_spiking_model_of_superior_colliculus_for_bottom_up_saliency">A spiking model of superior colliculus for bottom-up saliency</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Active exploration of visual space is controlled by a saccadic system using a combination of bott...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Active exploration of visual space is controlled by a saccadic system using a combination of bottom-up and topdown signals. Rate based models [1] and simple V1 models (e.g. [2]), have been proposed to explain bottom-up or "pop-out" attentional mechanisms. We present a biologically detailed spiking model of superior colliculus to generate saccadic eye movements using a competitive mechanism based on spike arrival times. Various pop-out tasks based on color, orientation features and motion is used to analyze the reaction time of the model.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="68179d6b7e6b82b1836fb147ee7b05c1" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":50734125,"asset_id":30267594,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/50734125/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="30267594"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="30267594"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 30267594; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=30267594]").text(description); $(".js-view-count[data-work-id=30267594]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 30267594; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='30267594']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "68179d6b7e6b82b1836fb147ee7b05c1" } } $('.js-work-strip[data-work-id=30267594]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":30267594,"title":"A spiking model of superior colliculus for bottom-up saliency","internal_url":"https://www.academia.edu/30267594/A_spiking_model_of_superior_colliculus_for_bottom_up_saliency","owner_id":57694469,"coauthors_can_edit":true,"owner":{"id":57694469,"first_name":"Micah","middle_initials":null,"last_name":"Richert","page_name":"MicahRichert","domain_name":"independent","created_at":"2016-12-05T19:18:13.684-08:00","display_name":"Micah Richert","url":"https://independent.academia.edu/MicahRichert"},"attachments":[{"id":50734125,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/50734125/thumbnails/1.jpg","file_name":"1471-2202-14-S1-P185.pdf","download_url":"https://www.academia.edu/attachments/50734125/download_file","bulk_download_file_name":"A_spiking_model_of_superior_colliculus_f.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/50734125/1471-2202-14-S1-P185-libre.pdf?1480994608=\u0026response-content-disposition=attachment%3B+filename%3DA_spiking_model_of_superior_colliculus_f.pdf\u0026Expires=1740159089\u0026Signature=N0TihQutzy~YSflxlmjk~Hwa4LlIRmmPQ1gkAdPHnGJwhl-QOeHOoUOqv-N0oxY6UtCOuljv21MENavqRn0iK3ilCwa1-KWrADE86RY7OZ0nNrv9QV6cQZ-kvP8GoD7Yh2fq94Ep9Fbq86sbzjso9L4WP2u6336uKU02XjR69BpCciG~8hCuXuqUbQH-MgSe7A7O3fJlWv~t-2IEpah0u9cF7NYk~-Miro0iEOOtdlasPQWAIUX9WR05e4Y3zfF3sXu0HAfHC0WV-LzLle4wEXO-dqAnAqTtBIQu2PnBz81p3TLv2Y-Lns5n1j2YDMOe-7dslLuT8yWQVkGw2PANUg__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="30267593"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/30267593/Towards_reverse_engineering_the_brain_Modeling_abstractions_and_simulation_frameworks"><img alt="Research paper thumbnail of Towards reverse engineering the brain: Modeling abstractions and simulation frameworks" class="work-thumbnail" src="https://attachments.academia-assets.com/50734133/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/30267593/Towards_reverse_engineering_the_brain_Modeling_abstractions_and_simulation_frameworks">Towards reverse engineering the brain: Modeling abstractions and simulation frameworks</a></div><div class="wp-workCard_item"><span>2010 18th IEEE/IFIP International Conference on VLSI and System-on-Chip</span><span>, 2010</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Biological neural systems are well known for their robust and power-efficient operation in highly...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Biological neural systems are well known for their robust and power-efficient operation in highly noisy environments. Biological circuits are made up of low-precision, unreliable and massively parallel neural elements with highly reconfigurable and plastic connections. Two of the most interesting properties of the neural systems are its self-organizing capabilities and its template architecture. Recent research in spiking neural networks has demonstrated interesting principles about learning and neural computation. Understanding and applying these principles to practical problems is only possible if large-scale spiking neural simulators can be constructed. Recent advances in low-cost multiprocessor architectures make it possible to build large-scale spiking network simulators. In this paper we review modeling abstractions for neural circuits and frameworks for modeling, simulating and analyzing spiking neural networks.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="4a1da5bb1ddece77e645e1e2ac301163" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":50734133,"asset_id":30267593,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/50734133/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="30267593"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="30267593"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 30267593; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=30267593]").text(description); $(".js-view-count[data-work-id=30267593]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 30267593; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='30267593']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "4a1da5bb1ddece77e645e1e2ac301163" } } $('.js-work-strip[data-work-id=30267593]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":30267593,"title":"Towards reverse engineering the brain: Modeling abstractions and simulation frameworks","internal_url":"https://www.academia.edu/30267593/Towards_reverse_engineering_the_brain_Modeling_abstractions_and_simulation_frameworks","owner_id":57694469,"coauthors_can_edit":true,"owner":{"id":57694469,"first_name":"Micah","middle_initials":null,"last_name":"Richert","page_name":"MicahRichert","domain_name":"independent","created_at":"2016-12-05T19:18:13.684-08:00","display_name":"Micah Richert","url":"https://independent.academia.edu/MicahRichert"},"attachments":[{"id":50734133,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/50734133/thumbnails/1.jpg","file_name":"Towards_reverse_engineering_the_brain_Mo20161205-16383-1qao2gl.pdf","download_url":"https://www.academia.edu/attachments/50734133/download_file","bulk_download_file_name":"Towards_reverse_engineering_the_brain_Mo.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/50734133/Towards_reverse_engineering_the_brain_Mo20161205-16383-1qao2gl-libre.pdf?1480994614=\u0026response-content-disposition=attachment%3B+filename%3DTowards_reverse_engineering_the_brain_Mo.pdf\u0026Expires=1740159089\u0026Signature=C6U4T2bHZ5Pr8LlCTBKOtaNi6vHy-mEx2uaUlCDpKI1MvhpBCpwKQaWDPc1505bW15baBE67FbjoKx1-vyXAIwJP5oCgwX230o6YbfbnLtIWV4Jjp-828JgB8JFVXTO6DxMd-o6fAQ54pJZV4biCKiWvtUC6u3Qst7RQwfmeqYdHu~d75zsh0NuHavxACJ9SCR5IUB3e9nmAG6nm2F1rB8I380ThEQY0IKw0850~9AQF2DmxRJUoVRfZUldBOJjwtC5cnaJ7bUX2Jh19PbtntSeBoyJqT8vf7MwgE4NQ140g3Ar-~zrUCsR2nmtlx-qD5lwzVE0WnG3kqstWfTVtUQ__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="30267592"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/30267592/Biologically_plausible_models_of_homeostasis_and_STDP_Stability_and_learning_in_spiking_neural_networks"><img alt="Research paper thumbnail of Biologically plausible models of homeostasis and STDP: Stability and learning in spiking neural networks" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" rel="nofollow" href="https://www.academia.edu/30267592/Biologically_plausible_models_of_homeostasis_and_STDP_Stability_and_learning_in_spiking_neural_networks">Biologically plausible models of homeostasis and STDP: Stability and learning in spiking neural networks</a></div><div class="wp-workCard_item"><span>The 2013 International Joint Conference on Neural Networks (IJCNN)</span><span>, 2013</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">ABSTRACT</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="30267592"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="30267592"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 30267592; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=30267592]").text(description); $(".js-view-count[data-work-id=30267592]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 30267592; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='30267592']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=30267592]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":30267592,"title":"Biologically plausible models of homeostasis and STDP: Stability and learning in spiking neural networks","internal_url":"https://www.academia.edu/30267592/Biologically_plausible_models_of_homeostasis_and_STDP_Stability_and_learning_in_spiking_neural_networks","owner_id":57694469,"coauthors_can_edit":true,"owner":{"id":57694469,"first_name":"Micah","middle_initials":null,"last_name":"Richert","page_name":"MicahRichert","domain_name":"independent","created_at":"2016-12-05T19:18:13.684-08:00","display_name":"Micah Richert","url":"https://independent.academia.edu/MicahRichert"},"attachments":[]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="30267591"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/30267591/An_efficient_simulation_environment_for_modeling_large_scale_cortical_processing"><img alt="Research paper thumbnail of An efficient simulation environment for modeling large-scale cortical processing" class="work-thumbnail" src="https://attachments.academia-assets.com/50734120/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/30267591/An_efficient_simulation_environment_for_modeling_large_scale_cortical_processing">An efficient simulation environment for modeling large-scale cortical processing</a></div><div class="wp-workCard_item"><span>Frontiers in neuroinformatics</span><span>, 2011</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">We have developed a spiking neural network simulator, which is both easy to use and computational...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">We have developed a spiking neural network simulator, which is both easy to use and computationally efficient, for the generation of large-scale computational neuroscience models. The simulator implements current or conductance based Izhikevich neuron networks, having spike-timing dependent plasticity and short-term plasticity. It uses a standard network construction interface. The simulator allows for execution on either GPUs or CPUs. The simulator, which is written in C/C++, allows for both fine grain and coarse grain specificity of a host of parameters. We demonstrate the ease of use and computational efficiency of this model by implementing a large-scale model of cortical areas V1, V4, and area MT. The complete model, which has 138,240 neurons and approximately 30 million synapses, runs in real-time on an off-the-shelf GPU. The simulator source code, as well as the source code for the cortical model examples is publicly available.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="75318afb41001bbcfb69fa37b084f079" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":50734120,"asset_id":30267591,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/50734120/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="30267591"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="30267591"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 30267591; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=30267591]").text(description); $(".js-view-count[data-work-id=30267591]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 30267591; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='30267591']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "75318afb41001bbcfb69fa37b084f079" } } $('.js-work-strip[data-work-id=30267591]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":30267591,"title":"An efficient simulation environment for modeling large-scale cortical processing","internal_url":"https://www.academia.edu/30267591/An_efficient_simulation_environment_for_modeling_large_scale_cortical_processing","owner_id":57694469,"coauthors_can_edit":true,"owner":{"id":57694469,"first_name":"Micah","middle_initials":null,"last_name":"Richert","page_name":"MicahRichert","domain_name":"independent","created_at":"2016-12-05T19:18:13.684-08:00","display_name":"Micah Richert","url":"https://independent.academia.edu/MicahRichert"},"attachments":[{"id":50734120,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/50734120/thumbnails/1.jpg","file_name":"An_Efficient_Simulation_Environment_for_20161205-16378-13k396v.pdf","download_url":"https://www.academia.edu/attachments/50734120/download_file","bulk_download_file_name":"An_efficient_simulation_environment_for.