Small Print

2014

• Dayan P (2014)
  Rationalizable irrationalities of choice
  Topics in Cognitive Science doi: 10.1111/tops.12082

• Dayan P & Berridge KC (2014)
  Model-based and model-free Pavlovian reward learning: Revaluation, revision, and revelation
  Cognitive, Affective, & Behavioral Neuroscience doi: 10.3758/s13415-014-0277-8

• Guez, A, Silver, D & Dayan P (2014)
  Better Optimism By Bayes: Adaptive Planning with Rich Models
  arXiv doi: arXiv:1402.1958

• Guitart-Masip M, Duzel E, Dolan R & Dayan P (2014)
  Action versus valence in decision making
  Trends in Cognitive Sciences doi: 10.1016/j.tics.2014.01.003

• Guitart-Masip M, Economides M, Huys QJ, Frank MJ, Chowdhury R, Duzel E, Dayan P & Dolan RJ (2014)
  Differential, but not opponent, effects of L-DOPA and citalopram on action learning with reward and punishment.
  Psychopharmacology doi: 10.1007/s00213-013-3313-4

• Manley H, Dayan P & Diedrichsen J (2014)
  When money is not enough: awareness, success, and variability in motor learning.
  PLoS ONE doi: 10.1371/journal.pone.0086580

• Niyogi RK, Breton Y-A, Solomon RB, Conover K, Shizgal P, Dayan P (2014)
  Optimal indolence: a normative microscopic approach to work and leisure
  Journal of the Royal Society Interface doi: 10.1098/rsif.2013.0969

• Savin C, Dayan P & Lengyel M (2014)
  Optimal recall from bounded metaplastic synapses: predicting functional adaptations in hippocampal area CA3.
  PLoS Computational Biology doi: 10.1371/journal.pcbi.1003489

• Voon V, Derbyshire K, Rück C, Irvine MA, Worbe Y, Enander J, Schreiber LR, Gillan C, Fineberg NA, Sahakian BJ, Robbins TW, Harrison NA, Wood J, Daw ND, Dayan P, Grant JE, Bullmore ET (2014)
  Disorders of compulsivity: a common bias towards learning habits.
  Molecular Psychiatry doi: 10.1038/mp.2014.44

2013

• Beierholm U, Guitart-Masip M, Economides M, Chowdhury R, Duzel E, Dolan R, Dayan P (2013)
  Dopamine modulates reward related vigor.
  Neuropsychopharmacology doi: 10.1038/npp.2013.48

• Chowdhury R, Guitart-Masip M, Lambert C, Dayan P, Huys Q, Duzel E & Dolan RJ. (2013)
  Dopamine restores reward prediction errors in old age.
  Nature Neuroscience doi: 10.1038/nn.3364

• Dolan RJ & Dayan P (2013)
  Goals and Habits in the Brain
  Neuron doi: 10.1016/j.neuron.2013.09.007

• Guez A, Silver D & Dayan P (2013)
  Scalable and Efficient Bayes-Adaptive Reinforcement Learning Based on Monte-Carlo Tree Search
  Journal of AI Research doi: 10.1613/jair.4117    [31/3/2016: correcting a couple of typos]

• Hunt JJ, Dayan P & Goodhill GJ (2013)
  Sparse Coding Can Predict Primary Visual Cortex Receptive Field Changes Induced by Abnormal Visual Input
  PLoS Computational Biology doi: 10.1371/journal.pcbi.1003005a

• Huys QJM, Pizzagalli DA, Bogdan R & Dayan P (2013)
  Mapping anhedonia onto reinforcement learning: a behavioural meta-analysis
  Biology of Mood & Anxiety Disorders doi: 10.1186/2045-5380-3-12

• Kurniawan IT, Guitart-Masip M, Dayan P & Dolan RJ (2013)
  Effort and valuation in the brain: the effects of anticipation and execution.
  Journal of Neuroscience doi: 10.1523/JNEUROSCI.4777-12.2013

• Qian N & Dayan P (2013)
  The company they keep: Background similarity influences transfer of aftereffects from second- to first-order stimuli
  Vision Research doi: 10.1016/j.visres.2013.05.008

