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SUMMARY:A distributed\, hierarchical and recurrent model of choice - Laure
 nce Hunt\, University College London
DTSTART:20160208T141500Z
DTEND:20160208T151500Z
UID:TALK63990@talks.cam.ac.uk
CONTACT:Paula Kaanders
DESCRIPTION:Traditional approaches to understanding neural mechanisms of e
 conomic choice treat it as a serial and localized process. Such models div
 ide choices into discrete evaluation\, comparison\, and selection stages\,
  and seek to link these stages with corresponding neuroanatomical loci. Ho
 wever\, several recently observed features of single neuron and macroscopi
 c data are difficult to reconcile with this divide-and-conquer approach. I
 n this talk\, I will argue that these data can more readily be reconciled 
 with a model in which value-guided decisions are implemented in a distribu
 ted fashion across many brain regions performing canonical computations in
  concert. This account draws on recent ideas in deep learning to emphasize
  the importance of recurrent over feedforward architectures in solving rea
 l-world sequential decision problems. It suggests that value may be an eme
 rgent phenomenon\, not represented explicitly in any particular part of th
 e brain.
LOCATION:Kenneth Craik Room\, Craik Marshall Building\, Downing Site\, Cam
 bridge
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