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/50734120/An_Efficient_Simulation_Environment_for_20161205-16378-13k396v-libre.pdf?1480994614=\u0026response-content-disposition=attachment%3B+filename%3DAn_efficient_simulation_environment_for.pdf\u0026Expires=1740159089\u0026Signature=TcS4cSPTi1XluWuJ1-EVprIqIQfFoCOCSuWLHhTeT53l1gTwg0vTFWToPiq1u0wBEkmTxjVhdCcrBfEBaw8dyLIJzEmS1NRc4HZIkyCcGoIH17njCpcui~FxaOpfTvu8BCI6rOrpQgLncuR~X2xrS8tTqhVIqSZX7yXDnYo2NQDcer-Vde8VOgvvI1GXz1C-q-aSdvo-2BtHzA9roa5TqhlRBcb4-j89SopCHl~wDxklishYOt95znvac-qrPrPz9M-fF5Z3PXxNE8wxp6NISXkTsMycs81~ZgylqsD6tfPENW-ojn7JmqKsc5P~tFoq~-Ld-rOJFjIhcUjkFuaDoQ__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="30267590"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/30267590/The_Complex_Structure_of_Receptive_Fields_in_the_Middle_Temporal_Area"><img alt="Research paper thumbnail of The Complex Structure of Receptive Fields in the Middle Temporal Area" class="work-thumbnail" src="https://attachments.academia-assets.com/50734130/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/30267590/The_Complex_Structure_of_Receptive_Fields_in_the_Middle_Temporal_Area">The Complex Structure of Receptive Fields in the Middle Temporal Area</a></div><div class="wp-workCard_item"><span>Frontiers in Systems Neuroscience</span><span>, 2013</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="028f6ec2705cc63c094c327bd57630f2" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":50734130,"asset_id":30267590,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/50734130/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="30267590"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="30267590"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 30267590; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=30267590]").text(description); $(".js-view-count[data-work-id=30267590]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 30267590; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='30267590']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "028f6ec2705cc63c094c327bd57630f2" } } $('.js-work-strip[data-work-id=30267590]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":30267590,"title":"The Complex Structure of Receptive Fields in the Middle Temporal Area","internal_url":"https://www.academia.edu/30267590/The_Complex_Structure_of_Receptive_Fields_in_the_Middle_Temporal_Area","owner_id":57694469,"coauthors_can_edit":true,"owner":{"id":57694469,"first_name":"Micah","middle_initials":null,"last_name":"Richert","page_name":"MicahRichert","domain_name":"independent","created_at":"2016-12-05T19:18:13.684-08:00","display_name":"Micah Richert","url":"https://independent.academia.edu/MicahRichert"},"attachments":[{"id":50734130,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/50734130/thumbnails/1.jpg","file_name":"The_Complex_Structure_of_Receptive_Field20161205-16383-3x86n6.pdf","download_url":"https://www.academia.edu/attachments/50734130/download_file","bulk_download_file_name":"The_Complex_Structure_of_Receptive_Field.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/50734130/The_Complex_Structure_of_Receptive_Field20161205-16383-3x86n6-libre.pdf?1480994625=\u0026response-content-disposition=attachment%3B+filename%3DThe_Complex_Structure_of_Receptive_Field.pdf\u0026Expires=1740159089\u0026Signature=EvOzi2ZYYZNLKTTGRpKA2jYyGxQLnQKaLhgPJv23tiS3y3HJSQcvkusL6lZ46Zh5-3P1LWyaMo3uaTnLNRTrw8L8B~9rU53xrG5O7fcCBPGq2bFIadWzRIuEFWKvd6gW1ETqRpL2hFQMOExzk614Roq~LodK9kYV7oSujMF4GDL5mnTGx5xGyAfukMUtpyRVMQiJ2LXzY2VY1wa4AOcxYko0d~XFosMqHI4McCSDGp9rka0x4m6uSAXlXS6-8BUctg-76I7hYDyaE6wlj~nEKcpfCDeGDxjZv9rhK4NyqE1aR9h0N7756u5ZVKL83vmK0paHFw6VlkLNwXT2IVd~sA__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="30267589"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/30267589/Temporally_evolving_surround_suppression_helps_decoding_in_a_spiking_model_of_motion_processing"><img alt="Research paper thumbnail of Temporally evolving surround suppression helps decoding in a spiking model of motion processing" class="work-thumbnail" src="https://attachments.academia-assets.com/50734127/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/30267589/Temporally_evolving_surround_suppression_helps_decoding_in_a_spiking_model_of_motion_processing">Temporally evolving surround suppression helps decoding in a spiking model of motion processing</a></div><div class="wp-workCard_item"><span>BMC Neuroscience</span><span>, 2013</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="6dfe5a1684da36758e6dc3eec32e4d56" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":50734127,"asset_id":30267589,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/50734127/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="30267589"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="30267589"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 30267589; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=30267589]").text(description); $(".js-view-count[data-work-id=30267589]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 30267589; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='30267589']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "6dfe5a1684da36758e6dc3eec32e4d56" } } $('.js-work-strip[data-work-id=30267589]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":30267589,"title":"Temporally evolving surround suppression helps decoding in a spiking model of motion processing","internal_url":"https://www.academia.edu/30267589/Temporally_evolving_surround_suppression_helps_decoding_in_a_spiking_model_of_motion_processing","owner_id":57694469,"coauthors_can_edit":true,"owner":{"id":57694469,"first_name":"Micah","middle_initials":null,"last_name":"Richert","page_name":"MicahRichert","domain_name":"independent","created_at":"2016-12-05T19:18:13.684-08:00","display_name":"Micah Richert","url":"https://independent.academia.edu/MicahRichert"},"attachments":[{"id":50734127,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/50734127/thumbnails/1.jpg","file_name":"Temporally_evolving_surround_suppression20161205-16378-13me8yz.pdf","download_url":"https://www.academia.edu/attachments/50734127/download_file","bulk_download_file_name":"Temporally_evolving_surround_suppression.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/50734127/Temporally_evolving_surround_suppression20161205-16378-13me8yz-libre.pdf?1480994609=\u0026response-content-disposition=attachment%3B+filename%3DTemporally_evolving_surround_suppression.pdf\u0026Expires=1740159089\u0026Signature=CxDim8az1ML6aUySj4ip5RE3nWvOa9TSBYhqcnTve1nTPy7BqHP5vPh9wp-pX-4NqZ6JgGR0UgkYjnf-FGzsOFAbQxz4~b5iWAHcB6BCCJ5rfnmYlvxGvwcduFbsRRCOZio618jZ9tgCkmz1gZVLl9Ego4ZaYzuHJUnYyJJOHH1x4ww3EUKHoeK6VrYTfo5vePa4fcL6aAiu3buFzUk8-bzwVOySb5DCZFu8jGXlNfteZX-CvzZEqdA6fioQevJafkQxvULNJiY5NHgne4Hc2JxeuZcx2A4ud2TAtR94gWfVNoqID8RT4ZH59zwQJ72A4SiKBPvLui3WLD-kmEy2xA__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="30267588"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/30267588/Spontaneous_emergence_of_simple_and_complex_receptive_fields_in_a_spiking_model_of_V1"><img alt="Research paper thumbnail of Spontaneous emergence of simple and complex receptive fields in a spiking model of V1" class="work-thumbnail" src="https://attachments.academia-assets.com/50734119/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/30267588/Spontaneous_emergence_of_simple_and_complex_receptive_fields_in_a_spiking_model_of_V1">Spontaneous emergence of simple and complex receptive fields in a spiking model of V1</a></div><div class="wp-workCard_item wp-workCard--coauthors"><span>by </span><span><a class="" data-click-track="profile-work-strip-authors" href="https://independent.academia.edu/MicahRichert">Micah Richert</a> and <a class="" data-click-track="profile-work-strip-authors" href="https://independent.academia.edu/DimitryFisher">Dimitry Fisher</a></span></div><div class="wp-workCard_item"><span>BMC Neuroscience</span><span>, 2013</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="8e87a992ec17f74e658de7ef5545e0e5" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":50734119,"asset_id":30267588,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/50734119/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="30267588"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="30267588"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 30267588; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=30267588]").text(description); $(".js-view-count[data-work-id=30267588]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 30267588; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='30267588']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "8e87a992ec17f74e658de7ef5545e0e5" } } $('.js-work-strip[data-work-id=30267588]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":30267588,"title":"Spontaneous emergence of simple and complex receptive fields in a spiking model of V1","internal_url":"https://www.academia.edu/30267588/Spontaneous_emergence_of_simple_and_complex_receptive_fields_in_a_spiking_model_of_V1","owner_id":57694469,"coauthors_can_edit":true,"owner":{"id":57694469,"first_name":"Micah","middle_initials":null,"last_name":"Richert","page_name":"MicahRichert","domain_name":"independent","created_at":"2016-12-05T19:18:13.684-08:00","display_name":"Micah Richert","url":"https://independent.academia.edu/MicahRichert"},"attachments":[{"id":50734119,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/50734119/thumbnails/1.jpg","file_name":"Spontaneous_emergence_of_simple_and_comp20161205-16378-1cagney.pdf","download_url":"https://www.academia.edu/attachments/50734119/download_file","bulk_download_file_name":"Spontaneous_emergence_of_simple_and_comp.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/50734119/Spontaneous_emergence_of_simple_and_comp20161205-16378-1cagney-libre.pdf?1480994608=\u0026response-content-disposition=attachment%3B+filename%3DSpontaneous_emergence_of_simple_and_comp.pdf\u0026Expires=1740159089\u0026Signature=OjddmM8daJ1rwWvpApNIx7TNb34ImV9-SCZu1HTyYOThvXa6lvI3TV0hoI9mxnGde~UNzKgHP2DTIcCsQUPFy9jMXBvEn6BBuEi6Z2E9XisiDFsLMXilSOg2BB3~BRDYBkwEnWph3sH8hkiGknLJ850RxMDvVW7Sip1PgZsDUx6BfMNEmeTAnS2ktvg2jm8h8zhW6e8FjdomwfVCJiGsDi7rC16AKCWctRtmmBtbNqD07yxJFlU6EYHwyJ1z9Ioa1h2of8ue9AFp08vXFEJqMi9BX1TNCGonBN78i2hUlQSxlTLXh95IjTp5ZcKWkYWMFUZzdeiBzrchhmScAH6wzA__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="30267587"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/30267587/Balanced_excitation_and_inhibition_in_a_spiking_model_of_V1"><img alt="Research paper thumbnail of Balanced excitation and inhibition in a spiking model of V1" class="work-thumbnail" src="https://attachments.academia-assets.com/50734122/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/30267587/Balanced_excitation_and_inhibition_in_a_spiking_model_of_V1">Balanced excitation and inhibition in a spiking model of V1</a></div><div class="wp-workCard_item wp-workCard--coauthors"><span>by </span><span><a class="" data-click-track="profile-work-strip-authors" href="https://independent.academia.edu/MicahRichert">Micah Richert</a> and <a class="" data-click-track="profile-work-strip-authors" href="https://independent.academia.edu/DimitryFisher">Dimitry Fisher</a></span></div><div class="wp-workCard_item"><span>BMC Neuroscience</span><span>, 2013</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="5598cf94dea75025bf32e3f0315b7ae9" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":50734122,"asset_id":30267587,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/50734122/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="30267587"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="30267587"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 30267587; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=30267587]").text(description); $(".js-view-count[data-work-id=30267587]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 30267587; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='30267587']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "5598cf94dea75025bf32e3f0315b7ae9" } } $('.js-work-strip[data-work-id=30267587]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":30267587,"title":"Balanced excitation and inhibition in a spiking model of V1","internal_url":"https://www.academia.edu/30267587/Balanced_excitation_and_inhibition_in_a_spiking_model_of_V1","owner_id":57694469,"coauthors_can_edit":true,"owner":{"id":57694469,"first_name":"Micah","middle_initials":null,"last_name":"Richert","page_name":"MicahRichert","domain_name":"independent","created_at":"2016-12-05T19:18:13.684-08:00","display_name":"Micah Richert","url":"https://independent.academia.edu/MicahRichert"},"attachments":[{"id":50734122,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/50734122/thumbnails/1.jpg","file_name":"Balanced_excitation_and_inhibition_in_a_20161205-16383-1ro383g.pdf","download_url":"https://www.academia.edu/attachments/50734122/download_file","bulk_download_file_name":"Balanced_excitation_and_inhibition_in_a.