2012

• Coen-Cagli R, Dayan P, Schwartz O (2012)
  Cortical surround interactions and perceptual salience via natural scene statistics.
  PLoS Computational Biology doi: 10.1371/journal.pcbi.1002405

• Dayan P (2012)
  How to set the switches on this thing.
  Current Opinion in Neurobiology doi:10.1016/j.conb.2012.05.011

• Dayan P (2012)
  Instrumental vigour in punishment and reward.
  European Journal of Neuroscience doi: 10.1111/j.1460-9568.2012.08026.x

• Dayan P (2012)
  Twenty-five lessons from computational neuromodulation.
  Neuron doi: 10.1016/j.neuron.2012.09.027,

• Dayan P & Walton ME (2012)
  A step-by-step guide to dopamine.
  Biological Psychiatry doi: 10.1016/j.biopsych.2012.03.008

• Guitart-Masip M, Chowdhury R, Sharot T, Dayan P, Duzel E & Dolan RJ (2012)
  Action controls dopaminergic enhancement of reward representations.
  PNAS doi: 10.1073/pnas.1202229109

• Guitart-Masip M, Huys QJ, Fuentemilla L, Dayan P, Duzel E & Dolan RJ (2012).
  Go and no-go learning in reward and punishment: Interactions between affect and effect.
  Neuroimage doi: 10.1016/j.neuroimage.2012.04.024

• Huys QJ, Eshel N, O'Nions E, Sheridan L, Dayan P & Roiser JP (2012)
  Bonsai trees in your head: How the pavlovian system sculpts goal-directed choices by pruning decision trees.
  PLoS Computational Biology doi:10.1371/journal.pcbi.1002410

• Montague PR, Dolan RJ, Friston KJ & Dayan P (2012)
  Computational psychiatry.
  Trends in Cognitive Science doi:10.1016/j.tics.2011.11.018

• Niv Y, Edlund, JA, Dayan, P & O'Doherty, JP (2012)
  Neural prediction errors reveal a risk-sensitive reinforcement-learning process in the human brain.
  Journal of Neuroscience doi: 10.1523/JNEUROSCI.5498-10.2012

• Seymour B, Daw ND, Roiser, RP, Dayan P & Dolan RJ (2012)
  Serotonin selectively modulates reward value in human decision-making.
  Journal of Neuroscience doi: 10.1523/JNEUROSCI.0053-12.201

• Shiner T, Seymour B, Symmonds M, Bhatia KP, Dayan P & Dolan RJ (2012)
  The effect of motivation on movement: A study of bradykinesia in Parkinson's disease
  PLoS One doi: 10.1371/journal.pone.0047138

• Shiner T, Seymour B, Wunderlich K, Hill C, Bhatia KP, Dayan P & Dolan RJ (2012)
  Dopamine and performance in a reinforcement learning task: evidence from Parkinson's disease.
  Brain doi: 10.1093/brain/aws083

• Wunderlich K, Dayan P & Dolan RJ (2012)
  Mapping value based planning and extensively trained choice in the human brain.
  Nature Neuroscience doi:10.1038/nn.3068

• Xu, H, Liu, P, Dayan, P & Qian N (2012)
  Multi-level visual adaptation: Dissociating curvature and facial-expression aftereffects produced by the same adapting stimuli .
  Vision Reseach doi:10.1016/j.visres.2012.09.003

2011

• Boureau Y-L & Dayan P (2011)
  Opponency revisited: Competition and cooperation between dopamine and serotonin.
  Neuropsychopharmacology doi:10.1038/npp.2010.151.

• Daw ND, Gershman SJ, Seymour B, Dayan P & Dolan RJ (2011)
  Model-based influences on humans' choices and striatal prediction errors.
  Neuron doi:10.1016/j.neuron.2011.02.027

• Guitart-Masip M, Beierholm UR, Dolan R, Duzel E & Dayan P (2011)
  Vigor in the face of fluctuating rates of reward: An experimental examination.
  Journal of Cognitive Neuroscience Epub ahead of print.

