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Journal Article (229)

  1. 221.
    Journal Article
    Dayan, P.: Computational Modelling. Current Opinion in Neurobiology 4 (2), pp. 212 - 217 (1994)
  2. 222.
    Journal Article
    Berns, G.; Dayan, P.; Sejnowski, T.: A correlational model for the development of disparity selectivity in visual cortex that depends on prenatal and postnatal phases. Proceedings of the National Academy of Sciences of the United States of America 90 (17), pp. 8277 - 8281 (1993)
  3. 223.
    Journal Article
    Dayan, P.: Improving Generalization for Temporal Difference Learning: The Successor Representation. Neural computation 5 (4), pp. 613 - 624 (1993)
  4. 224.
    Journal Article
    Dayan, P.: Arbitrary Elastic Topologies and Ocular Dominance. Neural computation 5 (3), pp. 392 - 401 (1993)
  5. 225.
    Journal Article
    Dayan, P.; Sejnowski, T.: The Variance of Covariance Rules for Associative Matrix Memories and Reinforcement Learning. Neural computation 5 (2), pp. 205 - 209 (1993)
  6. 226.
    Journal Article
    Dayan, P.: The Convergence of TD(λ) for General λ. Machine Learning 8 (3-4), pp. 341 - 362 (1992)
  7. 227.
    Journal Article
    Watkins, C.; Dayan, P.: Q-learning. Machine Learning 8 (3-4), pp. 279 - 292 (1992)
  8. 228.
    Journal Article
    Dayan, P.; Willshaw, D.: Optimising synaptic learning rules in linear associative memories. Biological Cybernetics 65 (4), pp. 253 - 265 (1991)
  9. 229.
    Journal Article
    Willshaw, D.; Dayan, P.: Optimal Plasticity from Matrix Memories: What Goes Up Must Come Down. Neural computation 2 (1), pp. 85 - 93 (1990)

Book (1)

  1. 230.
    Book
    Dayan, P.; Abbott, L.: Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems. MIT Press, Cambridge, MA, USA (2001), 460 pp.

Book Chapter (3)

  1. 231.
    Book Chapter
    Dayan, P.: Exploration from Generalization Mediated by Multiple Controllers. In: Intrinsically Motivated Learning in Natural and Artificial Systems, pp. 73 - 91 (Eds. Baldassarre, G.; Mirolli, M.). Springer, Berlin, Germany (2013)
  2. 232.
    Book Chapter
    Dayan, P.: Models of Value and Choice. In: Neuroscience of Preference and Choice: Cognitive and Neural Mechanisms, pp. 33 - 52 (Eds. Dolan, R.; Sharot, T.). Elsevier/Academic Press, London, UK (2012)
  3. 233.
    Book Chapter
    Platt, M.; Dayan, P.; Dehaene, S.; McCabe, H.; Menzel, R.; Phelps, E.; Plassmann, H.; Ratcliff, R.; Shadlen, M.; Singer, W.: Neuronal Correlates of Decision Making. In: Better than conscious?: decision making, the human mind, and implications for institutions (Eds. Engel, C.; Singer, W.). MIT Press, Cambridge, MA, USA (2008)

Proceedings (1)

  1. 234.
    Proceedings
    Exploration and Curiosity in Robot Learning and Inference (Dagstuhl Reports, 1). Dagstuhl Seminar 11131, Dagstuhl, Germany, March 27, 2011 - April 01, 2011. (2011)

Conference Paper (67)

  1. 235.
    Conference Paper
    Dezfouli , A.; Ashtiani , H.; Ghattas, O.; Nock, R.; Dayan, P.; Ong, C.: Disentangled behavioural representations. In: Advances in Neural Information Processing Systems 32 (Eds. Wallach, H.; Larochelle, H.; Beygelzimer , A.; d'Alché-Buc, F.; Fox, E. et al.). Thirty-third Conference on Neural Information Processing Systems (NeurIPS 2019), Vancouver, Canada, December 09, 2019 - December 13, 2019. (2019)
  2. 236.
    Conference Paper
    Jain, Y.; Gupta, S.; Rakesh, V.; Dayan, P.; Callaway, F.; Lieder, F.: How do people learn how to plan? In: Conference on Cognitive Computational Neuroscience (CCN 2019). Conference on Cognitive Computational Neuroscience (CCN 2019), Berlin, Germany, September 13, 2019 - September 16, 2019. (2019)
  3. 237.
    Conference Paper
    Browning, M.; Carter, C.; Chatham, C.; Den Ouden, H.; Gillan, C.; Baker, J.; Chekroud, A.; Cools, R.; Dayan, P.; Gold, J. et al.; Goldstein, R.; Hartley, C.; Kepecs, A.; Lawson, R.; Mourao-Miranda, J.; Phillips, M.; Pizzagalli, D.; Powers, A.; Rindskopf, D.; Roiser, J.; Schmack, K.; Schiller, D.; Sebold, M.; Stephan, K.; Frank, M.; Huys, Q.; Paulus, M.: Realizing the Clinical Potential of Computational Psychiatry: Report from the Banbury Center Meeting, February 2019. In: Banbury Center Meeting. Banbury Center Meeting, Huntington, NY, USA, 2019-02. (2019)
  4. 238.
    Conference Paper
    Dezfouli, A.; Morris, R.; Ramos, F.; Dayan, P.; Balleine, B.: Integrated accounts of behavioral and neuroimaging data using flexible recurrent neural network models. In: Advances in Neural Information Processing Systems 31, pp. 4233 - 4242 (Eds. Bengio, S.; Wallach, H.; Larochelle, H.; Grauman, K.; Cesa-Bianchi, N. et al.). 32nd Annual Conference on Neural Information Processing Systems (NeurIPS 2018), Montréal, Canada, December 03, 2018 - December 08, 2018. Curran, Red Hook, NY, USA (2019)
  5. 239.
    Conference Paper
    Ahilan, S.; Dayan, P.: Feudal Multi-Agent Hierarchies for Cooperative Reinforcement Learning. In: Annual Conference of the American Library Association (ALA 2019). Annual Conference of the American Library Association (ALA 2019) , Washington, DC, USA, June 20, 2019 - June 25, 2019. (2019)
  6. 240.
    Conference Paper
    Ahilan, S.; Dayan, P.: Feudal Multi-Agent Hierarchies for Cooperative Reinforcement Learning. In: Workshop on Structure & Priors in Reinforcement Learning (SPiRL 2019) at ICLR 2019. Workshop on Structure & Priors in Reinforcement Learning (SPiRL 2019) at ICLR 2019, New Orleans, LA, USA, May 06, 2019. (2019)
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