|Alma mater||University of Cambridge (BA)|
University of Edinburgh (PhD)
|Awards||Rumelhart Prize (2012)|
The Brain Prize (2017)
|Institutions||Max Planck Institute for Biological Cybernetics|
University College London
Massachusetts Institute of Technology
University of Toronto
|Thesis||Reinforcing connectionism : learning the statistical way (1991)|
|Doctoral advisor||David Willshaw|
Peter Dayan Max Planck Institute for Biological Cybernetics in Tübingen, Germany. He is co-author of Theoretical Neuroscience, an influential textbook on computational neuroscience. He is known for applying bayesian methods from machine learning and artificial intelligence to understand neural function and is particularly recognized for relating neurotransmitter levels to prediction errors and Bayesian uncertainties. He has pioneered the field of reinforcement learning (RL) where he helped develop the Q-learning algorithm, and made contributions to unsupervised learning, including the wake-sleep algorithm for neural networks and the Helmholtz machine.is director at the
Dayan studied mathematics at the University of Cambridge and then continued for a PhD in artificial intelligence at the University of Edinburgh School of Informatics on statistical learning  supervised by David Willshaw and David Wallace, focusing on associative memory and reinforcement learning.
Career and research
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 (MIT), and moved to the Gatsby Charitable Foundation computational neuroscience unit at University College London (UCL) in 1998, becoming professor and director in 2002. In September 2018, the Max Planck Society announced his appointment as a director at the Max Planck Institute for Biological Cybernetics in Tübingen.
Awards and honours
- Ghahramani, Zoubin (2017). "Welcoming Peter Dayan to Uber AI Labs". uber.com. Archived from the original on 15 March 2018.
- Shead, Sam (2018). "Elon Musk Signed A 350-Year-Old Book With DeepMind's Demis Hassabis". forbes.com. Retrieved 9 February 2019.
- Kumaran, Dharshan; Banino, Andrea; Blundell, Charles; Hassabis, Demis; Dayan, Peter (2016). "Computations Underlying Social Hierarchy Learning: Distinct Neural Mechanisms for Updating and Representing Self-Relevant Information". Neuron. 92 (5): 1135–1147. doi:10.1016/j.neuron.2016.10.052. ISSN 0896-6273. PMC 5158095. PMID 27930904.
- Dayan, Peter; Abbott, Laurence (2014). Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems. Cambridge: MIT Press. ISBN 9780262541855. OCLC 952504127.
- Schultz, W.; Dayan, P.; Montague, P. R. (1997). "A Neural Substrate of Prediction and Reward" (PDF). Science. 275 (5306): 1593–1599. doi:10.1126/science.275.5306.1593. ISSN 0036-8075. PMID 9054347.
- Watkins, Christopher J. C. H.; Dayan, Peter (1992). "Q-learning". Machine Learning. 8 (3–4): 279–292. doi:10.1007/BF00992698. ISSN 0885-6125.
- Dayan, Peter (1992). "The convergence of TD (λ) for general λ". Machine Learning. 8 (3/4): 341–362. doi:10.1023/A:1022632907294. ISSN 0885-6125.
- Peter, Dayan; Hinton, Geoffrey E.; Neal, Radford M.; Zemel, Richard S. (1995). "The helmholtz machine". Neural Computation. 7 (5): 889–904. doi:10.1162/neco.19220.127.116.119. hdl:21.11116/0000-0002-D6D3-E. PMID 7584891.
- Dayan, Peter Samuel (1991). Reinforcing connectionism: learning the statistical way (PhD thesis). hdl:1842/14754. EThOS uk.bl.ethos.649240.
- "Peter Dayan". gatsby.ucl.ac.uk. Archived from the original on 25 March 2019.
- Anon (2018). "Peter Dayan and Li Zhaoping appointed to the Max Planck Institute for Biological Cybernetics". mpg.de. Archived from the original on 3 April 2019. Retrieved 2 October 2018.
- Anon (2018). "Professor Peter Dayan FRS". royalsociety.org. London: Royal Society. Retrieved 22 May 2018. One or more of the preceding sentences incorporates text from the royalsociety.org 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)