• Guitart-Masip M, Fuentemilla L, Bach DR, Huys QJ, Dayan P, Dolan RJ & Duzel E (2011)
  Action dominates valence in anticipatory representations in the human striatum and dopaminergic midbrain.
  Journal of Neuroscience doi: 10.1523/​JNEUROSCI.6376-10.2011

• Huys QJ, Cools R, Golzer M, Friedel E, Heinz A, Dolan RJ & Dayan P (2011)
  Disentangling the roles of approach, activation and valence in instrumental and Pavlovian responding.
  PLoS Computational Biology doi: 10.1371/journal.pcbi.1002028

• Mortimer D, Dayan P, Burrage K & Goodhill GJ (2011)
  Bayes-optimal chemotaxis.
  Neural Computation doi: 10.1162/NECO_a_00075

• Moutoussis M, Bentall RP, El-Deredy W & Dayan P (2011)
  Bayesian modelling of Jumping-to-Conclusions bias in delusional patients.
  Cognitive Neuropsychiatry doi: 10.1080/13546805.2010.548678

• Sanborn AN & Dayan P (2011).
  Optimal decisions for contrast discrimination.
  Journal of Vision doi:10.1167/11.14.9

2010

• Beierholm U & Dayan P (2010)
  Pavlovian-instrumental interaction in 'observing behavior'.
  PLoS Computational Biology doi:10.1371/journal.pcbi.1000903

• Bunzeck N & Dayan P and Dolan RJ & Duzel E (2010)
  A common mechanism for adaptive scaling of reward and novelty.
  Human Brain Mapping doi:10.1002/hbm.20939

• Dayan P & Solomon JA (2010)
  Selective Bayes: Attentional load and crowding.
  Vision Research doi:10.1016/j.visres.2010.04.014

• Glascher J & Daw ND & Dayan P & O'Doherty JP (2010)
  States versus rewards: dissociable neural prediction error signals underlying model-based and model-free reinforcement learning.
  Neuron 10.1016/j.neuron.2010.04.016

• Moazzezi R & Dayan P (2010)
  Change-Based Inference in Attractor Nets: Linear Analysis.
  Neural Computation doi:10.1162/NECO_a_00051

• Mortimer D & Dayan P & Burrage K & Goodhill GJ (2010)
  Optimizing chemotaxis by measuring unbound-bound transitions.
  Physica D 10.1016/j.physd.2009.09.009

• Pfister J-P & Dayan P & Lengyel M (2010)
  Synapses with short-term plasticity are optimal estimators of presynaptic membrane potentials.
  Nature Neuroscience doi:10.1038/nn.2640

2009

• Dayan P (2009)
  Dopamine, reinforcement learning, and addiction.
  Pharmacopsychiatry 42 (S1) S56-S65.

• Dayan P (2009)
  Goal-directed control and its antipodes
  Neural Networks 22 213-219.

• Dayan P (2009)
  Prospective and retrospective temporal difference learning.
  Network 20 32-46.

• Dayan P & Huys QJM (2009)
  Serotonin in Affective Control
  Annual Review of Neuroscience 32 95-126.

• Huys QJM & Dayan P (2009)
  A Bayesian formulation of behavioral control.
  Cognition 113 314-328

• Krueger KA & Dayan P (2009)
  Flexible shaping: How learning in small steps helps.
  Cognition 110 380-394.

• Mortimer D & Feldner J & Vaughan T & Vetter I & Pujic Z & Rosoff WJ & Burrage K & Dayan P & Richards LJ & Goodhill GJ (2009)
  A Bayesian model predicts the response of axons to molecular gradients.
  PNAS 106 10296-10301.

• Pfister J-P & Dayan P & Lengyel M (2009)
  Know thy neighbour: A normative theory of synaptic depression.
  NIPS 22 1464-1472.

• Schwartz O & Sejnowski TJ & Dayan P (2009)
  Perceptual organization in the tilt illusion.
  Journal of Vision 9 1-20.

• Talmi D & Dayan P & Kiebel SJ & Frith CD & Dolan RJ (2009)
  How humans integrate the prospects of pain and reward during choice.
  Journal of Neuroscience 29 14617-14626.

• Yu AJ & Dayan P & Cohen JD (2009)
  Dynamics of attentional selection under conflict: Toward a rational Bayesian Account
  Journal of Experimental Psychology: Human Perception and Performance 35 700-717.

• Wu J & Xu H & Dayan P & Qian N (2009)
  The role of background statistics in face adaptation
  Journal of Neuroscience 29 12035-12044.
  Please see the correction to this paper.

