2002

• Dayan, P (2002).
  Matters temporal.
  Trends in Cognitive Sciences, 5, 105-106.
  

2001

• Daw, ND, Kakade, S & Dayan, P (2001).
  Opponent interactions between serotonin and dopamine.
  Submitted to Neural Networks.
  

• Dayan, P (2001a).
  Levels of analysis in neural modeling.
  Encyclopedia of Cognitive Science. London, England: MacMillan Press.
  

• Dayan, P (2001b).
  Motivated reinforcement learning.
  NIPS 2001, to appear.
  

• Dayan, P & Yu, A (2001).
  ACh, uncertainty, and cortical inference.
  NIPS 2001, to appear.
  

• Kakade, S & Dayan, P (2001).
  Dopamine bonuses.
  Submitted to Neural Networks.
  

• Yu, A & Dayan, P (2001).
  Acetylcholine and cortical inference.
  Submitted to Neural Networks.
  

2000

• Dayan, P (2000a).
  Helmholtz machines and wake-sleep learning.
  Submitted to M Arbib, editor, Handbook of Brain Theory and Neural Networks, 2. Cambridge, MA: MIT Press.
  

• Dayan, P (2000c).
  Competition and arbors in ocular dominance.
  In NIPS 2000, 203-209.

• Dayan, P & Kakade, S (2000).
  Explaining away in weight space.
  In NIPS 2000, 451-457.

• Dayan, P, Kakade, S & Montague, PR (2000).
  Learning and selective attention.
  Nature Neuroscience, 3 , 1218-1223.
  

• Foster, DJ & Dayan, P (2000).
  Using unsupervised learning methods to extract structure in value functions.
  Machine Learning, in press.
  

• Foster, DJ, Morris, RGM & Dayan, P (2000).
  Models of hippocampally dependent navigation using the temporal difference learning rule.
  Hippocampus, 10, 1-16.
  

• Kakade, S & Dayan, P (2000a).
  Acquisition and extinction in autoshaping.
  Psychological Review, 109, 533-544.
  

• Kakade, S & Dayan, P (2000b).
  Dopamine bonuses.
  In NIPS 2000, 131-137.
  

• Kakade, S & Dayan, P (2000c).
  Acquisition in autoshaping.
  In NIPS 12, 24-30.

• Káli, S & Dayan, P (2000a).
  Hippocampally-dependent consolidation in a hierarchical model of neocortex.
  In NIPS 2000, 24-30.

• Káli, S & Dayan, P (2000b).
  The involvement of recurrent connections in area CA3 in establishing the properties of place fields: A model.
  Journal of Neuroscience, 20, 7463-7477.
  

• Káli, S & Dayan, P (2000c).
  A familiarity-based learning procedure for the establishment of place fields in area CA3 of the rat hippocampus.
  In CNS 2000.
  

• Li, Z & Dayan, P (2000).
  Position variance, recurrence and perceptual learning.
  NIPS 2000, to appear.

• Pouget, A, Dayan, P & Zemel, RS (2000).
  Information coding and representation with population codes..
  Nature Reviews Neuroscience, 1 , 125-132.
  See also Zemel & Dayan (1999).

• Schraudolph, NN, Dayan, P & Sejnowski, TJ (2000).
  Learning to evaluate Go positions via temporal difference methods.
  In LC Jain & N Baba, editors, Soft Computing Techniques in Game Playing. Berlin, Germany: Springer-Verlag.

1999

• Abbott, LF & Dayan, P (1999).
  The effect of correlated variability on the accuracy of a population code.
  Neural Computation, 11, 91-101.

• Dayan, P (1999a).
  Recurrent sampling models for the Helmholtz machine.
  Neural Computation, 11, 653-677.
  

• Dayan, P (1999b).
  Unsupervised learning.
  In Wilson, RA & Keil, F, editors. The MIT Encyclopedia of the Cognitive Sciences.
  

• Dayan, P & Zemel, RS (1999).
  Statistical models and sensory attention.
  In ICANN, 1999, 1017-1022.
  

• Káli, S & Dayan, P (1999).
  Spatial representations in related environments in a recurrent model of area CA3 of the rat.
  In ICANN 1999 , 138-143.
  

• Li, Z & Dayan, P (1999a).
  Computational differences between asymmetrical and symmetrical networks.
  Network, 10, 59-77.
  

• Li, Z & Dayan, P (1999b).
  Computational differences between asymmetrical and symmetrical networks.
  In NIPS 11.
  

• Lin, JK & Dayan, P (1999).
  Curved gaussian models with application to modeling currency exchange rates.
  In YS Abu-Mostafa, B LeBaron, AW Lo & AS Weigend, editors, Computational Finance 99. Cambridge, MA: MIT Press.

• Zemel, RS & Dayan, P (1999).
  Distributional population codes and multiple motion models.,
  In NIPS 11.
  

1998

• Dayan, P (1998a).
  A hierarchical model of visual rivalry.
  Neural Computation, 10, 1119-1136.
  

