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Neural circuits trained with standard reinforcement learning can accumulate probabilistic information during decision making

Abstract:

Much experimental evidence suggests that during decision making neural circuits accumulate evidence supporting alternative options. A computational model well describing this accumulation for choices between two options assumes that the brain integrates the log ratios of the likelihoods of the sensory inputs given the two options. Several models have been proposed for how neural circuits can learn these log-likelihood ratios from experience, but all these models introduced novel and specially...

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Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.1162/NECO_a_00917

Authors


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Institution:
University of Oxford
Division:
MSD
Department:
Clinical Neurosciences
Role:
Author
More by this author
Institution:
University of Oxford
Division:
MSD
Department:
Experimental Psychology
Role:
Author
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Funding agency for:
Bogacz, R
Grant:
MC UU 12024/5
Publisher:
Massachusetts Institute of Technology Press Publisher's website
Journal:
Neural Computation Journal website
Volume:
29
Issue:
2
Pages:
368-393
Publication date:
2016-10-01
Acceptance date:
2016-09-09
DOI:
ISSN:
0899-7667
Source identifiers:
652826
Keywords:
Pubs id:
pubs:652826
UUID:
uuid:3beb56f8-fefe-4fd3-96b9-394316fbb2e9
Local pid:
pubs:652826
Deposit date:
2016-10-18

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