Publications by Theme

Conditioning and Neuromodulation
The Hippocampus
Reinforcement Learning
Population Coding
Dynamics
Self-Supervised Learning
Activity-Dependent development
Miscellaneous

More comprehensive list of publications by date

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Some electronic versions are earlier drafts of published papers. The published versions should be considered definitive.
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Conditioning and Neuromodulation

Kakade, S & Dayan, P (2002).
Acquisition and extinction in autoshaping.
Psychological Review, 109, 533-544.
See also Kakade & Dayan (2000c), Dayan & Long (1998).
Daw, ND, Kakade, S & Dayan, P (2001).
Opponent interactions between serotonin and dopamine.
Submitted to Neural Networks.
Dayan, P (2001b).
Motivated reinforcement learning.
NIPS 2001, to appear.
Dayan, P & Yu, A (2001).
ACh, uncertainty, and cortical inference.
NIPS 2001, to appear.
See also Yu & Dayan (2001).
Kakade, S & Dayan, P (2001).
Dopamine bonuses.
Submitted to Neural Networks.
See also Kakade & Dayan (2001); Schultz, Dayan & Montague (1997).
Yu, A & Dayan, P (2001).
Acetylcholine and cortical inference.
Submitted to Neural Networks.
See also Dayan & Yu (2001).
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.
See also Kakade & Dayan (2000c), Dayan & Long (1998)
Foster, DJ, Morris, RGM & Dayan, P (2000).
Models of hippocampally dependent navigation using the temporal difference learning rule.
Hippocampus, 10, 1-16.
See also, Foster, Morris & Dayan (1998) , Foster, Morris & Dayan (1997), Dayan (1991).
Schultz, W, Dayan, P & Montague, PR (1997).
A neural substrate of prediction and reward.
Science, 275, 1593-1599.
See also Quartz, Dayan, Montague & Sejnowski (1993).
Montague, PR, Dayan, P & Sejnowski, TK (1996).
A framework for mesencephalic dopamine systems based on predictive Hebbian learning.
Journal of Neuroscience, 16, 1936-1947.
See also Quartz, Dayan, Montague & Sejnowski (1993).
Montague, PR, Dayan, P, Person, C & Sejnowski, TJ (1995).
Bee foraging in uncertain environments using predictive Hebbian learning.
Nature, 377, 725-728.
See also Montague, Dayan & Sejnowski (1994).

The Hippocampus

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.
See also Káli & Dayan (2000c), Káli & Dayan (1998).
See also Foster, Morris & Dayan (2000).

Reinforcement Learning

Foster, DJ & Dayan, P (2000).
Using unsupervised learning methods to extract structure in value functions.
Machine Learning, in press.
See also Dayan & Hinton (1993).
Singh, SP & Dayan, P (1998).
Analytical mean squared error curves in Temporal Difference learning.
Machine Learning, 32, 5-40.
See also Singh & Dayan (1997).
Dayan, P & Hinton, GE (1997).
Using EM for reinforcement learning.
Neural Computation, 9, 271-278.
Dayan, P & Sejnowski, TJ (1996).
Exploration bonuses and dual control.
Machine Learning, 25, 5-22.
Dayan, P & Sejnowski, TJ (1994).
TD() converges with probability 1.
Machine Learning, 14, 295-301.
Schraudolph, N, Dayan, P & Sejnowski, TJ (1994).
Temporal difference learning of position evaluation in the game of Go.
In NIPS 6, 817-824.
See also Schraudolph, Dayan & Sejnowski (2000).
Dayan, P (1993b).
Improving generalisation for temporal difference learning: The successor representation.
Neural Computation, 5, 613-624.
Dayan, P & Sejnowski, TJ (1993).
The variance of covariance rules for associative matrix memories and reinforcement learning.
Neural Computation, 5 205-209. See also Dayan (1990).
Dayan, P (1992).
The convergence of TD() for general .
Machine Learning, 8, 341-362.
Watkins, CJCH & Dayan, P (1992).
Q-learning.
Machine Learning, 8, 279-292.

Population Coding

Pouget, A, Dayan, P & Zemel, RS (2000).
Information processing with population codes.
Nature Reviews Neuroscience, 1 , 125-132.
See also Zemel & Dayan (1999).
Abbott, LF & Dayan, P (1999).
The effect of correlated variability on the accuracy of a population code.
Neural Computation, 11, 91-101.
Zemel, RS, Dayan, P & Pouget A (1998).
Probabilistic interpretation of population codes.
Neural Computation, 10, 403-430.
See also Zemel & Dayan (1998), Zemel & Dayan (1997), Zemel, Dayan & Pouget (1997).

Dynamics

Li, Z & Dayan, P (2000).
Position variance, recurrence and perceptual learning.
NIPS 2000, in press.
Li, Z & Dayan, P (1999a).
Computational differences between asymmetrical and symmetrical networks.
Network, 10, 59-77.
See also Li & Dayan (1999b).
Dayan, P (1998a).
A hierarchical model of visual rivalry.
Neural Computation, 10, 1119-1136.
See also Dayan (1997a).

Self-Supervised Learning

Dayan, P (1999a).
Recurrent sampling models for the Helmholtz machine.
Neural Computation, 11, 653-677.
See also Dayan (1998b), Dayan (1997b).
Hinton, GE, Dayan, P & Revow, M (1997).
Modeling the manifolds of images of handwritten digits.
IEEE Transactions on Neural Networks, 8, 65-74.
See also Hinton, Revow & Dayan (1995).
Neal, RM & Dayan, P (1997).
Factor Analysis using delta-rule wake-sleep learning.
Neural Computation, 9, 1781-1803.
Dayan, P & Hinton, GE (1996).
Varieties of Helmholtz machine.
Neural Networks, 9, 1385-1403.
Dayan, P, Hinton, GE, Neal, RM & Zemel, RS (1995).
The Helmholtz machine.
Neural Computation, 7, 889-904.
See also Dayan (2000a), Hinton, Dayan, To & Neal (1995).
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.
See also Frey, Dayan & Hinton (1997), Frey, Hinton & Dayan (1996), Hinton, Dayan, Neal & Zemel (1994).
See also Dayan (1999b).

Activity-Dependent Development

Dayan, P (2000c).
Competition and arbors in ocular dominance.
In NIPS 2000, 203-209.
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.
See also Berns, Dayan & Sejnowski (1992a), Berns, Dayan & Sejnowski (1992b).
Dayan, P (1993a).
Arbitrary elastic topologies and ocular dominance.
Neural Computation, 5 392-401.
See also Dayan & Goodhill (1992), Montague, Dayan & Sejnowski (1992).

Miscellaneous

Sommer, FT & Dayan, P (1998).
Bayesian retrieval in associative memories with storage errors.
IEEE Transactions in Neural Networks, 9, 705-713.
Dayan, P (1994).
Computational modelling.
Current Opinion in Neurobiology, 4, 212-217.
See also Dayan (2000b), Montague & Dayan (1998).
Dayan, P & Willshaw, DJ (1991).
Optimising synaptic learning rules in linear associative memories.
Biological Cybernetics, 65, 253-265.
Willshaw, DJ & Dayan, P (1990).
Optimal plasticity in matrix memories: What goes up must come down.
Neural Computation, 2, 85-93.

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