• Dayan, P (1998b).
  Recurrent sampling models. In KM Wong, I King & D-Y Yeung, editors,
  Theoretical Aspects of Neural Computation: A Multidisciplinary Perspective, 287-298. Berlin: Springer.
  

• Dayan, P & Long, T (1998).
  Statistical models of conditioning.
  In NIPS 10, 117-123.
  

• Foster, DJ, Morris, RGM & Dayan, P (1998).
  Hippocampal model of rat spatial abilities using temporal difference learning.
  In NIPS 10, 145-151.
  

• Káli, S & Dayan, P (1998).
  Formation of circularly symmetric place fields in the rodent hippocampus.
  Society for Neuroscience Abstracts, 28.
  

• Montague, PR & Dayan, P (1998).
  Neurobiological modeling: Squeezing top down to meet bottom up.
  In W Bechtel & G Graham, editors, A Companion to Cognitive Science.\ Oxford: Basil Blackwell, 526-542.
  

• Singh, SP & Dayan, P (1998).
  Analytical mean squared error curves in Temporal Difference learning.
  Machine Learning, 32, 5-40.
  

• Sommer, FT & Dayan, P (1998).
  Bayesian retrieval in associative memories with storage errors.
  IEEE Transactions in Neural Networks, 9, 705-713.

• Zemel, RS & Dayan, P (1998).
  Moving targets: Interpreting population responses in area MT.
  Society for Neuroscience Abstracts, 28.
  

• Zemel, RS, Dayan, P & Pouget A (1998).
  Probabilistic interpretation of population codes.
  Neural Computation, 10, 403-430.
  

1997

• Dayan, P (1997a).
  A hierarchical model of visual rivalry.
  In NIPS 9, 48-54.
  

• Dayan, P (1997b).
  Recognition in hierarchical models.
  In F Cucker & M Shub, editors, Foundations of Computational Mathematics. Berlin, Germany: Springer.
  

• Dayan, P & Hinton, GE (1997).
  Using EM for reinforcement learning.
  Neural Computation, 9, 271-278.

• Foster, DJ, Morris, RGM & Dayan, P (1997).
  Hippocampal model of rat spatial abilities using temporal difference learning.
  Society for Neuroscience Abstracts, 27, 624.3.
  

• Frey, BJ, Dayan, P & Hinton, GE (1997).
  A simple algorithm that discovers efficient perceptual codes.
  In M Jenkin & L Harris, editors, Computation and Psychophysical Mechanisms of Visual Coding, 296-315. Cambridge: CUP.
  

• Hinton, GE, Dayan, P & Revow, M (1997).
  Modeling the manifolds of images of handwritten digits.
  IEEE Transactions on Neural Networks, 8, 65-74.
  

• Neal, RM & Dayan, P (1997).
  Factor Analysis using delta-rule wake-sleep learning.
  Neural Computation, 9, 1781-1803.

• Reisenhuber, M & Dayan, P (1997).
  Neural models for part-whole hierarchies.
  In NIPS 9, 661-667.
  

• Schultz, W, Dayan, P & Montague, PR (1997).
  A neural substrate of prediction and reward.
  Science, 275, 1593-1599.
  

• Singh, SP & Dayan, P (1997).
  Analytical mean squared error curves in temporal difference learning.
  In NIPS 9, 1054-1060.
  

• Zemel, RS, Dayan, P & Pouget, A (1997).
  Probabilistic interpretation of population codes.
  In NIPS 9, 676-682.
  

• Zemel, RS & Dayan, P (1997).
  Combining probabilistic population codes.
  In IJCAI 15, 1114-1119.
  

1996

• Dayan, P & Hinton, GE (1996).
  Varieties of Helmholtz machine.
  Neural Networks, 9, 1385-1403.

• Dayan, P & Sejnowski, TJ (1996).
  Exploration bonuses and dual control.
  Machine Learning, 25, 5-22.

• Dayan, P & Singh, SP (1996a).
  Improving policies without measuring merits.
  In NIPS 8, 1059-1065.

• Dayan, P & Singh, SP (1996b).
  Long term potentiation, navigation and dynamic programming.
  In CNS 96.

• Frey, BJ, Hinton, GE & Dayan, P (1996).
  Does the wake-sleep algorithm produce good density estimators?
  In NIPS 8, 661-667.
  

• Montague, PR, Dayan, P & Sejnowski, TK (1996).
  A framework for mesencephalic dopamine systems based on predictive Hebbian learning.
  Journal of Neuroscience, 16, 1936-1947.
  

• Oberlander, J & Dayan, P (1996).
  Altered states and virtual beliefs.
  In A Clark & PJR Millican, editors, Connectionism, Concepts and Folk Psychology: The Legacy of Alan Turing. Oxford, UK: Clarendon.

1995

• Dayan, P, Hinton, GE, Neal, RM & Zemel, RS (1995).
  The Helmholtz machine.
  Neural Computation, 7, 889-904.
  

• Dayan, P & Zemel, RS (1995).
  Competition and multiple cause models.
  Neural Computation, 7, 565-579.

