{"id":44522,"date":"2023-01-17T14:52:34","date_gmt":"2023-01-17T19:52:34","guid":{"rendered":"https:\/\/engineering.jhu.edu\/ams\/?post_type=tribe_events&#038;p=44522"},"modified":"2023-03-29T15:06:13","modified_gmt":"2023-03-29T19:06:13","slug":"ams-weekly-seminar-professor-carina-curto","status":"publish","type":"tribe_events","link":"https:\/\/engineering.jhu.edu\/ams\/event\/ams-weekly-seminar-professor-carina-curto\/","title":{"rendered":"AMS Weekly Seminar | Professor Carina Curto"},"content":{"rendered":"<div>\n<p><strong><span>Location:\u00a0<\/span><\/strong><span>Gilman 132<\/span><\/p>\n<\/div>\n<div>\n<p><strong><span>When: <\/span><\/strong>March 30th\u00a0at 1:30 p.m.<span><\/span><\/p>\n<\/div>\n<div>\n<p><strong><span>Title: <\/span><\/strong>An introduction to threshold-linear networks for neuroscience<\/p>\n<\/div>\n<div>\n<p><strong><span>Abstract:\u00a0<\/span><\/strong><span>Threshold-linear networks (TLNs) are popular models for modeling neural activity in the brain. They are simple, recurrently-connected networks with a rich repertoire of nonlinear dynamics, including multistability, limit cycles, quasiperiodic attractors, and chaos. Over the past few years, we have developed a mathematical theory relating stable and unstable fixed points of TLNs to graph-theoretic properties of the underlying network. The resulting &#8220;graph rules&#8221; and &#8220;gluing rules&#8221; provide a direct connection between network architecture and key features of the dynamics. In this talk, I will provide an introduction to the theory of TLNs via a mix of theorems and examples. In a selection of applications, I will show how the theory enables us to design networks that perform various neural computations, such as counting stimulus pulses, position tracking, and transitioning between various locomotive gaits.<\/span><\/p>\n<p><strong>Zoom link: <\/strong>https:\/\/wse.zoom.us\/j\/95738965246<\/p>\n<\/div>\n","protected":false},"excerpt":{"rendered":"<p>Location:\u00a0Gilman 132 When: March 30th\u00a0at 1:30 p.m. Title: An introduction to threshold-linear networks for neuroscience Abstract:\u00a0Threshold-linear networks (TLNs) are popular models for modeling neural activity in the brain. They are&hellip;<\/p>\n","protected":false},"author":69,"featured_media":0,"template":"","meta":{"_acf_changed":false,"_relevanssi_hide_post":"","_relevanssi_hide_content":"","_relevanssi_pin_for_all":"","_relevanssi_pin_keywords":"","_relevanssi_unpin_keywords":"","_relevanssi_related_keywords":"","_relevanssi_related_include_ids":"","_relevanssi_related_exclude_ids":"","_relevanssi_related_no_append":"","_relevanssi_related_not_related":"","_relevanssi_related_posts":"","_relevanssi_noindex_reason":"","_tribe_events_status":"","_tribe_events_status_reason":"","footnotes":""},"tags":[],"tribe_events_cat":[260],"class_list":["post-44522","tribe_events","type-tribe_events","status-publish","hentry","tribe_events_cat-seminars-and-endowed-lectures","cat_seminars-and-endowed-lectures"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.7 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>AMS Weekly Seminar | Professor Carina Curto | Department of Applied Mathematics and Statistics<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/engineering.jhu.edu\/ams\/event\/ams-weekly-seminar-professor-carina-curto\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"AMS Weekly Seminar | Professor Carina Curto | Department of Applied Mathematics and Statistics\" \/>\n<meta property=\"og:description\" content=\"Location:\u00a0Gilman 132 When: March 30th\u00a0at 1:30 p.m. Title: An introduction to threshold-linear networks for neuroscience Abstract:\u00a0Threshold-linear networks (TLNs) are popular models for modeling neural activity in the brain. 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