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Peter Samuel Dayan FRS is the director of the Gatsby Charitable Foundation Computational Neuroscience Unit at University College London.[2] He is co-author of Theoretical Neuroscience, a textbook on Computational neuroscience.[citation needed] 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.[3] He co-authored a paper on Q-learning with Chris Watkins,[4][clarification needed] and provided a proof of convergence of TD(λ) for arbitrary λ.[5]

Peter Dayan
Born Peter Samuel Dayan
1965 (age 52–53)
Alma mater University of Cambridge (BA)
University of Edinburgh (PhD)
Awards Rumelhart Prize (2012)
The Brain Prize (2017)
Scientific career
Fields Computational neuroscience
Institutions University College London
Massachusetts Institute of Technology
University of Toronto
Salk Institute
Thesis Reinforcing connectionism : learning the statistical way (1991)
Doctoral advisor David Willshaw
Influences Geoffrey Hinton
Terry Sejnowski



Dayan studied mathematics at the University of Cambridge and then continued for a PhD in artificial intelligence at the University of Edinburgh on statistical learning [6]with David Willshaw and David Wallace, focusing on associative memory and reinforcement learning.[6]

Career and researchEdit

After his PhD, Dayan held postdoctoral research positions with Terry Sejnowski at the Salk Institute and Geoffrey Hinton at the University of Toronto. He then took up an assistant professor position at the Massachusetts Institute of Technology, and moved to the Gatsby Computational Neuroscience Unit at University College London in 1998, becoming professor and director in 2002. He stepped down as director in 2017. .

Awards and honoursEdit

Dayan was elected a Fellow of the Royal Society (FRS) in 2018.[7] He was awarded the Rumelhart Prize in 2012 and The Brain Prize in 2017.[7]


  1. ^ Ghahramani, Zoubin (2017). "Welcoming Peter Dayan to Uber AI Labs". 
  2. ^ "Peter Dayan". 
  3. ^ Schultz, W., Dayan, P., & Montague, P. R. (1997). A neural substrate of prediction and reward. Science, 275 (5306), 1593–1599 doi:10.1126/science.275.5306.1593  
  4. ^ Watkins, Christopher JCH, and Peter Dayan. "Q-learning". Machine learning 8, no. 3–4 (1992): 279–292. doi:10.1007/BF00992698
  5. ^ Dayan, Peter. "The convergence of TD (λ) for general λ". Machine learning 8, no. 3–4 (1992): 341–362 doi:10.1023/A:1022632907294
  6. ^ a b Dayan, Peter Samuel (1991). Reinforcing connectionism: learning the statistical way. (PhD thesis). hdl:1842/14754. EThOS   
  7. ^ a b Anon (2018). "Professor Peter Dayan FRS". London: Royal Society. Retrieved 22 May 2018.  One or more of the preceding sentences incorporates text from the website where:

    “All text published under the heading 'Biography' on Fellow profile pages is available under Creative Commons Attribution 4.0 International License.” --Royal Society Terms, conditions and policies at the Wayback Machine (archived 2016-11-11)

  This article incorporates text available under the CC BY 4.0 license.