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/50734122/Balanced_excitation_and_inhibition_in_a_20161205-16383-1ro383g-libre.pdf?1480994608=\u0026response-content-disposition=attachment%3B+filename%3DBalanced_excitation_and_inhibition_in_a.pdf\u0026Expires=1740159089\u0026Signature=UK9D6CAjClLOE1-6~aLySnyHWoA8wphHBQG7DvcI3p8jUeqttj708QOgUXhpmPmjUTjYUx~-ODRdxVaUHqZX1CBtGsdPLiSLU6UuDaXf9DwDSLTN-fQaG0FWNZIXQt4g8v-YwWWPvfvhV0xuyQrnOjuCGdHuOkPkk15Qoexuigq8SeHkFSuUlKMK6Ei6L4VzabOFRYBFF8mzgz80SaMrJMrl~ehYey9dT0fUhqCedcNwq8Mqo95P9MKTc48LQB1aX15hWYsj3ZpbpI9qBuP0u26xkKb4Nis1u2UByJ9cbtO5HCMGmO7AEDtp1KPGwbV6wn6bds9Y21q9f4SN4WIDhg__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="30267586"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/30267586/Self_tuning_spike_timing_dependent_plasticity_curves_to_simplify_models_and_improve_learning"><img alt="Research paper thumbnail of Self-tuning spike-timing dependent plasticity curves to simplify models and improve learning" class="work-thumbnail" src="https://attachments.academia-assets.com/50734123/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/30267586/Self_tuning_spike_timing_dependent_plasticity_curves_to_simplify_models_and_improve_learning">Self-tuning spike-timing dependent plasticity curves to simplify models and improve learning</a></div><div class="wp-workCard_item"><span>BMC Neuroscience</span><span>, 2013</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="c54543e774a9078768832b6be0aeb959" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":50734123,"asset_id":30267586,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/50734123/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="30267586"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="30267586"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 30267586; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=30267586]").text(description); $(".js-view-count[data-work-id=30267586]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 30267586; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='30267586']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "c54543e774a9078768832b6be0aeb959" } } $('.js-work-strip[data-work-id=30267586]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":30267586,"title":"Self-tuning spike-timing dependent plasticity curves to simplify models and improve learning","internal_url":"https://www.academia.edu/30267586/Self_tuning_spike_timing_dependent_plasticity_curves_to_simplify_models_and_improve_learning","owner_id":57694469,"coauthors_can_edit":true,"owner":{"id":57694469,"first_name":"Micah","middle_initials":null,"last_name":"Richert","page_name":"MicahRichert","domain_name":"independent","created_at":"2016-12-05T19:18:13.684-08:00","display_name":"Micah Richert","url":"https://independent.academia.edu/MicahRichert"},"attachments":[{"id":50734123,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/50734123/thumbnails/1.jpg","file_name":"Self-tuning_spike-timing_dependent_plast20161205-16378-180ctj5.pdf","download_url":"https://www.academia.edu/attachments/50734123/download_file","bulk_download_file_name":"Self_tuning_spike_timing_dependent_plast.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/50734123/Self-tuning_spike-timing_dependent_plast20161205-16378-180ctj5-libre.pdf?1480994609=\u0026response-content-disposition=attachment%3B+filename%3DSelf_tuning_spike_timing_dependent_plast.pdf\u0026Expires=1740159089\u0026Signature=KlW3J~pvx7gr99jIdQXc2eXMpYiyqTsF03c36QvaJ8X2e-2A1UgN5iISirx6HH1B6g9cBHiSeYcvdtW7w4zJSih2uDEl55nS5Kgu8clNjkENVD2~Ye7jOAAW-OLGx4cjCJLGVpUntW~1wEmlaApvqHbIer9nWuWwbe283hlFVr9iDL2~jPuPOABjwzMnT81UFMMYP8F82YVcuey1ldQGkRGr4IkW3Q4ZzLZj6MMQMSLRJQhbQc4fLvNCysL8zawixOrqfL~ntErjAcn9-WA8wGwJUlpMPd7g8-huNRxc-0L9Xw4L2UZ-I-3NKJKUesTb9EgSVCZVTpG3T-qW-nbp0g__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="30182869"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/30182869/Efficient_Spiking_Neural_Network_Model_of_Pattern_Motion_Selectivity_in_Visual_Cortex"><img alt="Research paper thumbnail of Efficient Spiking Neural Network Model of Pattern Motion Selectivity in Visual Cortex" class="work-thumbnail" src="https://attachments.academia-assets.com/50641757/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/30182869/Efficient_Spiking_Neural_Network_Model_of_Pattern_Motion_Selectivity_in_Visual_Cortex">Efficient Spiking Neural Network Model of Pattern Motion Selectivity in Visual Cortex</a></div><div class="wp-workCard_item wp-workCard--coauthors"><span>by </span><span><a class="" data-click-track="profile-work-strip-authors" href="https://washington.academia.edu/MichaelBeyeler">Michael Beyeler</a> and <a class="" data-click-track="profile-work-strip-authors" href="https://independent.academia.edu/MicahRichert">Micah Richert</a></span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Simulating large-scale models of biological motion perception is challenging, due to the required...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Simulating large-scale models of biological motion perception is challenging, due to the required memory to store the network structure and the computational power needed to quickly solve the neuronal dynamics. A low-cost yet high-performance approach to simulating large-scale neural network models in real-time is to leverage the parallel processing capability of graphics processing units (GPUs). Based on this approach, we present a two-stage model of visual area MT that we believe to be the first large-scale spiking network to demonstrate pattern direction selectivity. In this model, component-direction-selective (CDS) cells in MT linearly combine inputs from V1 cells that have spatiotemporal receptive fields according to the motion energy model of Simoncelli and Heeger. Pattern-direction-selective (PDS) cells in MT are constructed by pooling over MT CDS cells with a wide range of preferred directions. Responses of our model neurons are comparable to electrophysiological results for grating and plaid stimuli as well as speed tuning. The behavioral response of the network in a motion discrimination task is in agreement with psychophysical data. Moreover, our implementation out-performs a previous implementation of the motion energy model by orders of magnitude in terms of computational speed and memory usage. The full network, which comprises 153,216 neurons and approximately 40 million synapses, processes 20 frames per second of a 40脳40 input video in real-time using a single off-the-shelf GPU. To promote the use of this algorithm among neuroscientists and computer vision researchers, the source code for the simulator, the network, and analysis scripts are publicly available.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="e2523ebe87dceeea6b604edea3db8c3b" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":50641757,"asset_id":30182869,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/50641757/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="30182869"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="30182869"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 30182869; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=30182869]").text(description); $(".js-view-count[data-work-id=30182869]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 30182869; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='30182869']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "e2523ebe87dceeea6b604edea3db8c3b" } } $('.js-work-strip[data-work-id=30182869]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":30182869,"title":"Efficient Spiking Neural Network Model of Pattern Motion Selectivity in Visual Cortex","internal_url":"https://www.academia.edu/30182869/Efficient_Spiking_Neural_Network_Model_of_Pattern_Motion_Selectivity_in_Visual_Cortex","owner_id":57466114,"coauthors_can_edit":true,"owner":{"id":57466114,"first_name":"Michael","middle_initials":null,"last_name":"Beyeler","page_name":"MichaelBeyeler","domain_name":"washington","created_at":"2016-11-30T11:41:53.408-08:00","display_name":"Michael Beyeler","url":"https://washington.academia.edu/MichaelBeyeler"},"attachments":[{"id":50641757,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/50641757/thumbnails/1.jpg","file_name":"Beyeler-2014-PatternMotion-Neuroinf.pdf","download_url":"https://www.academia.edu/attachments/50641757/download_file","bulk_download_file_name":"Efficient_Spiking_Neural_Network_Model_o.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/50641757/Beyeler-2014-PatternMotion-Neuroinf-libre.pdf?1480536314=\u0026response-content-disposition=attachment%3B+filename%3DEfficient_Spiking_Neural_Network_Model_o.pdf\u0026Expires=1740048010\u0026Signature=aka3SYhWwru7UvrXmGsqCqng9TIYoaEVU-a~DjSyElyIimGlnEdO18oLogXF3HpTvV0KtT9Pdw06Ct4uMhtlocZ0SIb3T1eH5HpM-YU6kZXdk5g2Grh0i4ouwYgaY1wqqp9lXuexvrdepyQZ6jKUZLArppo5pCsgVIVpQvez7-htS4HtKY-rMO4ZMGrvSVm3erPvpVem-clwkExl9PIfr3Jwj~sGQhKBhtOlAfIyuocdWphl8v2r~O6fnVmVg-glaeoKOqknMjyiuFz7oo9Jx2Os6dHGTylurzkU7vkfyufwr1n7zJhuJ-dCPAdMSiLP8tBBr2DDxNuc0l87kj9AfQ__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> </div><div class="profile--tab_content_container js-tab-pane tab-pane" data-section-id="6256412" id="papers"><div class="js-work-strip profile--work_container" data-work-id="30267602"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/30267602/Rate_Stabilization_Through_Plasticity_in_Spiking_Neuron_Network"><img alt="Research paper thumbnail of Rate Stabilization Through Plasticity in Spiking Neuron Network" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" rel="nofollow" href="https://www.academia.edu/30267602/Rate_Stabilization_Through_Plasticity_in_Spiking_Neuron_Network">Rate Stabilization Through Plasticity in Spiking Neuron Network</a></div><div class="wp-workCard_item wp-workCard--coauthors"><span>by </span><span><a class="" data-click-track="profile-work-strip-authors" href="https://independent.academia.edu/MicahRichert">Micah Richert</a> and <a class="" data-click-track="profile-work-strip-authors" href="https://independent.academia.edu/DimitryFisher">Dimitry Fisher</a></span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="30267602"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="30267602"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 30267602; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=30267602]").text(description); $(".js-view-count[data-work-id=30267602]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 30267602; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='30267602']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=30267602]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":30267602,"title":"Rate Stabilization Through Plasticity in Spiking Neuron Network","internal_url":"https://www.academia.edu/30267602/Rate_Stabilization_Through_Plasticity_in_Spiking_Neuron_Network","owner_id":57694469,"coauthors_can_edit":true,"owner":{"id":57694469,"first_name":"Micah","middle_initials":null,"last_name":"Richert","page_name":"MicahRichert","domain_name":"independent","created_at":"2016-12-05T19:18:13.684-08:00","display_name":"Micah Richert","url":"https://independent.academia.edu/MicahRichert"},"attachments":[]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="30267601"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/30267601/Spiking_Network_Apparatus_and_Method_with_Bimodal_Spike_Timing_Dependent_Plasticity"><img alt="Research paper thumbnail of Spiking Network Apparatus and Method with Bimodal Spike-Timing Dependent Plasticity" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" rel="nofollow" href="https://www.academia.edu/30267601/Spiking_Network_Apparatus_and_Method_with_Bimodal_Spike_Timing_Dependent_Plasticity">Spiking Network Apparatus and Method with Bimodal Spike-Timing Dependent Plasticity</a></div><div class="wp-workCard_item wp-workCard--coauthors"><span>by </span><span><a class="" data-click-track="profile-work-strip-authors" href="https://independent.academia.edu/MicahRichert">Micah Richert</a> and <a class="" data-click-track="profile-work-strip-authors" href="https://independent.academia.edu/SachSokol">Sach Sokol</a></span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="30267601"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="30267601"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 30267601; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=30267601]").text(description); $(".js-view-count[data-work-id=30267601]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 30267601; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='30267601']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=30267601]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":30267601,"title":"Spiking Network Apparatus and Method with Bimodal Spike-Timing Dependent