BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//Computer Science and Engineering - ECPv6.13.0//NONSGML v1.0//EN
CALSCALE:GREGORIAN
METHOD:PUBLISH
X-ORIGINAL-URL:https://homecse.iitd.ac.in
X-WR-CALDESC:Events for Computer Science and Engineering
REFRESH-INTERVAL;VALUE=DURATION:PT1H
X-Robots-Tag:noindex
X-PUBLISHED-TTL:PT1H
BEGIN:VTIMEZONE
TZID:Asia/Kolkata
BEGIN:STANDARD
TZOFFSETFROM:+0530
TZOFFSETTO:+0530
TZNAME:IST
DTSTART:20260101T000000
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTART;TZID=Asia/Kolkata:20260306T120000
DTEND;TZID=Asia/Kolkata:20260306T130000
DTSTAMP:20260922T124517
CREATED:20260303T103646Z
LAST-MODIFIED:20260303T103646Z
UID:2432-1772798400-1772802000@homecse.iitd.ac.in
SUMMARY:Neural Circuit Discovery via Representation and Dynamics by Savik Kinger
DESCRIPTION:Abstract: Neuroscience and AI share a bottleneck: while one can build (artificial) or record (biological) complex networks\, we struggle to explain their functional circuitry; i.e.\, how they compute. In this talk I use whole-brain recordings from C. elegans\, a canonical neurobiological system\, as a concrete testbed for “circuit interpretability.” I then introduce two complementary inference approaches for turning high-dimensional activity data into mechanistic structure. Approach 1 treats circuit discovery as a representation problem: learn time-varying functional structure and uncover recurring\, stimulus-dependent modules rather than a single static connectivity map. Approach 2 treats circuit discovery as a dynamics problem: go beyond correlation to estimate directed\, time-lagged influence—i.e.\, which units appear to drive others and over what delays—using modern score-based generative modeling ideas. I will show how these ML methods produce testable hypotheses for biologists and\, potentially\, offer new avenues for understanding complex networks in AI. \nBio: Savik Kinger is a PhD candidate in Computer Science at Yale University\, advised by Steven Zucker. His research focuses on developing methods to analyze biological and artificial neural networks\, integrating ideas from machine learning\, dynamical systems\, and causal inference. He received Bachelor’s degrees in Math and Computer Science from Columbia University. He is supported by a Nathan Hale fellowship.
URL:https://homecse.iitd.ac.in/event/neural-circuit-discovery-via-representation-and-dynamics-by-savik-kinger/
LOCATION:SIT 113\, Amar Nath and Shashi Khosla School of Information Technology\, Indian Institute of Technology\, Delhi\, Hauz Khas\, New Delhi\, Delhi\, 110016\, India
CATEGORIES:Seminars
END:VEVENT
END:VCALENDAR