2008

• Daw ND, Courville AC & Dayan P (2008)
  Semi-rational models of conditioning: The case of trial order.
  In Chater N & Oaksford M, editors, The Probabilistic Mind: Prospects for Bayesian Cognitive Science New York, NY: OUP, 427-448.

• Dayan P (2008)
  The role of value systems in decision making.
  In Engel C & Singer W, editors, Better than Conscious? Decision Making, the Human Mind, and Implications for Institutions Frankfurt, Germany: MIT Press, 51-70.

• Dayan P (2008)
  Simple substrates for complex cognition.
  Frontiers in Neuroscience 2 255-263.

• Dayan P (2008)
  Load and attentional Bayes.
  NIPS 2008.

• Dayan P & Daw ND (2008)
  Decision theory, reinforcement learning, and the brain.
  Cognitive, Affective & Behavioral Neuroscience 8 429-453.

• Dayan P & Huys QJM (2008)
  Serotonin, inhibition and negative mood.
  Public Library of Science: Computational Biology 4 e4.

• Dayan P & Niv Y (2008)
  Reinforcement learning: The good, the bad and the ugly.
  Current Opinion in Neurobiology 18 185-196.

• de Lucia, M, Fritschy, J, Dayan, P & Holder, DS (2008)
  A novel method for automated classification of the human Electroencephalogram based on Independent Component Analysis.
  Medical & Biological Engineering & Computing 46 263-272.

• Herrero JL, Roberts MJ, Delicato LS, Gieselmann MA, Dayan P & Thiele A (2008)
  Acetylcholine contributes through muscarinic receptors to attentional modulation in V1
  Nature 454 1110-1114.

• Huys QJM, Vogelstein J & Dayan P (2008)
  Psychiatry: Insights into depression through normative decision-making models.
  NIPS 2008.

• Moazzezi R & Dayan P (2008)
  Change-based inference for invariant discrimination.
  Network 19 236-252.

• Moutoussis, M, Bentall, RP, Williams, J & Dayan P (2008)
  A temporal difference account of avoidance learning.
  Network 19 137-160.

• Natarajan R, Huys QJ, Dayan P & Zemel RS (2008)
  Encoding and decoding spikes for dynamic stimuli.
  Neural Computation 20 2325-2360.

• Ray D, King-Casas B, Montague, PR & Dayan P (2008)
  Bayesian model of behaviour in economic games.
  NIPS 2008.

• Talmi D, Seymour B, Dayan P & Dolan RJ (2008)
  Human pavlovian-instrumental transfer.
  Journal of Neuroscience 28 360-368.

• Xu H, Dayan P, Lipkin RM & Qian N (2008)
  Adaptation across the cortical hierarchy: low-level curve adaptation affects high-level facial-expression judgments.
  Journal of Neuroscience 28 3374-3383.

2007

• Dayan, P (2007)
  Bilinearity, rules and prefrontal cortex.
  Frontiers in Computational Neuroscience 1 1.

• De Lucia M, Fritschy J, Dayan P & Holder DS (2007)
  A novel method for automated classification of epileptiform activity in the human electroencephalogram-based on independent component analysis.
  Medical & Biological Engineering & Computing 10.1007/s11517-007-0289-4.

• Hsu, A & Dayan, P (2007)
  An unsupervised learning model of neural plasticity: Orientation selectivity in goggle-reared kittens.
  Vision Research 47 2868-2877.

• Huys, QJM, Zemel, RS, Natarajan, R & Dayan, P (2007)
  Fast population coding.
  Neural Computation 19 404-441.

• Lengyel, M & Dayan P (2007)
  Hippocampal contributions to control: The third way
  NIPS 2007

• Moutoussis M, Williams J, Dayan P & Bentall RP (2007)
  Persecutory delusions and the conditioned avoidance paradigm: towards an integration of the psychology and biology of paranoia.
  Cognitive Neuropsychiatry 12 495-510.

• Schwartz, O, Hsu, A & Dayan, P (2007)
  Space and time in visual context
  Nature Reviews Neuroscience 8 522-535.

• Seymour, B, Daw, ND, Dayan, P, Singer, T & Dolan, RJ (2007)
  Differential encoding of losses and gains in the human striatum.
  Journal of Neuroscience 27 4826-4831.