Plasticity","internal_url":"https://www.academia.edu/30267601/Spiking_Network_Apparatus_and_Method_with_Bimodal_Spike_Timing_Dependent_Plasticity","owner_id":57694469,"coauthors_can_edit":true,"owner":{"id":57694469,"first_name":"Micah","middle_initials":null,"last_name":"Richert","page_name":"MicahRichert","domain_name":"independent","created_at":"2016-12-05T19:18:13.684-08:00","display_name":"Micah Richert","url":"https://independent.academia.edu/MicahRichert"},"attachments":[]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="30267600"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/30267600/Adaptive_Plasticity_Apparatus_and_Methods_for_Spiking_Neuron_Network"><img alt="Research paper thumbnail of Adaptive Plasticity Apparatus and Methods for Spiking Neuron Network" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" rel="nofollow" href="https://www.academia.edu/30267600/Adaptive_Plasticity_Apparatus_and_Methods_for_Spiking_Neuron_Network">Adaptive Plasticity Apparatus and Methods for Spiking Neuron Network</a></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="30267600"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="30267600"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 30267600; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=30267600]").text(description); $(".js-view-count[data-work-id=30267600]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 30267600; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='30267600']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=30267600]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":30267600,"title":"Adaptive Plasticity Apparatus and Methods for Spiking Neuron Network","internal_url":"https://www.academia.edu/30267600/Adaptive_Plasticity_Apparatus_and_Methods_for_Spiking_Neuron_Network","owner_id":57694469,"coauthors_can_edit":true,"owner":{"id":57694469,"first_name":"Micah","middle_initials":null,"last_name":"Richert","page_name":"MicahRichert","domain_name":"independent","created_at":"2016-12-05T19:18:13.684-08:00","display_name":"Micah Richert","url":"https://independent.academia.edu/MicahRichert"},"attachments":[]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="30267599"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/30267599/Apparatus_and_Methods_for_Activity_Based_Plasticity_in_a_Spiking_Neuron_Network"><img alt="Research paper thumbnail of Apparatus and Methods for Activity-Based Plasticity in a Spiking Neuron Network" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" rel="nofollow" href="https://www.academia.edu/30267599/Apparatus_and_Methods_for_Activity_Based_Plasticity_in_a_Spiking_Neuron_Network">Apparatus and Methods for Activity-Based Plasticity in a Spiking Neuron Network</a></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="30267599"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="30267599"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 30267599; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=30267599]").text(description); $(".js-view-count[data-work-id=30267599]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 30267599; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='30267599']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=30267599]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":30267599,"title":"Apparatus and Methods for Activity-Based Plasticity in a Spiking Neuron Network","internal_url":"https://www.academia.edu/30267599/Apparatus_and_Methods_for_Activity_Based_Plasticity_in_a_Spiking_Neuron_Network","owner_id":57694469,"coauthors_can_edit":true,"owner":{"id":57694469,"first_name":"Micah","middle_initials":null,"last_name":"Richert","page_name":"MicahRichert","domain_name":"independent","created_at":"2016-12-05T19:18:13.684-08:00","display_name":"Micah Richert","url":"https://independent.academia.edu/MicahRichert"},"attachments":[]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="30267598"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/30267598/Apparatus_and_Methods_for_Event_Based_Communication_in_a_Spiking_Neuron_Networks"><img alt="Research paper thumbnail of Apparatus and Methods for Event-Based Communication in a Spiking Neuron Networks" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" rel="nofollow" href="https://www.academia.edu/30267598/Apparatus_and_Methods_for_Event_Based_Communication_in_a_Spiking_Neuron_Networks">Apparatus and Methods for Event-Based Communication in a Spiking Neuron Networks</a></div><div class="wp-workCard_item wp-workCard--coauthors"><span>by </span><span><a class="" data-click-track="profile-work-strip-authors" href="https://independent.academia.edu/MicahRichert">Micah Richert</a> and <a class="" data-click-track="profile-work-strip-authors" href="https://braincorporation.academia.edu/OlegSinyavskiy">Oleg Sinyavskiy</a></span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="30267598"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="30267598"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 30267598; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=30267598]").text(description); $(".js-view-count[data-work-id=30267598]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 30267598; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='30267598']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=30267598]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":30267598,"title":"Apparatus and Methods for Event-Based Communication in a Spiking Neuron Networks","internal_url":"https://www.academia.edu/30267598/Apparatus_and_Methods_for_Event_Based_Communication_in_a_Spiking_Neuron_Networks","owner_id":57694469,"coauthors_can_edit":true,"owner":{"id":57694469,"first_name":"Micah","middle_initials":null,"last_name":"Richert","page_name":"MicahRichert","domain_name":"independent","created_at":"2016-12-05T19:18:13.684-08:00","display_name":"Micah Richert","url":"https://independent.academia.edu/MicahRichert"},"attachments":[]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="30267597"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/30267597/Apparatus_and_Methods_for_Rate_Modulated_Plasticity_in_a_Spiking_Neuron_Network"><img alt="Research paper thumbnail of Apparatus and Methods for Rate-Modulated Plasticity in a Spiking Neuron Network" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" rel="nofollow" href="https://www.academia.edu/30267597/Apparatus_and_Methods_for_Rate_Modulated_Plasticity_in_a_Spiking_Neuron_Network">Apparatus and Methods for Rate-Modulated Plasticity in a Spiking Neuron Network</a></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="30267597"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="30267597"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 30267597; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=30267597]").text(description); $(".js-view-count[data-work-id=30267597]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 30267597; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='30267597']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=30267597]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":30267597,"title":"Apparatus and Methods for Rate-Modulated Plasticity in a Spiking Neuron Network","internal_url":"https://www.academia.edu/30267597/Apparatus_and_Methods_for_Rate_Modulated_Plasticity_in_a_Spiking_Neuron_Network","owner_id":57694469,"coauthors_can_edit":true,"owner":{"id":57694469,"first_name":"Micah","middle_initials":null,"last_name":"Richert","page_name":"MicahRichert","domain_name":"independent","created_at":"2016-12-05T19:18:13.684-08:00","display_name":"Micah Richert","url":"https://independent.academia.edu/MicahRichert"},"attachments":[]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="30267596"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/30267596/Beyond_inhibition_lateral_modulation_of_plasticity_of_feedforward_synapses_in_a_spiking_model_of_V1"><img alt="Research paper thumbnail of Beyond inhibition: lateral modulation of plasticity of feedforward synapses in a spiking model of V1" class="work-thumbnail" src="https://attachments.academia-assets.com/50734126/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/30267596/Beyond_inhibition_lateral_modulation_of_plasticity_of_feedforward_synapses_in_a_spiking_model_of_V1">Beyond inhibition: lateral modulation of plasticity of feedforward synapses in a spiking model of V1</a></div><div class="wp-workCard_item"><span>BMC Neuroscience</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="fca7860d674f818f954e2e69666fba70" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":50734126,"asset_id":30267596,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/50734126/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="30267596"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="30267596"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 30267596; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=30267596]").text(description); $(".js-view-count[data-work-id=30267596]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 30267596; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='30267596']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "fca7860d674f818f954e2e69666fba70" } } $('.js-work-strip[data-work-id=30267596]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":30267596,"title":"Beyond inhibition: lateral modulation of plasticity of feedforward synapses in a spiking model of V1","internal_url":"https://www.academia.edu/30267596/Beyond_inhibition_lateral_modulation_of_plasticity_of_feedforward_synapses_in_a_spiking_model_of_V1","owner_id":57694469,"coauthors_can_edit":true,"owner":{"id":57694469,"first_name":"Micah","middle_initials":null,"last_name":"Richert","page_name":"MicahRichert","domain_name":"independent","created_at":"2016-12-05T19:18:13.684-08:00","display_name":"Micah Richert","url":"https://independent.academia.edu/MicahRichert"},"attachments":[{"id":50734126,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/50734126/thumbnails/1.jpg","file_name":"Beyond_inhibition_lateral_modulation_of_20161205-16376-x1dt2i.pdf","download_url":"https://www.academia.edu/attachments/50734126/download_file","bulk_download_file_name":"Beyond_inhibition_lateral_modulation_of.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/50734126/Beyond_inhibition_lateral_modulation_of_20161205-16376-x1dt2i-libre.pdf?1480994608=\u0026response-content-disposition=attachment%3B+filename%3DBeyond_inhibition_lateral_modulation_of.pdf\u0026Expires=1740159089\u0026Signature=SVVNIXIGzH9MnwbF8LJNjHj-DmpdhYBUzMhWGncNzFUSi5437Sr2IkkinM4V8sVqLwKlm85zXpqDhSGsquRzJ4YdiBzVYkJUznXO8XP1tQD78fUy~296cQOIWAQL3ua31eyD04s8h-4HD-yS70XI~y6~p8CvRO8U-Gvfs807dL~PfnlGpkunKoV2aY3YZuklxlenTGvt4sLDB-AHzXnySZqJSadYvVdcTIuDAUT~UaHa6jlwlsfAAqt-rEuAGfFDlW6pyn2Fbh07g9xbRo07q5ZeFUEo2iFukU84jmXdJB4aBFW1jxIJT2hF2UZB~6jXSaHwXTJXTMZHlSdBDQ0K6g__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="30267595"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/30267595/Neuromorphic_modeling_abstractions_and_simulation_of_large_scale_cortical_networks"><img alt="Research paper thumbnail of Neuromorphic modeling abstractions and simulation of large-scale cortical networks" class="work-thumbnail" src="https://attachments.academia-assets.com/50734129/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/30267595/Neuromorphic_modeling_abstractions_and_simulation_of_large_scale_cortical_networks">Neuromorphic modeling abstractions and simulation of large-scale cortical networks</a></div><div class="wp-workCard_item"><span>2011 IEEE/ACM International Conference on Computer-Aided Design (ICCAD)</span><span>, 2011</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Biological neural systems are well known for their robust and power-efficient operation in highly...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Biological neural systems are well known for their robust and power-efficient operation in highly noisy environments. We outline key modeling abstractions for the brain and focus on spiking neural network models. We discuss aspects of neuronal processing and computational issues related to modeling these processes. Although many of these algorithms can be efficiently realized in specialized hardware, we present a case study of simulation of the visual cortex using a GPU based simulation environment that is readily usable by neuroscientists and computer scientists and efficient enough to construct very large networks comparable to brain networks.