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SUMMARY:Sensory processing in neocortical networks: randomness\, specifici
 ty and learning - Sadra Sadeh\, Imperial College\, London\, UK
DTSTART:20141014T083000Z
DTEND:20141014T093000Z
UID:TALK55343@talks.cam.ac.uk
CONTACT:Guillaume Hennequin
DESCRIPTION:Recurrent neuronal networks of excitatory and inhibitory neuro
 ns are deemed to contribute significantly to sensory processing. Yet\, the
  exact nature of this contribution and the mechanisms underlying it remain
  elusive. By studying links between neuronal dynamics and functional prope
 rties of recurrent networks in network models\, we aim to elucidate the me
 chanisms responsible for some fundamental computations in primary sensory 
 cortices. Starting with randomly connected networks lacking any feature-sp
 ecific structure\, e.g. corresponding to the visual cortex of rodents at e
 ye opening\, we analyze the input-output transfer function of the network 
 in response to tuned input. Once the initial selectivity is established in
  the network\, accounting for synaptic plasticity of excitatory and inhibi
 tory connections can in fact lead to the emergence of feature-specific con
 nectivity. This is consistent with experimental reports on the sequence of
  events during the emergence of orientation selectivity and specific conne
 ctivity. Analyzing the neuronal dynamics of the networks before and after 
 synaptic refinement reveals further functional benefits of synaptic specif
 icity\, including the emergence of reverberating activity and pattern comp
 letion. Random and specific networks may\, therefore\, be responsible for 
 different aspects of sensory processing at different developmental stages\
 , as the computational analysis of neuronal dynamics and synaptic plastici
 ty in large-scale recurrent networks reveals.
LOCATION:Cambridge University Engineering Department\, CBL\, BE-438 (http:
 //learning.eng.cam.ac.uk/Public/Directions)
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