Name Peter Dayan Role Author | ||
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Books Theoretical neuroscience, Music Writing Literature, Art as Music - Music as, Nerval et ses peres, Mallarme's "divine transposition" | ||
Iit madras ccbr prof peter dayan neural reinforcement learning
Peter Dayan is the director of the Gatsby Computational Neuroscience Unit at University College London. He is co-author of Theoretical Neuroscience, a textbook in computational and mathematical modeling of brain function. He is known for applying Bayesian methods from machine learning and artificial intelligence to understand neural function, and is particularly recognized for having related neurotransmitter levels to prediction errors and Bayesian uncertainties. He co-authored Q-learning with Chris Watkins, and provided a proof of convergence of TD(λ) for arbitrary λ. His h-index according to Google Scholar is 76.
Contents
- Iit madras ccbr prof peter dayan neural reinforcement learning
- Peter dayan interactions between model free and model based reinforcement learning
- References
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, focusing 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.