• Hinton, GE, Dayan, P, Frey, BJ & Neal, RM (1995).
  The wake-sleep algorithm for unsupervised neural networks.
  Science, 268, 1158-1160.
  

• Hinton, GE, Dayan, P, To, A & Neal, R (1995).
  The Helmholtz machine through time.
  In F Fogelman-Soulie & R Gallinari, editors, ICANN-95,, 483-490.
  

• Hinton, GE, Revow, M & Dayan, P (1995).
  Recognizing handwritten digits using mixtures of linear models.
  In NIPS 7, 1015-1022.
  

• Montague, PR, Dayan, P, Person, C & Sejnowski, TJ (1995).
  Bee foraging in uncertain environments using predictive Hebbian learning.
  Nature, 377, 725-728.
  

• Sejnowski, TJ, Dayan, P & Montague, PR (1995).
  Predictive Hebbian learning.
  Invited talk to COLT 8, 15-18.
  

1994

• Dayan, P (1994).
  Computational modelling.
  Current Opinion in Neurobiology, 4, 212-217.
  

• Dayan, P & Sejnowski, TJ (1994).
  TD() converges with probability 1.
  Machine Learning, 14, 295-301.

• Hinton, GE, Dayan, P, Neal, RM & Zemel, RS (1994).
  Using neural networks to learn intractable generative models.
  Invited paper at the 1994 meeting of the American Statistical Association, Toronto, Canada.

• Montague, PR, Dayan, P & Sejnowski, TJ (1994).
  Foraging in an uncertain environment using predictive Hebbian learning.
  In NIPS 6, 598-605.
  

• Schraudolph, N, Dayan, P & Sejnowski, TJ (1994).
  Temporal difference learning of position evaluation in the game of Go.
  In NIPS 6, 817-824.
  

1993

• Berns, GS, Dayan, P & Sejnowski, TJ (1993).
  A correlational model for the development of disparity in visual cortex that depends on prenatal and postnatal phases.
  Proceedings of the National Academy of Science (USA), 90, 8277-8281.
  

• Dayan, P (1993a).
  Arbitrary elastic topologies and ocular dominance.
  Neural Computation, 5 392-401.

• Dayan, P (1993b).
  Improving generalisation for temporal difference learning: The successor representation.
  Neural Computation, 5, 613-624.

• Dayan, P & Hinton, GE (1993).
  Feudal reinforcement learning.
  In NIPS 5, 271-278.
  

• Dayan, P & Sejnowski, TJ (1993).
  The variance of covariance rules for associative matrix memories and reinforcement learning.
  Neural Computation, 5 205-209.

• Montague, PR, Dayan, P, Nowlan, SJ, Pouget, A & Sejnowski, TJ (1993).
  Using aperiodic reinforcement for directed self-organization.
  In NIPS 5, 969-977.

• Quartz, SR, Dayan, P, Montague, PR & Sejnowski, TJ (1993).
  Expectation learning in the brain using diffuse ascending projections.
  Society for Neuroscience Abstracts, 18, 1210.
  

1992

• Berns, GS, Dayan, P & Sejnowski, TJ (1992a).
  Correlation-based development of disparity sensitivity. In F Eeckmanm, editor, Neural Systems: Analysis and Modeling. Norwell, MA: Kluwer Academic.
  

• Berns, GS, Dayan, P & Sejnowski, TJ (1992b).
  Development of disparity sensitivity in a correlational-based network model of the visual cortex requires two phases.
  Society for Neuroscience Abstracts, 18.

• Dayan, P (1992).
  The convergence of TD() for general .
  Machine Learning, 8, 341-362.

• Dayan, P & Goodhill, GJ (1992).
  Perturbing Hebbian rules.
  In NIPS 4, 19-26.
  

• Montague, PR, Dayan, P & Sejnowski, TJ (1992).
  Volume learning: Signalling covariance through neural tissue.
  In F Eeckmanm, editor, Neural Systems: Analysis and Modeling. Norwell, MA: Kluwer Academic.

• Watkins, CJCH & Dayan, P (1992).
  Q-learning.
  Machine Learning, 8, 279-292.

1991

• Dayan, P (1991).
  Navigating through temporal differences.
  In NIPS 3, 464-470.
  

• Dayan, P & Willshaw, DJ (1991).
  Optimising synaptic learning rules in linear associative memories.
  Biological Cybernetics, 65, 253-265.

1990

• Dayan, P (1990).
  Reinforcement comparison.
  In DS Touretzky, JL Elman, TJ Sejnowski & GE Hinton, editors, Proceedings of the 1990 Connectionist Models Summer School. San Mateo, CA: Morgan Kaufmann, 45-51.
  

• Willshaw, DJ & Dayan, P (1990).
  Optimal plasticity in matrix memories: What goes up must come down.
  Neural Computation, 2, 85-93.

1985

• Robertson, MJ, Ritchie, S & Dayan, P (1985).
  Semiconductor waveguides: analysis of optical propagation in single rib structures and directional couplers.
  Proceedings of the Institution of Electrical Engineers, Part J, Optoelectronics, 132, 336-342.