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SUMMARY:Computational Neuroscience Journal Club - Dylan Festa (University 
 of Cambridge)
DTSTART:20140520T150000Z
DTEND:20140520T160000Z
UID:TALK52729@talks.cam.ac.uk
CONTACT:Guillaume Hennequin
DESCRIPTION:Dylan Festa will cover:\n\nNoise in neural populations account
 s for errors in working memory\n\nby P. M. Bays\, J Neurosci (2014)\n\nABS
 TRACT:\n\nErrors in short-term memory increase with the quantity of inform
 ation stored\, limiting the complexity of cognition and behavior. In visua
 l memory\, attempts to account for errors in terms of allocation of a limi
 ted pool of working memory resources have met with some success\, but the 
 biological basis for this cognitive architecture is unclear. An alternativ
 e perspective attributes recall errors to noise in tuned populations of ne
 urons that encode stimulus features in spiking activity. I show that error
 s associated with decreasing signal strength in probabilistically spiking 
 neurons reproduce the pattern of failures in human recall under increasing
  memory load. In particular\, deviations from the normal distribution that
  are characteristic of working memory errors and have been attributed prev
 iously to guesses or variability in precision are shown to arise as a natu
 ral consequence of decoding populations of tuned neurons. Observers posses
 s fine control over memory representations and prioritize accurate storage
  of behaviorally relevant information\, at a cost to lower priority stimul
 i. I show that changing the input drive to neurons encoding a prioritized 
 stimulus biases population activity in a manner that reproduces this empir
 ical tradeoff in memory precision. In a task in which predictive cues indi
 cate stimuli most probable for test\, human observers use the cues in an o
 ptimal manner to maximize performance\, within the constraints imposed by 
 neural noise.\n
LOCATION:Cambridge University Engineering Department\, CBL Rm #438 (http:/
 /learning.eng.cam.ac.uk/Public/Directions)
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