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TZID:Europe/Vienna
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DTSTART:20230326T030000
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DTSTART:20231029T020000
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BEGIN:VEVENT
DTSTAMP:20260424T153021Z
UID:6267c1da514e7111556072@ist.ac.at
DTSTART:20230627T160000
DTEND:20230627T170000
DESCRIPTION:Speaker: Friedemann Zenke\nhosted by Tim Vogels\nAbstract: Disc
 riminating distinct objects and concepts from sensory stimuli is essential
  for survival. Our brains perform this processing in deep sensory networks
  shaped through plasticity. However\, our understanding of the underlying 
 plasticity mechanisms remains rudimentary. I will introduce Latent Predict
 ive Learning (LPL)\, a plasticity model that prescribes a local learning r
 ule that combines Hebbian elements with predictive plasticity. I will show
  that deep neural networks equipped with LPL develop disentangled object r
 epresentations without supervision. The same rule accurately captures neur
 onal selectivity changes observed in the primate inferotemporal cortex in 
 response to altered visual experience. Finally\, our model generalizes to 
 spiking neural networks and naturally accounts for several experimentally 
 observed properties of synaptic plasticity\, including metaplasticity and 
 spike-timing-dependent plasticity (STDP). LPL thus constitutes a plausible
  normative theory of representation learning in the brain while making con
 crete testable predictions.
LOCATION:Central Bldg / O1 / Mondi 2 (I01.O1.008)\, ISTA
ORGANIZER:mmosiash@ist.ac.at
SUMMARY:Friedemann Zenke: Principles of predictive representation learning 
 in biological neural networks
URL:https://talks-calendar.ista.ac.at/events/4275
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