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Peter Dayan
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I build mathematical and computational models of neural processing, with a particular emphasis on representation and learning. The main focus is on reinforcement learning and unsupervised learning, covering the ways that animals come to choose appropriate actions in the face of rewards and punishments, and the ways and goals of the process by which they come to form neural representations of the world. The models are informed and constrained by neurobiological, psychological and ethological data.
Papers132 papers
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PLoS computational biology, no. 1 (2021)
FRONTIERS IN PSYCHOLOGY, (2020): 1644
eLife, (2020)
Michael Browning,Cameron S. Carter, Christopher Chatham,Hanneke Den Ouden, Claire M. Gillan,Justin T. Baker, Adam M. Chekroud,Roshan Cools,Peter Dayan,James Gold, Rita Z. Goldstein, Catherine A. Hartley
Biological Psychiatry, no. 2 (2020): e5-e10
eLife, (2020)
Thomas Akam, Ines Rodrigues-Vaz, Ivo Marcelo, Xiangyu Zhang, Michael Pereira, Rodrigo Freire Oliveira,Peter Dayan,Rui M Costa
Neuron, (2020)
Proceedings of the National Academy of Sciences of the United States of America, no. 46 (2020): 29221-29228
NIPS 2020, (2020)
EIBibtex
SCIENCE ADVANCES, no. 25 (2020)
Scientific reports, no. 1 (2020): 11839
ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 32 (NIPS 2019), (2019): 1-18
PLOS COMPUTATIONAL BIOLOGY, no. 10 (2019)
David B. Kastner, Eric A. Miller, Zhounan Yang, Demetris K. Roumis,Daniel F. Liu,Loren M. Frank,Peter Dayan
Neuron, pp.1-26, (2019)
NATURE COMMUNICATIONS, no. 1 (2019): 1141
NATURE COMMUNICATIONS, no. 1 (2019): 4030-16
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