2002
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 wakesleep
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, 203209.

Dayan, P & Kakade, S (2000).
Explaining away in weight space.
In NIPS 2000, 451457.

Dayan, P, Kakade, S & Montague, PR (2000).
Learning and selective attention.
Nature Neuroscience, 3 , 12181223.

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, 116.

Kakade, S & Dayan, P (2000a).
Acquisition and extinction in
autoshaping.
Psychological Review, 109, 533544.

Kakade, S & Dayan, P (2000b).
Dopamine bonuses.
In NIPS 2000, 131137.

Kakade, S & Dayan, P (2000c).
Acquisition in autoshaping.
In NIPS 12, 2430.

Káli, S & Dayan, P
(2000a).
Hippocampallydependent consolidation
in a hierarchical model of neocortex.
In NIPS 2000, 2430.

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, 74637477.

Káli, S & Dayan, P (2000c).
A familiaritybased 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 , 125132.
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: SpringerVerlag.
1999

Abbott, LF & Dayan, P (1999).
The effect of correlated variability on
the accuracy of a population code.
Neural Computation, 11, 91101.

Dayan, P (1999a).
Recurrent sampling models for the
Helmholtz machine.
Neural Computation, 11, 653677.

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, 10171022.

Káli, S & Dayan, P (1999).
Spatial representations in related
environments in a recurrent model of area CA3 of the
rat.
In ICANN 1999 , 138143.

Li, Z & Dayan, P (1999a).
Computational differences between
asymmetrical and symmetrical networks.
Network, 10, 5977.

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 AbuMostafa, 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, 11191136.

Dayan, P (1998b).
Recurrent sampling models.
In KM Wong, I King & DY Yeung, editors,
Theoretical Aspects of Neural Computation: A Multidisciplinary
Perspective, 287298. Berlin: Springer.

Dayan, P & Long, T (1998).
Statistical models of conditioning.
In NIPS 10, 117123.

Foster, DJ, Morris, RGM & Dayan, P (1998).
Hippocampal model of rat spatial abilities using
temporal difference learning.
In NIPS 10, 145151.

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, 526542.

Singh, SP & Dayan, P (1998).
Analytical mean squared error curves in Temporal
Difference learning.
Machine Learning, 32, 540.

Sommer, FT & Dayan, P (1998).
Bayesian retrieval in associative memories
with storage errors.
IEEE Transactions in Neural Networks, 9, 705713.

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, 403430.
1997

Dayan, P (1997a).
A hierarchical model of visual
rivalry.
In NIPS 9, 4854.

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, 271278.

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, 296315. Cambridge:
CUP.

Hinton, GE, Dayan, P & Revow, M (1997).
Modeling the manifolds of images of
handwritten digits.
IEEE Transactions on Neural Networks, 8, 6574.

Neal, RM & Dayan, P (1997).
Factor Analysis using deltarule
wakesleep learning.
Neural Computation, 9, 17811803.

Reisenhuber, M & Dayan, P (1997).
Neural models for partwhole
hierarchies.
In NIPS 9, 661667.

Schultz, W, Dayan, P & Montague, PR (1997).
A neural substrate of prediction and
reward.
Science, 275, 15931599.

Singh, SP & Dayan, P (1997).
Analytical mean squared error curves in
temporal difference learning.
In NIPS 9, 10541060.

Zemel, RS, Dayan, P & Pouget, A (1997).
Probabilistic interpretation of population
codes.
In NIPS 9, 676682.

Zemel, RS & Dayan, P (1997).
Combining probabilistic population
codes.
In IJCAI 15, 11141119.
1996

Dayan, P & Hinton, GE (1996).
Varieties of Helmholtz machine.
Neural Networks, 9, 13851403.

Dayan, P & Sejnowski, TJ (1996).
Exploration bonuses and dual control.
Machine Learning, 25, 522.

Dayan, P & Singh, SP (1996a).
Improving policies without measuring
merits.
In NIPS 8, 10591065.

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

Frey, BJ, Hinton, GE & Dayan, P
(1996).
Does the wakesleep algorithm produce
good density estimators?
In NIPS 8, 661667.

Montague, PR, Dayan, P & Sejnowski, TK (1996).
A framework for mesencephalic dopamine
systems based on predictive Hebbian learning.
Journal of Neuroscience, 16, 19361947.

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, 889904.

Dayan, P & Zemel, RS (1995).
Competition and multiple cause
models.
Neural Computation, 7, 565579.

Hinton, GE, Dayan, P, Frey, BJ & Neal, RM (1995).
The wakesleep algorithm for unsupervised
neural networks.
Science, 268, 11581160.

Hinton, GE, Dayan, P, To, A & Neal, R (1995).
The Helmholtz machine through time.
In F FogelmanSoulie & R Gallinari, editors, ICANN95,,
483490.

Hinton, GE, Revow, M & Dayan, P (1995).
Recognizing handwritten digits using
mixtures of linear models.
In NIPS 7, 10151022.

Montague, PR, Dayan, P, Person, C & Sejnowski, TJ (1995).
Bee foraging in uncertain environments
using predictive Hebbian learning.
Nature, 377, 725728.

Sejnowski, TJ, Dayan, P & Montague, PR (1995).
Predictive Hebbian learning.
Invited talk to COLT 8, 1518.
1994

Dayan, P (1994).
Computational modelling.
Current Opinion in Neurobiology, 4, 212217.

Dayan, P & Sejnowski, TJ (1994).
TD()
converges with probability 1.
Machine Learning, 14, 295301.

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, 598605.

Schraudolph, N, Dayan, P & Sejnowski, TJ (1994).
Temporal difference learning of position
evaluation in the game of Go.
In NIPS 6, 817824.
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, 82778281.

Dayan, P (1993a).
Arbitrary elastic topologies and ocular
dominance.
Neural Computation, 5 392401.

Dayan, P (1993b).
Improving generalisation for temporal difference learning: The
successor representation.
Neural Computation, 5, 613624.

Dayan, P & Hinton, GE (1993).
Feudal reinforcement learning.
In NIPS 5, 271278.

Dayan, P & Sejnowski, TJ (1993).
The variance of covariance rules for
associative matrix memories and reinforcement learning.
Neural Computation, 5 205209.

Montague, PR, Dayan, P, Nowlan, SJ, Pouget, A & Sejnowski, TJ
(1993).
Using aperiodic reinforcement for
directed selforganization.
In NIPS 5, 969977.

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).
Correlationbased 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 correlationalbased 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, 341362.

Dayan, P & Goodhill, GJ (1992).
Perturbing Hebbian rules.
In NIPS 4, 1926.

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).
Qlearning.
Machine Learning, 8, 279292.
1991
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, 4551.

Willshaw, DJ & Dayan, P (1990).
Optimal plasticity in matrix memories:
What goes up must come down.
Neural Computation, 2, 8593.
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, 336342.