| 2008 |
|---|
| 73 | EE | Debajyoti Ray,
Brooks King-Casas,
P. Read Montague,
Peter Dayan:
Bayesian Model of Behaviour in Economic Games.
NIPS 2008: 1345-1352 |
| 72 | EE | Peter Dayan:
Load and Attentional Bayes.
NIPS 2008: 369-376 |
| 71 | EE | Quentin J. M. Huys,
Joshua T. Vogelstein,
Peter Dayan:
Psychiatry: Insights into depression through normative decision-making models.
NIPS 2008: 729-736 |
| 70 | EE | Rama Natarajan,
Quentin J. M. Huys,
Peter Dayan,
Richard S. Zemel:
Encoding and Decoding Spikes for Dynamic Stimuli.
Neural Computation 20(9): 2325-2360 (2008) |
| 2007 |
|---|
| 69 | EE | Máté Lengyel,
Peter Dayan:
Hippocampal Contributions to Control: The Third Way.
NIPS 2007 |
| 68 | EE | Quentin J. M. Huys,
Richard S. Zemel,
Rama Natarajan,
Peter Dayan:
Fast Population Coding.
Neural Computation 19(2): 404-441 (2007) |
| 2006 |
|---|
| 67 | EE | Máté Lengyel,
Peter Dayan:
Uncertainty, phase and oscillatory hippocampal recall.
NIPS 2006: 833-840 |
| 66 | EE | Aaron J. Gruber,
Peter Dayan,
Boris S. Gutkin,
Sara A. Solla:
Dopamine modulation in the basal ganglia locks the gate to working memory.
Journal of Computational Neuroscience 20(2): 153-166 (2006) |
| 65 | EE | Peter Dayan:
Images, Frames, and Connectionist Hierarchies.
Neural Computation 18(10): 2293-2319 (2006) |
| 64 | EE | Odelia Schwartz,
Terrence J. Sejnowski,
Peter Dayan:
Soft Mixer Assignment in a Hierarchical Generative Model of Natural Scene Statistics.
Neural Computation 18(11): 2680-2718 (2006) |
| 63 | EE | Peter Dayan,
Yael Niv,
Ben Seymour,
Nathaniel D. Daw:
The misbehavior of value and the discipline of the will.
Neural Networks 19(8): 1153-1160 (2006) |
| 62 | EE | Zhaoping Li,
Peter Dayan:
Pre-attentive visual selection.
Neural Networks 19(9): 1437-1439 (2006) |
| 2005 |
|---|
| 61 | EE | Miguel Á. Carreira-Perpiñán,
Peter Dayan,
Geoffrey J. Goodhill:
Differential Priors for Elastic Nets.
IDEAL 2005: 335-342 |
| 60 | EE | Odelia Schwartz,
Terrence J. Sejnowski,
Peter Dayan:
A Bayesian Framework for Tilt Perception and Confidence.
NIPS 2005 |
| 59 | EE | Yael Niv,
Nathaniel D. Daw,
Peter Dayan:
How fast to work: Response vigor, motivation and tonic dopamine.
NIPS 2005 |
| 58 | EE | Peter Dayan,
Angela J. Yu:
Norepinephrine and Neural Interrupts.
NIPS 2005 |
| 2004 |
|---|
| 57 | EE | Odelia Schwartz,
Terrence J. Sejnowski,
Peter Dayan:
Assignment of Multiplicative Mixtures in Natural Images.
NIPS 2004 |
| 56 | EE | Angela J. Yu,
Peter Dayan:
Inference, Attention, and Decision in a Bayesian Neural Architecture.
NIPS 2004 |
| 55 | EE | Richard S. Zemel,
Quentin J. M. Huys,
Rama Natarajan,
Peter Dayan:
Probabilistic Computation in Spiking Populations.
NIPS 2004 |
| 54 | EE | Máté Lengyel,
Peter Dayan:
Rate- and Phase-coded Autoassociative Memory.
NIPS 2004 |
| 2003 |
|---|
| 53 | EE | Aaron J. Gruber,
Peter Dayan,
Boris S. Gutkin,
Sara A. Solla:
Dopamine Modulation in a Basal Ganglio-cortical Network Implements Saliency-based Gating of Working Memory.
NIPS 2003 |
| 52 | EE | Peter Dayan,
Michael Häusser:
Plasticity Kernels and Temporal Statistics.
NIPS 2003 |
| 51 | EE | Maneesh Sahani,
Peter Dayan:
Doubly Distributional Population Codes: Simultaneous Representation of Uncertainty and Multiplicity.
