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SUMMARY:Stimulus complexity shapes response correlations in primary visual
  cortex - Gergő Orbán (HAS Wigner Research Centre for Physics)
DTSTART:20190227T110000Z
DTEND:20190227T120000Z
UID:TALK120694@talks.cam.ac.uk
CONTACT:Rodrigo Echeveste
DESCRIPTION:Abstract: Spike count correlations (SCCs) are ubiquitous in se
 nsory cortices\, are characterized by rich structure\, and arise from stru
 ctured internal dynamics. However\, most theories of visual perception tre
 at contributions of neurons to the representation of stimuli independently
  and focus on mean responses. Here\, we argue that\, in a functional model
  of visual perception\, featuring probabilistic inference over a hierarchy
  of features\, inferences about high-level features modulate inferences ab
 out low-level features ultimately introducing structured internal dynamics
  and patterns in SCCs. Specifically\, high-level inferences for complex st
 imuli establish the local context in which neurons in the primary visual c
 ortex (V1) interpret stimuli. Since the local context differentially affec
 ts multiple neurons\, this conjecture predicts specific modulations in the
  fine structure of SCCs as stimulus identity and\, more importantly\, stim
 ulus complexity varies. We designed experiments with natural and synthetic
  stimuli to measure the fine structure of SCCs in V1 of awake behaving mac
 aques and assessed their dependence on stimulus identity and stimulus stat
 istics. We show that the fine structure of SCCs is specific to the identit
 y of natural stimuli and changes in SCCs are independent of changes in res
 ponse mean. Critically\, we demonstrate that stimulus specificity of SCCs 
 in V1 can be directly manipulated by altering the amount of high-order str
 ucture in synthetic stimuli. Finally\, we show that simple phenomenologica
 l models of V1 activity cannot account for the observed SCC patterns and c
 onclude that the stimulus dependence of SCCs is a natural consequence of s
 tructured internal dynamics in a hierarchical probabilistic model of natur
 al images.\n\nhttps://www.pnas.org/content/116/7/2723
LOCATION:Cambridge University Engineering Department\, CBL\, BE4-38 (http:
 //learning.eng.cam.ac.uk/Public/Directions)
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