2006

• Daw, ND, O'Doherty, JP, Dayan, P, Seymour, B & Dolan, RJ (2006)
  Cortical substrates for exploratory decisions in humans.
  Nature 441 876-879.

• Dayan, P (2006)
  Images, frames and connectionist hierarchies.
  Neural Computation 18 2293-2319.

• Dayan, P, Niv, Y, Seymour, BJ & Daw, ND (2006)
  The misbehavior of value and the discipline of the will.
  Neural Networks 19 1153-1160.

• Dayan, P & Yu, AJ (2006)
  Phasic norepinephrine: A neural interrupt signal for unexpected events.
  Network 17 335-350.

• Gruber, AJ, Dayan, P, Gutkin BS & Solla, SA (2006)
  Dopamine modulation in the basal ganglia locks the gate to working memory.
  Journal of Computational Neuroscience 20 153-166.

• Lengyel, M & Dayan P (2006)
  Uncertainty, phase and oscillatory hippocampal recall
  NIPS 2006

• Niv, Y, Daw, ND & Dayan, P (2006)
  Choice values.
  Nature Neuroscience 9 987-988.

• Niv, Y, Daw, ND, Joel, D & Dayan, P (2006)
  Tonic dopamine: Opportunity costs and the control of response vigor.
  Psychopharmacology DOI: 10.1007/s00213-006-0502-4

• Niv, Y, Joel, D & Dayan, P (2006)
  A normative perspective on motivation.
  Trends in Cognitive Science 8 375-381.

• Schwartz, O, Sejnowski, TJ & Dayan, P (2006)
  Soft mixer assignment in a hierarchical generative model of natural scene statistics.
  Neural Computation 18 2680-2718.

• Zhaoping, L & Dayan, P (2006)
  Pre-attentive visual selection.
  Neural Networks 19 1437-1439.

2005

• Daw, ND, Niv, Y & Dayan, P (2005)
  Actions, policies, values, and the basal ganglia.
  In Bezard, editor, Recent Breakthroughs in Basal Ganglia Research. New York, NY: Nova Science Publishers.

• Daw, ND, Niv, Y & Dayan, P (2005)
  Uncertainty-based competition between prefrontal and dorsolateral striatal systems for behavioral control.
  Nature Neuroscience 8 1704-1711.

• Latham, PE & Dayan, P (2005)
  The feeling of choice.
  Nature Neuroscience 8 408-409.

• Lengyel, M, Kwag, J, Paulsen, O & Dayan, P (2005).
  Matching storage and recall: hippocampal spike timing-dependent plasticity and phase response curves.
  Nature Neuroscience 8 1677-1683.

• Niv, Y, Duff, MO & Dayan, P (2005)
  Dopamine, uncertainty and TD learning.
  Behavioral and Brain Functions 1 6.

• Williams, J & Dayan, P (2005)
  Dopamine, learning, and impulsivity: A biological account of attention-deficit/hyperactivity disorder.
  Journal of Child and Adolescent Psychopharmacology 15 160-179.

• Yu, AJ & Dayan, P (2005)
  Uncertainty, neuromodulation, and attention.
  Neuron 46 681-692.

2004

• Daw, ND & Dayan, P (2004).
  Matchmaking.
  Science 304 1753-1754.
  

• Dayan, P (2004).
  Pattern formation and cortical maps.
  Journal of Physiology, Paris 97 475-489.
  

• Káli, S & Dayan, P (2004)
  Off-line replay maintains declarative memories in a model of hippocampal-neocortical interactions.
  Nature Neuroscience 7, 286-294.
  See also Káli & Dayan (2002).
  

• Lengyel, M & Dayan, P (2004)
  Rate- and phase-coded autoassociative memory.
  NIPS 2004.
  

• O'Doherty, J, Dayan, P, Schultz, J, Deischmann, R, Friston, K & Dolan, RJ (2004)
  Dissociable roles of ventral and dorsal striatum in instrumental conditioning.
  Science 304 452-454.
  

• Schwartz, O, Sejnowski, TJ & Dayan, P (2004)
  Assignment of multiplier mixture variables in natural scene.
  NIPS 2004.
  

