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SUMMARY:How a recurrent circuit of Poisson spiking neurons is able to impl
 ement sampling-based inference - Wen-Hao Zhang
DTSTART:20220517T140000Z
DTEND:20220517T150000Z
UID:TALK171575@talks.cam.ac.uk
CONTACT:Jake Stroud
DESCRIPTION:Zoom information:\nhttps://us02web.zoom.us/j/84958321096?pwd=d
 FpsYnpJYWVNeHlJbEFKbW1OTzFiQT09 Meeting ID: 849 5832 1096 Passcode: 506576
 \n\nTwo facts about cortex are widely accepted: neuronal responses show la
 rge spiking variability with near Poisson statistics and cortical circuits
  feature abundant recurrent connections between neurons. How these spiking
  and circuit properties combine to support sensory representation and info
 rmation processing is not well understood. We build a theoretical framewor
 k showing that these two ubiquitous features of cortex combine to produce 
 optimal sampling-based Bayesian inference. Recurrent connections store an 
 internal model of the external world\, and Poissonian variability of spike
  responses drives flexible sampling from the posterior stimulus distributi
 ons obtained by combining feedforward and recurrent neuronal inputs. We il
 lustrate how this framework for sampling-based inference can be used by co
 rtex to represent latent multivariate stimuli organized either hierarchica
 lly or in parallel. A neural signature of such network sampling are intern
 ally generated differential correlations whose amplitude is determined by 
 the prior stored in the circuit\, which provides an experimentally testabl
 e prediction for our framework.
LOCATION:Online on Zoom
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