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="f671eb612caf6c0985a19871ca067271" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":50734129,"asset_id":30267595,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/50734129/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="30267595"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="30267595"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 30267595; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=30267595]").text(description); $(".js-view-count[data-work-id=30267595]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 30267595; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='30267595']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "f671eb612caf6c0985a19871ca067271" } } $('.js-work-strip[data-work-id=30267595]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":30267595,"title":"Neuromorphic modeling abstractions and simulation of large-scale cortical networks","internal_url":"https://www.academia.edu/30267595/Neuromorphic_modeling_abstractions_and_simulation_of_large_scale_cortical_networks","owner_id":57694469,"coauthors_can_edit":true,"owner":{"id":57694469,"first_name":"Micah","middle_initials":null,"last_name":"Richert","page_name":"MicahRichert","domain_name":"independent","created_at":"2016-12-05T19:18:13.684-08:00","display_name":"Micah Richert","url":"https://independent.academia.edu/MicahRichert"},"attachments":[{"id":50734129,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/50734129/thumbnails/1.jpg","file_name":"Neuromorphic_modeling_abstractions_and_s20161205-16376-6bcmov.pdf","download_url":"https://www.academia.edu/attachments/50734129/download_file","bulk_download_file_name":"Neuromorphic_modeling_abstractions_and_s.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/50734129/Neuromorphic_modeling_abstractions_and_s20161205-16376-6bcmov-libre.pdf?1480994614=\u0026response-content-disposition=attachment%3B+filename%3DNeuromorphic_modeling_abstractions_and_s.pdf\u0026Expires=1740159089\u0026Signature=M~DpuCrKf~fufINBuACt8DNRe5-RKTEKiefpK0MswkQ6fEBIpjPXTB-a~QSis5QhHox~0U23bo-k4s4GiyqQ9t5EKUV36VIJn9Zp8STvSRpFxs3IT62ATbIYcaZPK3oVP-YzFHq7RXyAqkR45b55CQiT6NDLVOcSS7xB-GMM~3fp3~rP1F67zqKnqQnHObKC9n2FfqDY-b07jQe8P3iuvW2AIF5xtnaZL9YLv5S4ujKuUbiBAU~MqjeCLOdafnwSJAWy2QIpahYAw0zAdCA5XM2YiKA6Yi~j2ZiLIG~pMfSTR2DRfuiafTjkkQ2FH2XdSvJmrdFLNVRtrN-DVljcEA__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="30267594"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/30267594/A_spiking_model_of_superior_colliculus_for_bottom_up_saliency"><img alt="Research paper thumbnail of A spiking model of superior colliculus for bottom-up saliency" class="work-thumbnail" src="https://attachments.academia-assets.com/50734125/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/30267594/A_spiking_model_of_superior_colliculus_for_bottom_up_saliency">A spiking model of superior colliculus for bottom-up saliency</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Active exploration of visual space is controlled by a saccadic system using a combination of bott...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Active exploration of visual space is controlled by a saccadic system using a combination of bottom-up and topdown signals. Rate based models [1] and simple V1 models (e.g. [2]), have been proposed to explain bottom-up or "pop-out" attentional mechanisms. We present a biologically detailed spiking model of superior colliculus to generate saccadic eye movements using a competitive mechanism based on spike arrival times. Various pop-out tasks based on color, orientation features and motion is used to analyze the reaction time of the model.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="68179d6b7e6b82b1836fb147ee7b05c1" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":50734125,"asset_id":30267594,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/50734125/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="30267594"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="30267594"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 30267594; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=30267594]").text(description); $(".js-view-count[data-work-id=30267594]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 30267594; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='30267594']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "68179d6b7e6b82b1836fb147ee7b05c1" } } $('.js-work-strip[data-work-id=30267594]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":30267594,"title":"A spiking model of superior colliculus for bottom-up saliency","internal_url":"https://www.academia.edu/30267594/A_spiking_model_of_superior_colliculus_for_bottom_up_saliency","owner_id":57694469,"coauthors_can_edit":true,"owner":{"id":57694469,"first_name":"Micah","middle_initials":null,"last_name":"Richert","page_name":"MicahRichert","domain_name":"independent","created_at":"2016-12-05T19:18:13.684-08:00","display_name":"Micah Richert","url":"https://independent.academia.edu/MicahRichert"},"attachments":[{"id":50734125,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/50734125/thumbnails/1.jpg","file_name":"1471-2202-14-S1-P185.pdf","download_url":"https://www.academia.edu/attachments/50734125/download_file","bulk_download_file_name":"A_spiking_model_of_superior_colliculus_f.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/50734125/1471-2202-14-S1-P185-libre.pdf?1480994608=\u0026response-content-disposition=attachment%3B+filename%3DA_spiking_model_of_superior_colliculus_f.pdf\u0026Expires=1740159089\u0026Signature=N0TihQutzy~YSflxlmjk~Hwa4LlIRmmPQ1gkAdPHnGJwhl-QOeHOoUOqv-N0oxY6UtCOuljv21MENavqRn0iK3ilCwa1-KWrADE86RY7OZ0nNrv9QV6cQZ-kvP8GoD7Yh2fq94Ep9Fbq86sbzjso9L4WP2u6336uKU02XjR69BpCciG~8hCuXuqUbQH-MgSe7A7O3fJlWv~t-2IEpah0u9cF7NYk~-Miro0iEOOtdlasPQWAIUX9WR05e4Y3zfF3sXu0HAfHC0WV-LzLle4wEXO-dqAnAqTtBIQu2PnBz81p3TLv2Y-Lns5n1j2YDMOe-7dslLuT8yWQVkGw2PANUg__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="30267593"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/30267593/Towards_reverse_engineering_the_brain_Modeling_abstractions_and_simulation_frameworks"><img alt="Research paper thumbnail of Towards reverse engineering the brain: Modeling abstractions and simulation frameworks" class="work-thumbnail" src="https://attachments.academia-assets.com/50734133/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/30267593/Towards_reverse_engineering_the_brain_Modeling_abstractions_and_simulation_frameworks">Towards reverse engineering the brain: Modeling abstractions and simulation frameworks</a></div><div class="wp-workCard_item"><span>2010 18th IEEE/IFIP International Conference on VLSI and System-on-Chip</span><span>, 2010</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Biological neural systems are well known for their robust and power-efficient operation in highly...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Biological neural systems are well known for their robust and power-efficient operation in highly noisy environments. Biological circuits are made up of low-precision, unreliable and massively parallel neural elements with highly reconfigurable and plastic connections. Two of the most interesting properties of the neural systems are its self-organizing capabilities and its template architecture. Recent research in spiking neural networks has demonstrated interesting principles about learning and neural computation. Understanding and applying these principles to practical problems is only possible if large-scale spiking neural simulators can be constructed. Recent advances in low-cost multiprocessor architectures make it possible to build large-scale spiking network simulators. In this paper we review modeling abstractions for neural circuits and frameworks for modeling, simulating and analyzing spiking neural networks.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="4a1da5bb1ddece77e645e1e2ac301163" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":50734133,"asset_id":30267593,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/50734133/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="30267593"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="30267593"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 30267593; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=30267593]").text(description); $(".js-view-count[data-work-id=30267593]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 30267593; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='30267593']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "4a1da5bb1ddece77e645e1e2ac301163" } } $('.js-work-strip[data-work-id=30267593]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":30267593,"title":"Towards reverse engineering the brain: Modeling abstractions and simulation frameworks","internal_url":"https://www.academia.edu/30267593/Towards_reverse_engineering_the_brain_Modeling_abstractions_and_simulation_frameworks","owner_id":57694469,"coauthors_can_edit":true,"owner":{"id":57694469,"first_name":"Micah","middle_initials":null,"last_name":"Richert","page_name":"MicahRichert","domain_name":"independent","created_at":"2016-12-05T19:18:13.684-08:00","display_name":"Micah Richert","url":"https://independent.academia.edu/MicahRichert"},"attachments":[{"id":50734133,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/50734133/thumbnails/1.jpg","file_name":"Towards_reverse_engineering_the_brain_Mo20161205-16383-1qao2gl.pdf","download_url":"https://www.academia.edu/attachments/50734133/download_file","bulk_download_file_name":"Towards_reverse_engineering_the_brain_Mo.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/50734133/Towards_reverse_engineering_the_brain_Mo20161205-16383-1qao2gl-libre.pdf?1480994614=\u0026response-content-disposition=attachment%3B+filename%3DTowards_reverse_engineering_the_brain_Mo.pdf\u0026Expires=1740159089\u0026Signature=C6U4T2bHZ5Pr8LlCTBKOtaNi6vHy-mEx2uaUlCDpKI1MvhpBCpwKQaWDPc1505bW15baBE67FbjoKx1-vyXAIwJP5oCgwX230o6YbfbnLtIWV4Jjp-828JgB8JFVXTO6DxMd-o6fAQ54pJZV4biCKiWvtUC6u3Qst7RQwfmeqYdHu~d75zsh0NuHavxACJ9SCR5IUB3e9nmAG6nm2F1rB8I380ThEQY0IKw0850~9AQF2DmxRJUoVRfZUldBOJjwtC5cnaJ7bUX2Jh19PbtntSeBoyJqT8vf7MwgE4NQ140g3Ar-~zrUCsR2nmtlx-qD5lwzVE0WnG3kqstWfTVtUQ__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="30267592"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/30267592/Biologically_plausible_models_of_homeostasis_and_STDP_Stability_and_learning_in_spiking_neural_networks"><img alt="Research paper thumbnail of Biologically plausible models of homeostasis and STDP: Stability and learning in spiking neural networks" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" rel="nofollow" href="https://www.academia.edu/30267592/Biologically_plausible_models_of_homeostasis_and_STDP_Stability_and_learning_in_spiking_neural_networks">Biologically plausible models of homeostasis and STDP: Stability and learning in spiking neural networks</a></div><div class="wp-workCard_item"><span>The 2013 International Joint Conference on Neural Networks (IJCNN)</span><span>, 2013</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">ABSTRACT</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="30267592"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="30267592"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 30267592; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=30267592]").text(description); $(".js-view-count[data-work-id=30267592]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 30267592; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='30267592']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=30267592]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":30267592,"title":"Biologically plausible models of homeostasis and STDP: Stability and learning in spiking neural networks","internal_url":"https://www.academia.edu/30267592/Biologically_plausible_models_of_homeostasis_and_STDP_Stability_and_learning_in_spiking_neural_networks","owner_id":57694469,"coauthors_can_edit":true,"owner":{"id":57694469,"first_name":"Micah","middle_initials":null,"last_name":"Richert","page_name":"MicahRichert","domain_name":"independent","created_at":"2016-12-05T19:18:13.684-08:00","display_name":"Micah Richert","url":"https://independent.academia.edu/MicahRichert"},"attachments":[]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="30267591"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/30267591/An_efficient_simulation_environment_for_modeling_large_scale_cortical_processing"><img alt="Research paper thumbnail of An efficient simulation environment for modeling large-scale cortical processing" class="work-thumbnail" src="https://attachments.academia-assets.com/50734120/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/30267591/An_efficient_simulation_environment_for_modeling_large_scale_cortical_processing">An efficient simulation environment for modeling large-scale cortical processing</a></div><div class="wp-workCard_item"><span>Frontiers in neuroinformatics</span><span>, 2011</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">We have developed a spiking neural network simulator, which is both easy to use and computational...