• Seymour, B, O'Doherty, JP, Dayan, P, Koltzenburg, M, Jones, AK, Dolan, RJ, Friston, KJ & Frackowiak, R (2004)
  Temporal difference models describe higher order learning in humans.
  Nature 429 664-667.

• Yu, AJ & Dayan, P (2004)
  Inference, attention, and decision in a Bayesian neural architecture.
  NIPS 2004.
  

• Zemel, RS, Huys, QJM, Natarajan, R & Dayan, P (2004)
  Probabilistic computation in spiking populations.
  NIPS 2004.
  

2003

• Dayan, P, Hausser, M & London, M (2003)
  Plasticity kernels and temporal statistics.
  NIPS 2003.
  

• Dayan, P & Yu, AJ (2003).
  Uncertainty and learning.
  IETE Journal of Research 49, 171-182.
  

• Gruber, AJ, Dayan, P, Gutkin, BS & Solla, SA (2003)
  Dopamine modulation in a basal ganglio-cortical network of working memory.
  NIPS 2003.
  

• O'Doherty, J, Dayan, P, Friston, K, Critchley, H & Dolan, R (2003).
  Temporal difference models and reward-related learning in the human brain.
  Neuron 38, 329-337.
  

• Pouget, A, Dayan, P & Zemel, RS (2003)
  Inference and computation with population codes.
  Annual Review of Neuroscience 26, 381-410.
  

• Zhaoping, L, Herzog, MH & Dayan, P (2003).
  Quadratic ideal observation and recurrent preprocessing in perceptual learning.
  Network 14, 233-247.
  

2002

• Dayan, P (2002).
  Matters temporal.
  Trends in Cognitive Sciences, 6, 105-106.
  

• Daw, ND, Kakade, S & Dayan, P (2002).
  Opponent interactions between serotonin and dopamine.
  Neural Networks 15, 603-616.
  

• Dayan, P & Balleine, BW (2002).
  Reward, motivation and reinforcement learning.
  Neuron 36, 285-298.
  

• Dayan, P, Sahani, M & Deback, G (2002).
  Adaptation and unsupervised learning.
  NIPS 2002.
  

• Foster, DJ & Dayan, P (2002).
  Structure in the space of value functions.
  Machine Learning 49, 325-246.
  

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

• Káli, S & Dayan, P (2002)
  Replay, repair and consolidation.
  NIPS 2002.
  

• Kakade, S & Dayan, P (2002).
  Dopamine: Generalization and bonuses.
  Neural Networks 15, 549-559.
  

• Yu, AJ & Dayan, P (2002).
  Acetylcholine in cortical inference.
  Neural Networks 15, 719-730.
  

• Yu, AJ & Dayan, P (2002).
  Expected and unexpected uncertainty. ACh and NE in the neocortex.
  NIPS 2002.
  

2001

• 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.
  

• Dayan, P (2001c).
  Reinforcement learning.
  In CR Gallistel, editor, Steven's Handbook of Experimental Psychology New York, NY: Wiley.
  

• Dayan P & Watkins, CJCH (2001).
  Reinforcement learning.
  Encyclopedia of Cognitive Science London, England: MacMillan Press.
  

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

2000

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

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

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

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

• 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 (2000b).
  Dopamine bonuses.
  NIPS 2000.

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

• 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.
  CNS 2000.
  

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

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

• 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.
  

• Kakade, S & Dayan, P (1999).
  Acquisition in autoshaping.
  NIPS 1999.

• 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 (1999).
  Computational differences between asymmetrical and symmetrical networks.
  Network, 10, 59-77.
  

• 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.

1998

• Dayan, P (1998a).
  A hierarchical model of visual rivalry.
  Neural Computation, 10, 1119-1136.
  See erratum to P1124
  

• 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.
  

• Li, Z & Dayan, P (1998).
  Computational differences between asymmetrical and symmetrical networks.
  NIPS 1998.
  

• 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 (1998).
  Distributional population codes and multiple motion models.,
  NIPS 1998.
  

• 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.

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.

• Quartz, SR, Dayan, P, Montague, PR & Sejnowski, TJ (1992).
  Expectation learning in the brain using diffuse ascending projections.
  Society for Neuroscience Abstracts, 18, 1210.
  

• 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.