BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//ARNI - ECPv6.16.3//NONSGML v1.0//EN
CALSCALE:GREGORIAN
METHOD:PUBLISH
X-WR-CALNAME:ARNI
X-ORIGINAL-URL:https://arni-institute.org
X-WR-CALDESC:Events for ARNI
REFRESH-INTERVAL;VALUE=DURATION:PT1H
X-Robots-Tag:noindex
X-PUBLISHED-TTL:PT1H
BEGIN:VTIMEZONE
TZID:America/New_York
BEGIN:DAYLIGHT
TZOFFSETFROM:-0500
TZOFFSETTO:-0400
TZNAME:EDT
DTSTART:20250309T070000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0400
TZOFFSETTO:-0500
TZNAME:EST
DTSTART:20251102T060000
END:STANDARD
BEGIN:DAYLIGHT
TZOFFSETFROM:-0500
TZOFFSETTO:-0400
TZNAME:EDT
DTSTART:20260308T070000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0400
TZOFFSETTO:-0500
TZNAME:EST
DTSTART:20261101T060000
END:STANDARD
BEGIN:DAYLIGHT
TZOFFSETFROM:-0500
TZOFFSETTO:-0400
TZNAME:EDT
DTSTART:20270314T070000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0400
TZOFFSETTO:-0500
TZNAME:EST
DTSTART:20271107T060000
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20260213T113000
DTEND;TZID=America/New_York:20260213T130000
DTSTAMP:20260619T035546
CREATED:20260211T195441Z
LAST-MODIFIED:20260211T201644Z
UID:2351-1770982200-1770987600@arni-institute.org
SUMMARY:CTN: SueYeon Chung
DESCRIPTION:SueYeon Chung \nTitle: Computing with Neural Manifolds: A Multi-Scale Framework for Understanding Biological and Artificial Neural Networks \nAbstract: Recent breakthroughs in experimental neuroscience and machine learning have opened new frontiers in understanding the computational principles governing neural circuits and artificial neural networks (ANNs). Both biological and artificial systems exhibit an astonishing degree of orchestrated information processing capabilities across multiple scales – from the microscopic responses of individual neurons to the emergent macroscopic phenomena of cognition and task functions. At the mesoscopic scale\, the structures of neuron population activities manifest themselves as neural representations. Neural computation can be viewed as a series of transformations of these representations through various processing stages of the brain. The primary focus of my lab’s research is to develop theories of neural representations that describe the principles of neural coding and\, importantly\, capture the complex structure of real data from both biological and artificial systems. \nIn this talk\, I will present three related approaches that leverage techniques from statistical physics\, machine learning\, and geometry to study the multi-scale nature of neural computation. First\, I will introduce new theories based on statistical physics and convex geometry that connect complex geometric structures that arise from neural responses (i.e.\, neural manifolds) to the efficiency of neural representations in implementing a task. Second\, I will employ these theories to analyze how these representations evolve across scales\, shaped by the properties of single neurons\, learning dynamics\, and the transformations across distinct brain regions. Finally\, I will show how these insights extend efficient coding principles beyond early sensory stages\, linking representational geometry to efficient task implementations. This framework not only help interpret and compare models of brain data but also offers a principled approach to designing ANN models for higher-level vision. This perspective opens new opportunities for using neuroscience-inspired principles to guide the development of intelligent systems. \n 
URL:https://arni-institute.org/event/ctn-sueyeon-chung/
LOCATION:Zuckerman Institute – L5-084\, 3227 Broadway\, New York\, NY\, United States
END:VEVENT
END:VCALENDAR