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">We have developed a spiking neural network simulator, which is both easy to use and computationally efficient, for the generation of large-scale computational neuroscience models. The simulator implements current or conductance based Izhikevich neuron networks, having spike-timing dependent plasticity and short-term plasticity. It uses a standard network construction interface. The simulator allows for execution on either GPUs or CPUs. The simulator, which is written in C/C++, allows for both fine grain and coarse grain specificity of a host of parameters. We demonstrate the ease of use and computational efficiency of this model by implementing a large-scale model of cortical areas V1, V4, and area MT. The complete model, which has 138,240 neurons and approximately 30 million synapses, runs in real-time on an off-the-shelf GPU. The simulator source code, as well as the source code for the cortical model examples is publicly available.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="75318afb41001bbcfb69fa37b084f079" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":50734120,"asset_id":30267591,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/50734120/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="30267591"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="30267591"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 30267591; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=30267591]").text(description); $(".js-view-count[data-work-id=30267591]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 30267591; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='30267591']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "75318afb41001bbcfb69fa37b084f079" } } $('.js-work-strip[data-work-id=30267591]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":30267591,"title":"An efficient simulation environment for modeling large-scale cortical processing","internal_url":"https://www.academia.edu/30267591/An_efficient_simulation_environment_for_modeling_large_scale_cortical_processing","owner_id":57694469,"coauthors_can_edit":true,"owner":{"id":57694469,"first_name":"Micah","middle_initials":null,"last_name":"Richert","page_name":"MicahRichert","domain_name":"independent","created_at":"2016-12-05T19:18:13.684-08:00","display_name":"Micah Richert","url":"https://independent.academia.edu/MicahRichert"},"attachments":[{"id":50734120,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/50734120/thumbnails/1.jpg","file_name":"An_Efficient_Simulation_Environment_for_20161205-16378-13k396v.pdf","download_url":"https://www.academia.edu/attachments/50734120/download_file","bulk_download_file_name":"An_efficient_simulation_environment_for.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/50734120/An_Efficient_Simulation_Environment_for_20161205-16378-13k396v-libre.pdf?1480994614=\u0026response-content-disposition=attachment%3B+filename%3DAn_efficient_simulation_environment_for.pdf\u0026Expires=1740159089\u0026Signature=TcS4cSPTi1XluWuJ1-EVprIqIQfFoCOCSuWLHhTeT53l1gTwg0vTFWToPiq1u0wBEkmTxjVhdCcrBfEBaw8dyLIJzEmS1NRc4HZIkyCcGoIH17njCpcui~FxaOpfTvu8BCI6rOrpQgLncuR~X2xrS8tTqhVIqSZX7yXDnYo2NQDcer-Vde8VOgvvI1GXz1C-q-aSdvo-2BtHzA9roa5TqhlRBcb4-j89SopCHl~wDxklishYOt95znvac-qrPrPz9M-fF5Z3PXxNE8wxp6NISXkTsMycs81~ZgylqsD6tfPENW-ojn7JmqKsc5P~tFoq~-Ld-rOJFjIhcUjkFuaDoQ__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="30267590"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/30267590/The_Complex_Structure_of_Receptive_Fields_in_the_Middle_Temporal_Area"><img alt="Research paper thumbnail of The Complex Structure of Receptive Fields in the Middle Temporal Area" class="work-thumbnail" src="https://attachments.academia-assets.com/50734130/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/30267590/The_Complex_Structure_of_Receptive_Fields_in_the_Middle_Temporal_Area">The Complex Structure of Receptive Fields in the Middle Temporal Area</a></div><div class="wp-workCard_item"><span>Frontiers in Systems Neuroscience</span><span>, 2013</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="028f6ec2705cc63c094c327bd57630f2" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":50734130,"asset_id":30267590,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/50734130/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="30267590"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="30267590"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 30267590; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=30267590]").text(description); $(".js-view-count[data-work-id=30267590]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 30267590; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='30267590']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "028f6ec2705cc63c094c327bd57630f2" } } $('.js-work-strip[data-work-id=30267590]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":30267590,"title":"The Complex Structure of Receptive Fields in the Middle Temporal Area","internal_url":"https://www.academia.edu/30267590/The_Complex_Structure_of_Receptive_Fields_in_the_Middle_Temporal_Area","owner_id":57694469,"coauthors_can_edit":true,"owner":{"id":57694469,"first_name":"Micah","middle_initials":null,"last_name":"Richert","page_name":"MicahRichert","domain_name":"independent","created_at":"2016-12-05T19:18:13.684-08:00","display_name":"Micah Richert","url":"https://independent.academia.edu/MicahRichert"},"attachments":[{"id":50734130,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/50734130/thumbnails/1.jpg","file_name":"The_Complex_Structure_of_Receptive_Field20161205-16383-3x86n6.pdf","download_url":"https://www.academia.edu/attachments/50734130/download_file","bulk_download_file_name":"The_Complex_Structure_of_Receptive_Field.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/50734130/The_Complex_Structure_of_Receptive_Field20161205-16383-3x86n6-libre.pdf?1480994625=\u0026response-content-disposition=attachment%3B+filename%3DThe_Complex_Structure_of_Receptive_Field.pdf\u0026Expires=1740159089\u0026Signature=EvOzi2ZYYZNLKTTGRpKA2jYyGxQLnQKaLhgPJv23tiS3y3HJSQcvkusL6lZ46Zh5-3P1LWyaMo3uaTnLNRTrw8L8B~9rU53xrG5O7fcCBPGq2bFIadWzRIuEFWKvd6gW1ETqRpL2hFQMOExzk614Roq~LodK9kYV7oSujMF4GDL5mnTGx5xGyAfukMUtpyRVMQiJ2LXzY2VY1wa4AOcxYko0d~XFosMqHI4McCSDGp9rka0x4m6uSAXlXS6-8BUctg-76I7hYDyaE6wlj~nEKcpfCDeGDxjZv9rhK4NyqE1aR9h0N7756u5ZVKL83vmK0paHFw6VlkLNwXT2IVd~sA__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="30267589"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/30267589/Temporally_evolving_surround_suppression_helps_decoding_in_a_spiking_model_of_motion_processing"><img alt="Research paper thumbnail of Temporally evolving surround suppression helps decoding in a spiking model of motion processing" class="work-thumbnail" src="https://attachments.academia-assets.com/50734127/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/30267589/Temporally_evolving_surround_suppression_helps_decoding_in_a_spiking_model_of_motion_processing">Temporally evolving surround suppression helps decoding in a spiking model of motion processing</a></div><div class="wp-workCard_item"><span>BMC Neuroscience</span><span>, 2013</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="6dfe5a1684da36758e6dc3eec32e4d56" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":50734127,"asset_id":30267589,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/50734127/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="30267589"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="30267589"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 30267589; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=30267589]").text(description); $(".js-view-count[data-work-id=30267589]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 30267589; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='30267589']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "6dfe5a1684da36758e6dc3eec32e4d56" } } $('.js-work-strip[data-work-id=30267589]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":30267589,"title":"Temporally evolving surround suppression helps decoding in a spiking model of motion processing","internal_url":"https://www.academia.edu/30267589/Temporally_evolving_surround_suppression_helps_decoding_in_a_spiking_model_of_motion_processing","owner_id":57694469,"coauthors_can_edit":true,"owner":{"id":57694469,"first_name":"Micah","middle_initials":null,"last_name":"Richert","page_name":"MicahRichert","domain_name":"independent","created_at":"2016-12-05T19:18:13.684-08:00","display_name":"Micah Richert","url":"https://independent.academia.edu/MicahRichert"},"attachments":[{"id":50734127,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/50734127/thumbnails/1.jpg","file_name":"Temporally_evolving_surround_suppression20161205-16378-13me8yz.pdf","download_url":"https://www.academia.edu/attachments/50734127/download_file","bulk_download_file_name":"Temporally_evolving_surround_suppression.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/50734127/Temporally_evolving_surround_suppression20161205-16378-13me8yz-libre.pdf?1480994609=\u0026response-content-disposition=attachment%3B+filename%3DTemporally_evolving_surround_suppression.pdf\u0026Expires=1740159089\u0026Signature=CxDim8az1ML6aUySj4ip5RE3nWvOa9TSBYhqcnTve1nTPy7BqHP5vPh9wp-pX-4NqZ6JgGR0UgkYjnf-FGzsOFAbQxz4~b5iWAHcB6BCCJ5rfnmYlvxGvwcduFbsRRCOZio618jZ9tgCkmz1gZVLl9Ego4ZaYzuHJUnYyJJOHH1x4ww3EUKHoeK6VrYTfo5vePa4fcL6aAiu3buFzUk8-bzwVOySb5DCZFu8jGXlNfteZX-CvzZEqdA6fioQevJafkQxvULNJiY5NHgne4Hc2JxeuZcx2A4ud2TAtR94gWfVNoqID8RT4ZH59zwQJ72A4SiKBPvLui3WLD-kmEy2xA__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="30267588"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/30267588/Spontaneous_emergence_of_simple_and_complex_receptive_fields_in_a_spiking_model_of_V1"><img alt="Research paper thumbnail of Spontaneous emergence of simple and complex receptive fields in a spiking model of V1" class="work-thumbnail" src="https://attachments.academia-assets.com/50734119/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/30267588/Spontaneous_emergence_of_simple_and_complex_receptive_fields_in_a_spiking_model_of_V1">Spontaneous emergence of simple and complex receptive fields in a spiking model of V1</a></div><div class="wp-workCard_item wp-workCard--coauthors"><span>by </span><span><a class="" data-click-track="profile-work-strip-authors" href="https://independent.academia.edu/MicahRichert">Micah Richert</a> and <a class="" data-click-track="profile-work-strip-authors" href="https://independent.academia.edu/DimitryFisher">Dimitry Fisher</a></span></div><div class="wp-workCard_item"><span>BMC Neuroscience</span><span>, 2013</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="8e87a992ec17f74e658de7ef5545e0e5" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":50734119,"asset_id":30267588,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/50734119/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="30267588"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="30267588"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 30267588; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=30267588]").text(description); $(".js-view-count[data-work-id=30267588]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 30267588; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='30267588']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "8e87a992ec17f74e658de7ef5545e0e5" } } $('.js-work-strip[data-work-id=30267588]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":30267588,"title":"Spontaneous emergence of simple and complex receptive fields in a spiking model of V1","internal_url":"https://www.academia.edu/30267588/Spontaneous_emergence_of_simple_and_complex_receptive_fields_in_a_spiking_model_of_V1","owner_id":57694469,"coauthors_can_edit":true,"owner":{"id":57694469,"first_name":"Micah","middle_initials":null,"last_name":"Richert","page_name":"MicahRichert","domain_name":"independent","created_at":"2016-12-05T19:18:13.684-08:00","display_name":"Micah Richert","url":"https://independent.academia.edu/MicahRichert"},"attachments":[{"id":50734119,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/50734119/thumbnails/1.jpg","file_name":"Spontaneous_emergence_of_simple_and_comp20161205-16378-1cagney.pdf","download_url":"https://www.academia.edu/attachments/50734119/download_file","bulk_download_file_name":"Spontaneous_emergence_of_simple_and_comp.