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SUMMARY:Computational Neuroscience Journal Club - Yan Wu (University of Ca
 mbridge)
DTSTART:20140506T150000Z
DTEND:20140506T160000Z
UID:TALK52488@talks.cam.ac.uk
CONTACT:Guillaume Hennequin
DESCRIPTION:Yan Wu will cover:\n\nPopulation code in mouse V1 facilitates 
 readout of natural scenes through increased sparseness\n\nEmmanouil Frouda
 rakis\, Philipp Berens\, Alexander S Ecker\, R James Cotton\, Fabian H Sin
 z\, Dimitri Yatsenko\, Peter Saggau\, Matthias Bethge & Andreas S Tolias\n
 Nature\, 2014\n\nhttp://www.nature.com/neuro/journal/vaop/ncurrent/full/nn
 .3707.html\n\nABSTRACT:\n\nNeural codes are believed to have adapted to th
 e statistical properties of the natural environment. However\, the princip
 les that govern the organization of ensemble activity in the visual cortex
  during natural visual input are unknown. We recorded populations of up\nt
 o 500 neurons in the mouse primary visual cortex and characterized the str
 ucture of their activity\, comparing responses to natural movies with thos
 e to control stimuli. We found that higher order correlations in natural s
 cenes induced a sparser code\, in which information is encoded by reliable
  activation of a smaller set of neurons and can be\nread out more easily. 
 This computationally advantageous encoding for natural scenes was state-de
 pendent and apparent only in anesthetized and active awake animals\, but n
 ot during quiet wakefulness. Our results argue for a functional benefit of
  sparsification that could be a general principle governing the structure 
 of the population activity throughout cortical microcircuits.\n
LOCATION:Cambridge University Engineering Department\, CBL Rm #438 (http:/
 /learning.eng.cam.ac.uk/Public/Directions)
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