Neural Computation 15(10): 2255-2279 (2003) |
| 2002 |
|---|
| 50 | EE | Angela J. Yu,
Peter Dayan:
Expected and Unexpected Uncertainty: ACh and NE in the Neocortex.
NIPS 2002: 157-164 |
| 49 | EE | Szabolcs Káli,
Peter Dayan:
Replay, Repair and Consolidation.
NIPS 2002: 19-26 |
| 48 | EE | Peter Dayan,
Maneesh Sahani,
Gregoire Deback:
Adaptation and Unsupervised Learning.
NIPS 2002: 221-228 |
| 47 | | David J. Foster,
Peter Dayan:
Structure in the Space of Value Functions.
Machine Learning 49(2-3): 325-346 (2002) |
| 46 | EE | Kenji Doya,
Peter Dayan,
Michael E. Hasselmo:
Introduction for 2002 Special Issue: Computational Models of Neuromodulation.
Neural Networks 15(4-6): 475-477 (2002) |
| 45 | EE | Sham Kakade,
Peter Dayan:
Dopamine: generalization and bonuses.
Neural Networks 15(4-6): 549-559 (2002) |
| 44 | EE | Nathaniel D. Daw,
Sham Kakade,
Peter Dayan:
Opponent interactions between serotonin and dopamine.
Neural Networks 15(4-6): 603-616 (2002) |
| 43 | EE | Angela J. Yu,
Peter Dayan:
Acetylcholine in cortical inference.
Neural Networks 15(4-6): 719-730 (2002) |
| 2001 |
|---|
| 42 | EE | Peter Dayan:
Motivated Reinforcement Learning.
NIPS 2001: 11-18 |
| 41 | EE | Peter Dayan,
Angela J. Yu:
ACh, Uncertainty, and Cortical Inference.
NIPS 2001: 189-196 |
| 40 | EE | Szabolcs Káli,
Peter Dayan:
A familiarity-based learning procedure for the establishment of place fields in area CA3 of the rat hippocampus.
Neurocomputing 38-40: 691-695 (2001) |
| 2000 |
|---|
| 39 | | Sham Kakade,
Peter Dayan:
Dopamine Bonuses.
NIPS 2000: 131-137 |
| 38 | | Peter Dayan:
Competition and Arbors in Ocular Dominance.
NIPS 2000: 203-209 |
| 37 | | Szabolcs Káli,
Peter Dayan:
Hippocampally-Dependent Consolidation in a Hierarchical Model of Neocortex.
NIPS 2000: 24-30 |
| 36 | | Zhaoping Li,
Peter Dayan:
Position Variance, Recurrence and Perceptual Learning.
NIPS 2000: 31-37 |
| 35 | | Peter Dayan,
Sham Kakade:
Explaining Away in Weight Space.
NIPS 2000: 451-457 |
| 1999 |
|---|
| 34 | EE | Sham Kakade,
Peter Dayan:
Acquisition in Autoshaping.
NIPS 1999: 24-30 |
| 33 | | L. F. Abbott,
Peter Dayan:
The Effect of Correlated Variability on the Accuracy of a Population Code.
Neural Computation 11(1): 91-101 (1999) |
| 32 | | Peter Dayan:
Recurrent Sampling Models for the Helmholtz Machine.
Neural Computation 11(3): 653-677 (1999) |
| 1998 |
|---|
| 31 | EE | Richard S. Zemel,
Peter Dayan:
Distributional Population Codes and Multiple Motion Models.
NIPS 1998: 174-182 |
| 30 | EE | Zhaoping Li,
Peter Dayan:
Computational Differences between Asymmetrical and Symmetrical Networks.
NIPS 1998: 274-280 |
| 29 | EE | Friedrich T. Sommer,
Peter Dayan:
Bayesian retrieval in associative memories with storage errors.
IEEE Transactions on Neural Networks 9(4): 705-713 (1998) |
| 28 | | Satinder P. Singh,
Peter Dayan:
Analytical Mean Squared Error Curves for Temporal Difference Learning.
Machine Learning 32(1): 5-40 (1998) |
| 27 | | Richard S. Zemel,
Peter Dayan,
Alexandre Pouget:
Probabilistic Interpretation of Population Codes.
Neural Computation 10(2): 403-430 (1998) |
| 26 | | Peter Dayan:
A Hierarchical Model of Binocular Rivalry.
Neural Computation 10(5): 1119-1135 (1998) |
| 1997 |
|---|
| 25 | | Richard S. Zemel,
Peter Dayan:
Combining Probabilistic Population Codes.