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/50734119/Spontaneous_emergence_of_simple_and_comp20161205-16378-1cagney-libre.pdf?1480994608=\u0026response-content-disposition=attachment%3B+filename%3DSpontaneous_emergence_of_simple_and_comp.pdf\u0026Expires=1740159089\u0026Signature=OjddmM8daJ1rwWvpApNIx7TNb34ImV9-SCZu1HTyYOThvXa6lvI3TV0hoI9mxnGde~UNzKgHP2DTIcCsQUPFy9jMXBvEn6BBuEi6Z2E9XisiDFsLMXilSOg2BB3~BRDYBkwEnWph3sH8hkiGknLJ850RxMDvVW7Sip1PgZsDUx6BfMNEmeTAnS2ktvg2jm8h8zhW6e8FjdomwfVCJiGsDi7rC16AKCWctRtmmBtbNqD07yxJFlU6EYHwyJ1z9Ioa1h2of8ue9AFp08vXFEJqMi9BX1TNCGonBN78i2hUlQSxlTLXh95IjTp5ZcKWkYWMFUZzdeiBzrchhmScAH6wzA__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="30267587"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/30267587/Balanced_excitation_and_inhibition_in_a_spiking_model_of_V1"><img alt="Research paper thumbnail of Balanced excitation and inhibition in a spiking model of V1" class="work-thumbnail" src="https://attachments.academia-assets.com/50734122/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/30267587/Balanced_excitation_and_inhibition_in_a_spiking_model_of_V1">Balanced excitation and inhibition in a spiking model of V1</a></div><div class="wp-workCard_item wp-workCard--coauthors"><span>by </span><span><a class="" data-click-track="profile-work-strip-authors" href="https://independent.academia.edu/MicahRichert">Micah Richert</a> and <a class="" data-click-track="profile-work-strip-authors" href="https://independent.academia.edu/DimitryFisher">Dimitry Fisher</a></span></div><div class="wp-workCard_item"><span>BMC Neuroscience</span><span>, 2013</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="5598cf94dea75025bf32e3f0315b7ae9" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":50734122,"asset_id":30267587,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/50734122/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="30267587"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="30267587"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 30267587; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=30267587]").text(description); $(".js-view-count[data-work-id=30267587]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 30267587; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='30267587']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "5598cf94dea75025bf32e3f0315b7ae9" } } $('.js-work-strip[data-work-id=30267587]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":30267587,"title":"Balanced excitation and inhibition in a spiking model of V1","internal_url":"https://www.academia.edu/30267587/Balanced_excitation_and_inhibition_in_a_spiking_model_of_V1","owner_id":57694469,"coauthors_can_edit":true,"owner":{"id":57694469,"first_name":"Micah","middle_initials":null,"last_name":"Richert","page_name":"MicahRichert","domain_name":"independent","created_at":"2016-12-05T19:18:13.684-08:00","display_name":"Micah Richert","url":"https://independent.academia.edu/MicahRichert"},"attachments":[{"id":50734122,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/50734122/thumbnails/1.jpg","file_name":"Balanced_excitation_and_inhibition_in_a_20161205-16383-1ro383g.pdf","download_url":"https://www.academia.edu/attachments/50734122/download_file","bulk_download_file_name":"Balanced_excitation_and_inhibition_in_a.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/50734122/Balanced_excitation_and_inhibition_in_a_20161205-16383-1ro383g-libre.pdf?1480994608=\u0026response-content-disposition=attachment%3B+filename%3DBalanced_excitation_and_inhibition_in_a.pdf\u0026Expires=1740159089\u0026Signature=UK9D6CAjClLOE1-6~aLySnyHWoA8wphHBQG7DvcI3p8jUeqttj708QOgUXhpmPmjUTjYUx~-ODRdxVaUHqZX1CBtGsdPLiSLU6UuDaXf9DwDSLTN-fQaG0FWNZIXQt4g8v-YwWWPvfvhV0xuyQrnOjuCGdHuOkPkk15Qoexuigq8SeHkFSuUlKMK6Ei6L4VzabOFRYBFF8mzgz80SaMrJMrl~ehYey9dT0fUhqCedcNwq8Mqo95P9MKTc48LQB1aX15hWYsj3ZpbpI9qBuP0u26xkKb4Nis1u2UByJ9cbtO5HCMGmO7AEDtp1KPGwbV6wn6bds9Y21q9f4SN4WIDhg__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="30267586"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/30267586/Self_tuning_spike_timing_dependent_plasticity_curves_to_simplify_models_and_improve_learning"><img alt="Research paper thumbnail of Self-tuning spike-timing dependent plasticity curves to simplify models and improve learning" class="work-thumbnail" src="https://attachments.academia-assets.com/50734123/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/30267586/Self_tuning_spike_timing_dependent_plasticity_curves_to_simplify_models_and_improve_learning">Self-tuning spike-timing dependent plasticity curves to simplify models and improve learning</a></div><div class="wp-workCard_item"><span>BMC Neuroscience</span><span>, 2013</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="c54543e774a9078768832b6be0aeb959" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":50734123,"asset_id":30267586,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/50734123/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="30267586"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="30267586"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 30267586; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=30267586]").text(description); $(".js-view-count[data-work-id=30267586]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 30267586; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='30267586']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "c54543e774a9078768832b6be0aeb959" } } $('.js-work-strip[data-work-id=30267586]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":30267586,"title":"Self-tuning spike-timing dependent plasticity curves to simplify models and improve learning","internal_url":"https://www.academia.edu/30267586/Self_tuning_spike_timing_dependent_plasticity_curves_to_simplify_models_and_improve_learning","owner_id":57694469,"coauthors_can_edit":true,"owner":{"id":57694469,"first_name":"Micah","middle_initials":null,"last_name":"Richert","page_name":"MicahRichert","domain_name":"independent","created_at":"2016-12-05T19:18:13.684-08:00","display_name":"Micah Richert","url":"https://independent.academia.edu/MicahRichert"},"attachments":[{"id":50734123,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/50734123/thumbnails/1.jpg","file_name":"Self-tuning_spike-timing_dependent_plast20161205-16378-180ctj5.pdf","download_url":"https://www.academia.edu/attachments/50734123/download_file","bulk_download_file_name":"Self_tuning_spike_timing_dependent_plast.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/50734123/Self-tuning_spike-timing_dependent_plast20161205-16378-180ctj5-libre.pdf?1480994609=\u0026response-content-disposition=attachment%3B+filename%3DSelf_tuning_spike_timing_dependent_plast.pdf\u0026Expires=1740159089\u0026Signature=KlW3J~pvx7gr99jIdQXc2eXMpYiyqTsF03c36QvaJ8X2e-2A1UgN5iISirx6HH1B6g9cBHiSeYcvdtW7w4zJSih2uDEl55nS5Kgu8clNjkENVD2~Ye7jOAAW-OLGx4cjCJLGVpUntW~1wEmlaApvqHbIer9nWuWwbe283hlFVr9iDL2~jPuPOABjwzMnT81UFMMYP8F82YVcuey1ldQGkRGr4IkW3Q4ZzLZj6MMQMSLRJQhbQc4fLvNCysL8zawixOrqfL~ntErjAcn9-WA8wGwJUlpMPd7g8-huNRxc-0L9Xw4L2UZ-I-3NKJKUesTb9EgSVCZVTpG3T-qW-nbp0g__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="30182869"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/30182869/Efficient_Spiking_Neural_Network_Model_of_Pattern_Motion_Selectivity_in_Visual_Cortex"><img alt="Research paper thumbnail of Efficient Spiking Neural Network Model of Pattern Motion Selectivity in Visual Cortex" class="work-thumbnail" src="https://attachments.academia-assets.com/50641757/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/30182869/Efficient_Spiking_Neural_Network_Model_of_Pattern_Motion_Selectivity_in_Visual_Cortex">Efficient Spiking Neural Network Model of Pattern Motion Selectivity in Visual Cortex</a></div><div class="wp-workCard_item wp-workCard--coauthors"><span>by </span><span><a class="" data-click-track="profile-work-strip-authors" href="https://washington.academia.edu/MichaelBeyeler">Michael Beyeler</a> and <a class="" data-click-track="profile-work-strip-authors" href="https://independent.academia.edu/MicahRichert">Micah Richert</a></span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Simulating large-scale models of biological motion perception is challenging, due to the required...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Simulating large-scale models of biological motion perception is challenging, due to the required memory to store the network structure and the computational power needed to quickly solve the neuronal dynamics. A low-cost yet high-performance approach to simulating large-scale neural network models in real-time is to leverage the parallel processing capability of graphics processing units (GPUs). Based on this approach, we present a two-stage model of visual area MT that we believe to be the first large-scale spiking network to demonstrate pattern direction selectivity. In this model, component-direction-selective (CDS) cells in MT linearly combine inputs from V1 cells that have spatiotemporal receptive fields according to the motion energy model of Simoncelli and Heeger. Pattern-direction-selective (PDS) cells in MT are constructed by pooling over MT CDS cells with a wide range of preferred directions. Responses of our model neurons are comparable to electrophysiological results for grating and plaid stimuli as well as speed tuning. The behavioral response of the network in a motion discrimination task is in agreement with psychophysical data. Moreover, our implementation out-performs a previous implementation of the motion energy model by orders of magnitude in terms of computational speed and memory usage. The full network, which comprises 153,216 neurons and approximately 40 million synapses, processes 20 frames per second of a 40脳40 input video in real-time using a single off-the-shelf GPU. To promote the use of this algorithm among neuroscientists and computer vision researchers, the source code for the simulator, the network, and analysis scripts are publicly available.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="e2523ebe87dceeea6b604edea3db8c3b" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":50641757,"asset_id":30182869,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/50641757/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="30182869"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="30182869"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 30182869; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=30182869]").text(description); $(".js-view-count[data-work-id=30182869]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 30182869; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='30182869']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "e2523ebe87dceeea6b604edea3db8c3b" } } $('.js-work-strip[data-work-id=30182869]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":30182869,"title":"Efficient Spiking Neural Network Model of Pattern Motion Selectivity in Visual Cortex","internal_url":"https://www.academia.edu/30182869/Efficient_Spiking_Neural_Network_Model_of_Pattern_Motion_Selectivity_in_Visual_Cortex","owner_id":57466114,"coauthors_can_edit":true,"owner":{"id":57466114,"first_name":"Michael","middle_initials":null,"last_name":"Beyeler","page_name":"MichaelBeyeler","domain_name":"washington","created_at":"2016-11-30T11:41:53.408-08:00","display_name":"Michael Beyeler","url":"https://washington.academia.edu/MichaelBeyeler"},"attachments":[{"id":50641757,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/50641757/thumbnails/1.jpg","file_name":"Beyeler-2014-PatternMotion-Neuroinf.pdf","download_url":"https://www.academia.edu/attachments/50641757/download_file","bulk_download_file_name":"Efficient_Spiking_Neural_Network_Model_o.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/50641757/Beyeler-2014-PatternMotion-Neuroinf-libre.pdf?1480536314=\u0026response-content-disposition=attachment%3B+filename%3DEfficient_Spiking_Neural_Network_Model_o.pdf\u0026Expires=1740048010\u0026Signature=aka3SYhWwru7UvrXmGsqCqng9TIYoaEVU-a~DjSyElyIimGlnEdO18oLogXF3HpTvV0KtT9Pdw06Ct4uMhtlocZ0SIb3T1eH5HpM-YU6kZXdk5g2Grh0i4ouwYgaY1wqqp9lXuexvrdepyQZ6jKUZLArppo5pCsgVIVpQvez7-htS4HtKY-rMO4ZMGrvSVm3erPvpVem-clwkExl9PIfr3Jwj~sGQhKBhtOlAfIyuocdWphl8v2r~O6fnVmVg-glaeoKOqknMjyiuFz7oo9Jx2Os6dHGTylurzkU7vkfyufwr1n7zJhuJ-dCPAdMSiLP8tBBr2DDxNuc0l87kj9AfQ__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> </div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/google_contacts-0dfb882d836b94dbcb4a2d123d6933fc9533eda5be911641f20b4eb428429600.js"], function() { // from javascript_helper.rb $('.js-google-connect-button').click(function(e) { e.preventDefault(); GoogleContacts.authorize_and_show_contacts(); Aedu.Dismissibles.recordClickthrough("WowProfileImportContactsPrompt"); }); $('.js-update-biography-button').click(function(e) { e.preventDefault(); Aedu.Dismissibles.recordClickthrough("UpdateUserBiographyPrompt"); $.ajax({ url: $r.api_v0_profiles_update_about_path({ subdomain_param: 'api', about: "", }), type: 'PUT', success: function(response) { location.reload(); } }); }); $('.js-work-creator-button').click(function (e) { e.preventDefault(); window.location = $r.upload_funnel_document_path({ source: encodeURIComponent(""), }); }); $('.js-video-upload-button').click(function (e) { e.preventDefault(); window.location = $r.upload_funnel_video_path({ source: encodeURIComponent(""), }); }); $('.js-do-this-later-button').click(function() { $(this).closest('.js-profile-nag-panel').remove(); Aedu.Dismissibles.recordDismissal("WowProfileImportContactsPrompt"); }); $('.js-update-biography-do-this-later-button').click(function(){ $(this).closest('.js-profile-nag-panel').remove(); Aedu.Dismissibles.recordDismissal("UpdateUserBiographyPrompt"); }); $('.wow-profile-mentions-upsell--close').click(function(){ $('.wow-profile-mentions-upsell--panel').hide(); Aedu.Dismissibles.recordDismissal("WowProfileMentionsUpsell"); }); $('.wow-profile-mentions-upsell--button').click(function(){ Aedu.Dismissibles.recordClickthrough("WowProfileMentionsUpsell"); }); new WowProfile.SocialRedesignUserWorks({ initialWorksOffset: 20, allWorksOffset: 20, maxSections: 1 }) }); </script> </div></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile_edit-5ea339ee107c863779f560dd7275595239fed73f1a13d279d2b599a28c0ecd33.js","https://a.academia-assets.com/assets/add_coauthor-22174b608f9cb871d03443cafa7feac496fb50d7df2d66a53f5ee3c04ba67f53.js","https://a.academia-assets.com/assets/tab-dcac0130902f0cc2d8cb403714dd47454f11fc6fb0e99ae6a0827b06613abc20.js","https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js"], function() { // from javascript_helper.rb window.ae = window.ae || {}; window.ae.WowProfile = window.ae.WowProfile || {}; if(Aedu.User.current && Aedu.User.current.id === $viewedUser.id) { window.ae.WowProfile.current_user_edit = {}; new WowProfileEdit.EditUploadView({ el: '.js-edit-upload-button-wrapper', model: window.