BEGIN:VCALENDAR
PRODID:-//planitpurple.northwestern.edu//iCalendar Event//EN
VERSION:2.0
CALSCALE:GREGORIAN
METHOD:PUBLISH
CLASS:PUBLIC
BEGIN:VTIMEZONE
TZID:America/Chicago
TZURL:http://tzurl.org/zoneinfo-outlook/America/Chicago
X-LIC-LOCATION:America/Chicago
BEGIN:DAYLIGHT
TZOFFSETFROM:-0600
TZOFFSETTO:-0500
TZNAME:CDT
DTSTART:19700308T020000
RRULE:FREQ=YEARLY;BYMONTH=3;BYDAY=2SU
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0500
TZOFFSETTO:-0600
TZNAME:CST
DTSTART:19701101T020000
RRULE:FREQ=YEARLY;BYMONTH=11;BYDAY=1SU
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
SEQUENCE:0
DTSTART;TZID=America/Chicago:20260522T110000
DTEND;TZID=America/Chicago:20260522T121500
DTSTAMP:20260608T143254Z
SUMMARY:SEMINAR: Gated Recurrent Neural Networks as a Framework to Study Neuromodulation: improving memory capacity and generalization of dynamical tasks
UID:642553@northwestern.edu
TZID:America/Chicago
DESCRIPTION:Please join Hector Manuel Lopez Rios\, Postdoctoral Fellow\, for an upcoming seminar.   Gated Recurrent Neural Networks as a Framework to Study Neuromodulation: improving memory capacity and generalization of dynamical tasks  Recurrent neural networks (RNNs) have become a standard framework for modeling how neural circuits learn and process information\, yet most models assume that computation is mediated entirely through synaptic connectivity (short-range communication). However\, neurons also communicate extra-synaptically via chemical signaling (e.g.\, neuropeptides\, monoamines). These extra-synaptic interactions are essential for transitions between behavioral states\, but their computational role has received limited analytical treatment within the associative memory and RNN frameworks. In this talk\, I will introduce a minimal model that treats neuromodulation as dynamical tuning of neuronal integration timescales. We augment a time-continuous Hopfield network with a neuromodulatory gating layer inspired by the neuropeptidergic connectome of C. elegans. Using numerical simulations and dynamical mean-field theory\, we show that neuromodulation increases the memory capacity of the network beyond the classical limit and enables generalization to unseen conditions in timing tasks (measure-wait-go). Our results suggest that neuromodulatory signaling is an active computational resource that expands the dynamical repertoire of neural circuits.   Hector Manuel Lopez Rios is a postdoctoral fellow at the University of Chicago working with Suri Vaikuntanathan on the dynamics and information processing of biological and chemical networks. He earned his Ph.D. at Northwestern University\, advised by Monica Olvera de la Cruz. Hector holds a bachelor's degree in chemical engineering from UNAM in Mexico City\, Mexico.
LOCATION:Technological Institute\, A230\, 2145 Sheridan Road\, Evanston\, IL 60208
TRANSP:OPAQUE
URL:https://planitpurple.northwestern.edu/event/642553
CREATED:20260518T050000Z
STATUS:CONFIRMED
LAST-MODIFIED:20260518T184011Z
PRIORITY:0
BEGIN:VALARM
TRIGGER:-PT10M
ACTION:DISPLAY
DESCRIPTION:Reminder
END:VALARM
END:VEVENT
END:VCALENDAR