Peter Dayan

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Peter Dayan [1]

Peter Dayan,
a British mathematician, computer scientist and neuroscientist, and director at the Max Planck Institute for Biological Cybernetics in Tübingen, Germany, since early 2019 also affiliated with the SMARTStart training program of the Bernstein Network Computational Neuroscience [2] [3]. From 1998 until 2018, he was professor of computational neuroscience at University College London, and director of UCL's Gatsby Computational Neuroscience Unit [4].

Peter Dayan obtained a B.Sc. in mathematics from University of Cambridge and a Ph.D. in artificial intelligence from University of Edinburgh under David Wallace, which focused on Bayesian network and neural network models of machine learning [5]. He was postdoctoral researcher at the Salk Institute for Biological Studies working with Terrence J. Sejnowski, and at the University of Toronto with Geoffrey E. Hinton, and was further assistant professor at MIT before relocating to UCL.


Peter Dayan's work has been influential in several fields impinging on cognitive science, including machine learning, mathematical statistics, neuroscience and psychology - he has articulated a view in which neural computation is akin to a Bayesian inference process [6]. His research centers around self-supervised learning, reinforcement learning, temporal difference learning, population coding and Monte-Carlo tree search. He researched and published on Q-learning with Chris Watkins [7], and provided a proof of convergence of TD(λ) for arbitrary λ [8].

Learning Go

Along with Nicol N. Schraudolph and Terrence J. Sejnowski, Peter Dayan worked and published on temporal difference learning to evaluate positions in Go [9] [10].

Selected Publications


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External Links


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