$current_user, }); new AddCoauthor.AddCoauthorsController(); } var userInfoView = new WowProfile.SocialRedesignUserInfo({ recaptcha_key: "6LdxlRMTAAAAADnu_zyLhLg0YF9uACwz78shpjJB" }); WowProfile.router = new WowProfile.Router({ userInfoView: userInfoView }); Backbone.history.start({ pushState: true, root: "/" + $viewedUser.page_name }); new WowProfile.UserWorksNav() }); </script> </div> <div class="bootstrap login"><div class="modal fade login-modal" id="login-modal"><div class="login-modal-dialog modal-dialog"><div class="modal-content"><div class="modal-header"><button class="close close" data-dismiss="modal" type="button"><span aria-hidden="true">×</span><span class="sr-only">Close</span></button><h4 class="modal-title text-center"><strong>Log In</strong></h4></div><div class="modal-body"><div class="row"><div class="col-xs-10 col-xs-offset-1"><button class="btn btn-fb btn-lg btn-block btn-v-center-content" id="login-facebook-oauth-button"><svg style="float: left; width: 19px; line-height: 1em; margin-right: .3em;" aria-hidden="true" focusable="false" data-prefix="fab" data-icon="facebook-square" class="svg-inline--fa fa-facebook-square fa-w-14" role="img" xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><path fill="currentColor" d="M400 32H48A48 48 0 0 0 0 80v352a48 48 0 0 0 48 48h137.25V327.69h-63V256h63v-54.64c0-62.15 37-96.48 93.67-96.48 27.14 0 55.52 4.84 55.52 4.84v61h-31.27c-30.81 0-40.42 19.12-40.42 38.73V256h68.78l-11 71.69h-57.78V480H400a48 48 0 0 0 48-48V80a48 48 0 0 0-48-48z"></path></svg><small><strong>Log in</strong> with <strong>Facebook</strong></small></button><br /><button class="btn btn-google btn-lg btn-block btn-v-center-content" id="login-google-oauth-button"><svg style="float: left; width: 22px; line-height: 1em; margin-right: .3em;" aria-hidden="true" focusable="false" data-prefix="fab" data-icon="google-plus" class="svg-inline--fa fa-google-plus fa-w-16" role="img" xmlns="http://www.w3.org/2000/svg" viewBox="0 0 512 512"><path fill="currentColor" d="M256,8C119.1,8,8,119.1,8,256S119.1,504,256,504,504,392.9,504,256,392.9,8,256,8ZM185.3,380a124,124,0,0,1,0-248c31.3,0,60.1,11,83,32.3l-33.6,32.6c-13.2-12.9-31.3-19.1-49.4-19.1-42.9,0-77.2,35.5-77.2,78.1S142.3,334,185.3,334c32.6,0,64.9-19.1,70.1-53.3H185.3V238.1H302.2a109.2,109.2,0,0,1,1.9,20.7c0,70.8-47.5,121.2-118.8,121.2ZM415.5,273.8v35.5H380V273.8H344.5V238.3H380V202.8h35.5v35.5h35.2v35.5Z"></path></svg><small><strong>Log in</strong> with <strong>Google</strong></small></button><br /><style type="text/css">.sign-in-with-apple-button { width: 100%; height: 52px; border-radius: 3px; border: 1px solid black; cursor: pointer; } .sign-in-with-apple-button > div { margin: 0 auto; / This centers the Apple-rendered button horizontally }</style><script src="https://appleid.cdn-apple.com/appleauth/static/jsapi/appleid/1/en_US/appleid.auth.js" type="text/javascript"></script><div class="sign-in-with-apple-button" data-border="false" data-color="white" id="appleid-signin"><span   ="Sign Up with Apple" class="u-fs11"></span></div><script>AppleID.auth.init({ clientId: 'edu.academia.applesignon', scope: 'name email', redirectURI: 'https://www.academia.edu/sessions', state: "f7c9f9abaabd808ada2ef60fe6449632c89d296e69b032d4684dc8a186f40904", });</script><script>// Hacky way of checking if on fast loswp if (window.loswp == null) { (function() { const Google = window?.Aedu?.Auth?.OauthButton?.Login?.Google; const Facebook = window?.Aedu?.Auth?.OauthButton?.Login?.Facebook; if (Google) { new Google({ el: '#login-google-oauth-button', rememberMeCheckboxId: 'remember_me', track: null }); } if (Facebook) { new Facebook({ el: '#login-facebook-oauth-button', rememberMeCheckboxId: 'remember_me', track: null }); } })(); }</script></div></div></div><div class="modal-body"><div class="row"><div class="col-xs-10 col-xs-offset-1"><div class="hr-heading login-hr-heading"><span class="hr-heading-text">or</span></div></div></div></div><div class="modal-body"><div class="row"><div class="col-xs-10 col-xs-offset-1"><form class="js-login-form" action="https://www.academia.edu/sessions" accept-charset="UTF-8" method="post"><input type="hidden" name="authenticity_token" value="2fkpaw56NmNagjCky8ooUgOzaCnQKkBYlrkXKbog8ad2yheXhxzRTGA9HxsnOr-Qtjv4mXfX1YARW5L-06rw3A" autocomplete="off" /><div class="form-group"><label class="control-label" for="login-modal-email-input" style="font-size: 14px;">Email</label><input class="form-control" id="login-modal-email-input" name="login" type="email" /></div><div class="form-group"><label class="control-label" for="login-modal-password-input" style="font-size: 14px;">Password</label><input class="form-control" id="login-modal-password-input" name="password" type="password" /></div><input type="hidden" name="post_login_redirect_url" id="post_login_redirect_url" value="https://independent.academia.edu/MicahRichert" autocomplete="off" /><div class="checkbox"><label><input type="checkbox" name="remember_me" id="remember_me" value="1" checked="checked" /><small style="font-size: 12px; margin-top: 2px; display: inline-block;">Remember me on this computer</small></label></div><br><input type="submit" name="commit" value="Log In" class="btn btn-primary btn-block btn-lg js-login-submit" data-disable-with="Log In" /></br></form><script>typeof window?.Aedu?.recaptchaManagedForm === 'function' && window.Aedu.recaptchaManagedForm( document.querySelector('.js-login-form'), document.querySelector('.js-login-submit') );</script><small style="font-size: 12px;"><br />or <a data-target="#login-modal-reset-password-container" data-toggle="collapse" href="javascript:void(0)">reset password</a></small><div class="collapse" id="login-modal-reset-password-container"><br /><div class="well margin-0x"><form class="js-password-reset-form" action="https://www.academia.edu/reset_password" accept-charset="UTF-8" method="post"><input type="hidden" name="authenticity_token" value="WZFZFXkdjiJNaeczgI271fuwHaaz4_3WrZpiVV1qOjb2omfp8HtpDXfWyIxsfSwXTjiNFhQeaA4qeOeCNOA7TQ" autocomplete="off" /><p>Enter the email address you signed up with and we'll email you a reset link.</p><div class="form-group"><input class="form-control" name="email" type="email" /></div><script src="https://recaptcha.net/recaptcha/api.js" async defer></script> <script> var invisibleRecaptchaSubmit = function () { var closestForm = function (ele) { var curEle = ele.parentNode; while (curEle.nodeName !== 'FORM' && curEle.nodeName !== 'BODY'){ curEle = curEle.parentNode; } return curEle.nodeName === 'FORM' ? curEle : null }; var eles = document.getElementsByClassName('g-recaptcha'); if (eles.length > 0) { var form = closestForm(eles[0]); if (form) { form.submit(); } } }; </script> <input type="submit" data-sitekey="6Lf3KHUUAAAAACggoMpmGJdQDtiyrjVlvGJ6BbAj" data-callback="invisibleRecaptchaSubmit" class="g-recaptcha btn btn-primary btn-block" value="Email me a link" value=""/> </form></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/collapse-45805421cf446ca5adf7aaa1935b08a3a8d1d9a6cc5d91a62a2a3a00b20b3e6a.js"], function() { // from javascript_helper.rb $("#login-modal-reset-password-container").on("shown.bs.collapse", function() { $(this).find("input[type=email]").focus(); }); }); </script> </div></div></div><div class="modal-footer"><div class="text-center"><small style="font-size: 12px;">Need an account? <a rel="nofollow" href="https://www.academia.edu/signup">Click here to sign up</a></small></div></div></div></div></div></div><script>// If we are on subdomain or non-bootstrapped page, redirect to login page instead of showing modal (function(){ if (typeof $ === 'undefined') return; var host = window.location.hostname; if ((host === $domain || host === "www."+$domain) && (typeof $().modal === 'function')) { $("#nav_log_in").click(function(e) { // Don't follow the link and open the modal e.preventDefault(); $("#login-modal").on('shown.bs.modal', function() { $(this).find("#login-modal-email-input").focus() }).modal('show'); }); } })()</script> <div class="bootstrap" id="footer"><div class="footer-content clearfix text-center padding-top-7x" style="width:100%;"><ul class="footer-links-secondary footer-links-wide list-inline margin-bottom-1x"><li><a href="https://www.academia.edu/about">About</a></li><li><a href="https://www.academia.edu/press">Press</a></li><li><a href="https://www.academia.edu/documents">Papers</a></li><li><a href="https://www.academia.edu/topics">Topics</a></li><li><a href="https://www.academia.edu/journals">Academia.edu Journals</a></li><li><a rel="nofollow" href="https://www.academia.edu/hiring"><svg style="width: 13px; height: 13px;" aria-hidden="true" focusable="false" data-prefix="fas" data-icon="briefcase" class="svg-inline--fa fa-briefcase fa-w-16" role="img" xmlns="http://www.w3.org/2000/svg" viewBox="0 0 512 512"><path fill="currentColor" d="M320 336c0 8.84-7.16 16-16 16h-96c-8.84 0-16-7.16-16-16v-48H0v144c0 25.6 22.4 48 48 48h416c25.6 0 48-22.4 48-48V288H320v48zm144-208h-80V80c0-25.6-22.4-48-48-48H176c-25.6 0-48 22.4-48 48v48H48c-25.6 0-48 22.4-48 48v80h512v-80c0-25.6-22.4-48-48-48zm-144 0H192V96h128v32z"></path></svg> <strong>We're Hiring!</strong></a></li><li><a rel="nofollow" href="https://support.academia.edu/hc/en-us"><svg style="width: 12px; height: 12px;" aria-hidden="true" focusable="false" data-prefix="fas" data-icon="question-circle" class="svg-inline--fa fa-question-circle fa-w-16" role="img" xmlns="http://www.w3.org/2000/svg" viewBox="0 0 512 512"><path fill="currentColor" d="M504 256c0 136.997-111.043 248-248 248S8 392.997 8 256C8 119.083 119.043 8 256 8s248 111.083 248 248zM262.655 90c-54.497 0-89.255 22.957-116.549 63.758-3.536 5.286-2.353 12.415 2.715 16.258l34.699 26.31c5.205 3.947 12.621 3.008 16.665-2.122 17.864-22.658 30.113-35.797 57.303-35.797 20.429 0 45.698 13.148 45.698 32.958 0 14.976-12.363 22.667-32.534 33.976C247.128 238.528 216 254.941 216 296v4c0 6.627 5.373 12 12 12h56c6.627 0 12-5.373 12-12v-1.333c0-28.462 83.186-29.647 83.186-106.667 0-58.002-60.165-102-116.531-102zM256 338c-25.365 0-46 20.635-46 46 0 25.364 20.635 46 46 46s46-20.636 46-46c0-25.365-20.635-46-46-46z"></path></svg> <strong>Help Center</strong></a></li></ul><ul class="footer-links-tertiary list-inline margin-bottom-1x"><li class="small">Find new research papers in:</li><li class="small"><a href="https://www.academia.edu/Documents/in/Physics">Physics</a></li><li class="small"><a href="https://www.academia.edu/Documents/in/Chemistry">Chemistry</a></li><li class="small"><a href="https://www.academia.edu/Documents/in/Biology">Biology</a></li><li class="small"><a href="https://www.academia.edu/Documents/in/Health_Sciences">Health Sciences</a></li><li class="small"><a href="https://www.academia.edu/Documents/in/Ecology">Ecology</a></li><li class="small"><a href="https://www.academia.edu/Documents/in/Earth_Sciences">Earth Sciences</a></li><li class="small"><a href="https://www.academia.edu/Documents/in/Cognitive_Science">Cognitive Science</a></li><li class="small"><a href="https://www.academia.edu/Documents/in/Mathematics">Mathematics</a></li><li class="small"><a href="https://www.academia.edu/Documents/in/Computer_Science">Computer Science</a></li></ul></div></div><div class="DesignSystem" id="credit" style="width:100%;"><ul class="u-pl0x footer-links-legal list-inline"><li><a rel="nofollow" href="https://www.academia.edu/terms">Terms</a></li><li><a rel="nofollow" href="https://www.academia.edu/privacy">Privacy</a></li><li><a rel="nofollow" href="https://www.academia.edu/copyright">Copyright</a></li><li>Academia ©2025</li></ul></div><script> //<![CDATA[ window.detect_gmtoffset = true; window.Academia && window.Academia.set_gmtoffset && Academia.set_gmtoffset('/gmtoffset'); //]]> </script> <div id='overlay_background'></div> <div id='bootstrap-modal-container' class='bootstrap'></div> <div id='ds-modal-container' class='bootstrap DesignSystem'></div> <div id='full-screen-modal'></div> </div> </body> </html>