BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//BHI - ECPv6.15.10//NONSGML v1.0//EN
CALSCALE:GREGORIAN
METHOD:PUBLISH
X-ORIGINAL-URL:https://brainhealthinstitute.rutgers.edu
X-WR-CALDESC:Events for BHI
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:20260824T120000
DTEND;TZID=America/New_York:20260824T130000
DTSTAMP:20260825T071807
CREATED:20260818T055252Z
LAST-MODIFIED:20260818T055252Z
UID:10278-1787572800-1787576400@brainhealthinstitute.rutgers.edu
SUMMARY:Neuroscience Seminar Series for Rising Scholars (Hybrid): Ahmad Beyh\, PhD (PI: Linden Parkes\, PhD)
DESCRIPTION:“Geometric constraints and cognitive inputs jointly shape emergent brain dynamics and topology”\nSpeaker: Ahmad Beyh\, PhD\nPostdoctoral Fellow\nPI: Linden Parkes\, PhD \nDate and Time: Monday\, August 24\, 2026\, 12:00 PM \nIn-Person: Room 127\, Staged Research Building (SRB)\, Busch Campus \nJoin via Zoom: Please email the host\, Dr. Noelle Stiles (noelle.stiles@rutgers.edu)\, or check the CAHBIR Slack #General thread for the link \nAbstract: How the brain’s physical geometry gives rise to its flexible functional repertoire remains a central question in neuroscience. Here\, we trained three classes of recurrent neural networks (RNNs) on a working-memory task\, forming a graded hierarchy of spatial constraints: Vanilla RNNs (no spatial constraints)\, Masked RNNs (projection constraints limiting where information enters and leaves the network)\, and biophysical RNNs (bioRNNs; projection constraints and spatial embedding of the networks’ connectivity using the brain’s inter-regional Euclidean geometry). We assessed how well each RNN class predicted empirical fMRI activity without exposing them to it during training. Our results showed that bioRNNs were the only networks to successfully predict empirical brain activity and to organize their dynamics into a spatial pattern that recapitulated the brain’s principal hierarchy (the sensorimotor–association axis). Additionally\, bioRNNs’ ability to predict empirical brain activity emerged along a trajectory in which geometry was laid down first\, then partly traded back as the task was mastered. Importantly\, brain-like topological features emerged in bioRNNs as they increased their task proficiency while maintaining their ability to predict brain activity. Taken together\, our results indicate that physical geometry and cognitive inputs play distinct\, complementary roles: while geometry constrains the space of possible brain dynamics\, cognitive inputs determine which dynamics are expressed. They also situate topology as the scaffold through which the physically embedded brain reconciles wiring costs and computational demands.
URL:https://brainhealthinstitute.rutgers.edu/event/neuroscience-seminar-series-for-rising-scholars-hybrid-ahmad-beyh-phd-pi-linden-parkes-phd/
ATTACH;FMTTYPE=image/png:https://brainhealthinstitute.rutgers.edu/wp-content/uploads/2026/08/Neuroscience-Seminar-Series-for-Rising-Scholars_Ahmad-Beyh_0824.png
END:VEVENT
END:VCALENDAR