Peter Dayan

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Peter Dayan

Peter Dayan (* 1965 ) is a British cognitive and neuroscientist who has made significant contributions to computational neuroscience .

Dayan studied mathematics at Cambridge and received his PhD in computer science (artificial intelligence) with David Willshaw at the University of Edinburgh . As a post-doctoral student he worked with Terry Sejnowski at the Salk Institute and at the University of Toronto. He was Assistant Professor at the Massachusetts Institute of Technology and from 1998 Professor at University College London , where he has been Director of the Gatsby Computational Neuroscience Unit since 2002 . In September 2018 he was appointed director at the Max Planck Institute for Biological Cybernetics .

Dayan deals with machine learning ( reinforcement learning , use of Bayesian methods) and especially the creation of mathematical models for neural learning processes and neural information processing on this basis. He uses models of reinforcement learning in which the reward is simulated internally so that learning is also possible without external reward. He identified the neurotransmitter dopamine as a reinforcing element . In 1997 he and colleagues applied Temporal Difference Learning to the analysis of neural networks with dopamine as a transmitter in monkeys.

He sees the neural networks in the brain as similar to Bayesian networks. Dayan proposed a new interpretation of the function of the cerebellum: it repeats activity patterns of movements overnight so that these are not forgotten by the brain.

Peter Dayan was awarded the Rumelhart Prize in 2012 for contributions to the theoretical foundations of human cognition and in 2017 the Brain Prize of the Grete Lundbeck European Brain Research Foundation. In 2018 he was appointed a Fellow of the Royal Society of the United Kingdom and has been a Fellow of the American Association for the Advancement of Science (AAAS) since 2019 . He also received an Alexander von Humboldt Professorship , the most highly endowed research award in Germany, and a chair in the computer science department at the University of Tübingen .

Fonts (selection)

  • with Laurence F. Abbott: Theoretical neuroscience: computational and mathematical modeling of neural systems. MIT Press 2001

Essays:

  • The convergence of TD (λ) for general λ, Machine Learning ', Volume 8, 1992, pp. 341-362.
  • Christopher Watkins: Q-learning, Machine Learning, Volume 8, 1992, pp. 279-292.
  • with GE Hinton , B. Frey, RM Neal: The wake-sleep algorithm for unsupervised neural networks. Science, Vol. 268, 1995, pp. 1158-1160.
  • with PR Montague, T. Sejnowski: A framework for mesencephalic dopamine systems based on predictive Hebbian learning. Journal of Neuroscience, Volume 16, 1996, pp. 1936-1947.
  • with W. Schultz, PR Montague: A neural substrate of prediction and reward. Science, Vol. 275, 1997, pp. 1593-1599.
  • with S. Kakade, R. Montague: Learning and selective attention, Nature Neuroscience, Volume 3, 2000, pp. 1218-1223.
  • with ND Daw, S. Kakade: Opponent interactions between serotonin and dopamine. Neural Networks, Vol. 15, 2002, pp. 603-616.
  • with BW Balleine: Reward, motivation and reinforcement learning. Neuron, Volume 36, 2002, pp. 285-298.
  • with S. Kali: Off-line replay maintains declarative memories in a model of hippocampal-neocortical interactions. Nature Neuroscience, Volume 7, 2004, pp. 286-294.
  • with ND Daw, Y. Niv: Uncertainty-based competition between prefrontal and dorsolateral striatal systems for behavioral control, Nature Neuroscience, Volume 8, 2005, pp. 1704-1711.
  • with AJ Yu: Uncertainty, neuromodulation, and attention. Neuron, Volume 46, 2005, pp. 481-492.
  • with Y. Niv, BJ Seymour, ND Daw: The misbehavior of value and the discipline of the will. Neural Networks, Volume 19, 2006, pp. 1153-1160.
  • with Y. Niv, ND Daw, D. Joel: Tonic dopamine: Opportunity costs and the control of response vigor. Psychopharmacology, Volume 191, 2007, pp. 507-520.
  • with Q. Huys: Serotonin, inhibition and negative mood. Public Library of Science: Computational Biology, Volume 4, 2008, e4.
  • with Q. Huys: A Bayesian formulation of behavioral control. Cognition, Volume 113, 2009, pp. 314-328.
  • with O. Schwartz, TJ Sejnowski: Perceptual organization in the tilt illusion. Journal of Vision, Volume 9, 2009, pp. 1-20.
  • with JA Solomon: Selective Bayes: Attentional load and crowding. Vision Research, Volume 50, 2010, pp. 2248-2260.

Web links

Individual evidence

  1. ↑ New Humboldt Professor fits perfectly into Tübingen's research landscape , press release University of Tübingen, June 6, 2019
  2. ↑ https://www.mpg.de/12299335/berendung-dayan-li
personal data
SURNAME Dayan, Peter
BRIEF DESCRIPTION British computer scientist, neuroscientist and cognitive scientist
DATE OF BIRTH 1965