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DTSTART:20250330T030000
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BEGIN:VEVENT
DTSTAMP:20260426T035223Z
UID:687e3eaa97b01471336835@ist.ac.at
DTSTART:20251002T140000
DTEND:20251002T150000
DESCRIPTION:Speaker: Adam Gosztolai\nhosted by Maximilian Josch\nAbstract: 
 It is increasingly recognised that the computations in the brain can be un
 derstood based on the theory of dynamical systems conformed by the activit
 y of large neural populations. Moreover\, several works have observed that
  dominant dynamical patterns of computation are highly preserved across an
 imals performing similar tasks. In my talk\, I will argue that these prese
 rved dynamical patterns manifest from the existence of invariancesconserve
 d quantities and symmetries in population dynamics. I will then describe o
 ur efforts to mathematically formalise and computationally capture these i
 nvariances from the geometric activity of neural populations. Specifically
 \, in the first part of my talk I will talk vector field descriptions of n
 eural dynamics\, highlighting MARBLE\, a geometric deep learning method th
 at allows finding consistent latent representations across neural recordin
 gs. Then\, in the second part\, I will highlight current work to formulate
  a data-driven and predictive model for learning invariances.
LOCATION:Mondi Seminar Room 2\, Central Building\, ISTA
ORGANIZER:aburlica@ist.ac.at
SUMMARY:Adam Gosztolai: Modelling the geometry and invariance of neural pop
 ulation dynamics
URL:https://talks-calendar.ista.ac.at/events/6050
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