IJCAI 1997: 1114-1119 |
| 24 | | David J. Foster,
Richard G. M. Morris,
Peter Dayan:
Hippocampal Model of Rat Spatial Abilities Using Temporal Difference Learning.
NIPS 1997 |
| 23 | | Peter Dayan,
Theresa Long:
Statistical Models of Conditioning.
NIPS 1997 |
| 22 | EE | Peter Dayan,
Geoffrey E. Hinton:
Using Expectation-Maximization for Reinforcement Learning.
Neural Computation 9(2): 271-278 (1997) |
| 21 | EE | Radford M. Neal,
Peter Dayan:
Factor Analysis Using Delta-Rule Wake-Sleep Learning.
Neural Computation 9(8): 1781-1803 (1997) |
| 1996 |
|---|
| 20 | EE | Satinder P. Singh,
Peter Dayan:
Analytical Mean Squared Error Curves in Temporal Difference Learning.
NIPS 1996: 1054-1060 |
| 19 | EE | Maximilian Riesenhuber,
Peter Dayan:
Neural Models for Part-Whole Hierarchies.
NIPS 1996: 17-26 |
| 18 | EE | Peter Dayan:
A Hierarchical Model of Visual Rivalry.
NIPS 1996: 48-54 |
| 17 | EE | Richard S. Zemel,
Peter Dayan,
Alexandre Pouget:
Probabilistic Interpretation of Population Codes.
NIPS 1996: 676-684 |
| 16 | | Peter Dayan,
Terrence J. Sejnowski:
Exploration Bonuses and Dual Control.
Machine Learning 25(1): 5-22 (1996) |
| 15 | EE | Peter Dayan,
Geoffrey E. Hinton:
Varieties of Helmholtz Machine.
Neural Networks 9(8): 1385-1403 (1996) |
| 1995 |
|---|
| 14 | EE | Terrence J. Sejnowski,
Peter Dayan,
P. Read Montague:
Predictive Hebbian Learning.
COLT 1995: 15-18 |
| 13 | EE | Peter Dayan,
Satinder P. Singh:
Improving Policies without Measuring Merits.
NIPS 1995: 1059-1065 |
| 12 | EE | Brendan J. Frey,
Geoffrey E. Hinton,
Peter Dayan:
Does the Wake-sleep Algorithm Produce Good Density Estimators?
NIPS 1995: 661-667 |
| 11 | EE | Peter Dayan,
Geoffrey E. Hinton,
Radford M. Neal,
Richard S. Zemel:
The Helmholtz machine.
Neural Computation 7(5): 889-904 (1995) |
| 1994 |
|---|
| 10 | EE | Geoffrey E. Hinton,
Michael Revow,
Peter Dayan:
Recognizing Handwritten Digits Using Mixtures of Linear Models.
NIPS 1994: 1015-1022 |
| 9 | | Peter Dayan,
Terrence J. Sejnowski:
TD(lambda) Converges with Probability 1.
Machine Learning 14(1): 295-301 (1994) |
| 1993 |
|---|
| 8 | EE | P. Read Montague,
Peter Dayan,
Terrence J. Sejnowski:
Foraging in an Uncertain Environment Using Predictive Hebbian Learning.
NIPS 1993: 598-605 |
| 7 | EE | Nicol N. Schraudolph,
Peter Dayan,
Terrence J. Sejnowski:
Temporal Difference Learning of Position Evaluation in the Game of Go.
NIPS 1993: 817-824 |
| 1992 |
|---|
| 6 | EE | Peter Dayan,
Geoffrey E. Hinton:
Feudal Reinforcement Learning.
NIPS 1992: 271-278 |
| 5 | EE | P. Read Montague,
Peter Dayan,
Steven J. Nowlan,
Terrence J. Sejnowski:
Using Aperiodic Reinforcement for Directed Self-Organization During Development.
NIPS 1992: 969-976 |
| 4 | | Christopher J. C. H. Watkins,
Peter Dayan:
Technical Note Q-Learning.
Machine Learning 8: 279-292 (1992) |
| 3 | | Peter Dayan:
The Convergence of TD(lambda) for General lambda.
Machine Learning 8: 341-362 (1992) |
| 1991 |
|---|
| 2 | EE | Peter Dayan,
Geoffrey J. Goodhill:
Perturbing Hebbian Rules.
NIPS 1991: 19-26 |
| 1990 |
|---|
| 1 | EE | Peter Dayan:
Navigating Through Temporal Difference.
NIPS 1990: 464-470 |
