Peter Dayan is the director of the Gatsby Computational Neuroscience Unit at University College London. He is co-author of "Theoretical Neuroscience", a leading textbook in computational and mathematical modeling of brain function (see Computational Neuroscience). He is known for applying Bayesian methods from Machine Learning and Artificial Intelligence to understand neural function, and is particularly renown for having related neurotransmitter levels to prediction errors and Bayesian uncertainties. He also co-authored "Q-learning" with Chris Watkins, and provided a proof of convergence for TD(λ) for arbitrary λ (see temporal difference learning) . His h-index according to Google Scholar is 64.
He began his career studying Mathematics at the University of Cambridge (UK) and then continued for a PhD in Artificial Intelligence at the University of Edinburgh with David Willshaw, which focused on associative memory and reinforcement learning. He then went on to do a Postdoc with Terry Sejnowski at the Salk Institute. He then took up an Assistant Professor position at the Massachusetts Institute of Technology, and later moved to University College London where he became Professor and Director of the Gatsby Computational Neuroscience Unit.
- Schultz, W., Dayan, P., & Montague, P. R. (1997). A neural substrate of prediction and reward. Science, 275(5306), 1593-1599.
- Dayan, Peter. "The convergence of TD (λ) for general λ." Machine learning 8, no. 3-4 (1992): 341-362.
- Watkins, Christopher JCH, and Peter Dayan. "Q-learning." Machine learning 8, no. 3-4 (1992): 279-292.
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