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SUMMARY:A theory for Hebbian Learning in recurrent E-I networks   - Samuel
  Eckmann (Max Planck Institute for Brain Research\, Frankfurt am Main\, Ge
 rmany)
DTSTART:20201218T090000Z
DTEND:20201218T100000Z
UID:TALK154789@talks.cam.ac.uk
CONTACT:Yul Kang
DESCRIPTION:The Stabilized Supralinear Network is a model of recurrently c
 onnected excitatory (E) and inhibitory (I) neurons that can explain many c
 ortical phenomena such as response normalization and inhibitory stabilizat
 ion. However\, the network’s connectivity is designed by hand\, based on
  experimental measurements. How the connectivity can be learned from the s
 ensory input statistics in a biologically plausible way is unknown. Here w
 e present a recurrent E-I network model where all synaptic connections are
  simultaneously plastic. We employ local Hebbian plasticity rules and deve
 lop a theoretical framework that explains how neurons’ receptive fields 
 decorrelate and become self-stabilized by recruiting co-tuned inhibition. 
 As in the Stabilized Supralinear Network\, the circuit’s response is nor
 malized – the response to a combined stimulus is equal to a weighted sum
  of the individual stimulus responses.  \n\nIn summary\, we introduce a bi
 ologically plausible theoretical framework to model plasticity in fully pl
 astic recurrent E-I networks. While the connectivity is derived from the s
 ensory input statistics\, the circuit performs meaningful computations. Ou
 r work provides a mathematical framework of plasticity in recurrent networ
 ks\, which has previously only been studied numerically and can serve as t
 he basis for a new generation of brain-inspired unsupervised machine learn
 ing algorithms.\n\n\nJoin Zoom Meeting (will be recorded)\n\nhttps://us02w
 eb.zoom.us/j/83406384764?pwd=bEdNVUUyN280VGVURk9HUVB6RGtpUT09\n\nMeeting I
 D: 834 0638 4764\nPasscode: 061452
LOCATION:Online on Zoom (recorded)
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