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Peter Dayan

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2008
73EEDebajyoti Ray, Brooks King-Casas, P. Read Montague, Peter Dayan: Bayesian Model of Behaviour in Economic Games. NIPS 2008: 1345-1352
72EEPeter Dayan: Load and Attentional Bayes. NIPS 2008: 369-376
71EEQuentin J. M. Huys, Joshua T. Vogelstein, Peter Dayan: Psychiatry: Insights into depression through normative decision-making models. NIPS 2008: 729-736
70EERama 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
69EEMáté Lengyel, Peter Dayan: Hippocampal Contributions to Control: The Third Way. NIPS 2007
68EEQuentin J. M. Huys, Richard S. Zemel, Rama Natarajan, Peter Dayan: Fast Population Coding. Neural Computation 19(2): 404-441 (2007)
2006
67EEMáté Lengyel, Peter Dayan: Uncertainty, phase and oscillatory hippocampal recall. NIPS 2006: 833-840
66EEAaron 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)
65EEPeter Dayan: Images, Frames, and Connectionist Hierarchies. Neural Computation 18(10): 2293-2319 (2006)
64EEOdelia 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)
63EEPeter 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)
62EEZhaoping Li, Peter Dayan: Pre-attentive visual selection. Neural Networks 19(9): 1437-1439 (2006)
2005
61EEMiguel Á. Carreira-Perpiñán, Peter Dayan, Geoffrey J. Goodhill: Differential Priors for Elastic Nets. IDEAL 2005: 335-342
60EEOdelia Schwartz, Terrence J. Sejnowski, Peter Dayan: A Bayesian Framework for Tilt Perception and Confidence. NIPS 2005
59EEYael Niv, Nathaniel D. Daw, Peter Dayan: How fast to work: Response vigor, motivation and tonic dopamine. NIPS 2005
58EEPeter Dayan, Angela J. Yu: Norepinephrine and Neural Interrupts. NIPS 2005
2004
57EEOdelia Schwartz, Terrence J. Sejnowski, Peter Dayan: Assignment of Multiplicative Mixtures in Natural Images. NIPS 2004
56EEAngela J. Yu, Peter Dayan: Inference, Attention, and Decision in a Bayesian Neural Architecture. NIPS 2004
55EERichard S. Zemel, Quentin J. M. Huys, Rama Natarajan, Peter Dayan: Probabilistic Computation in Spiking Populations. NIPS 2004
54EEMáté Lengyel, Peter Dayan: Rate- and Phase-coded Autoassociative Memory. NIPS 2004
2003
53EEAaron 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
52EEPeter Dayan, Michael Häusser: Plasticity Kernels and Temporal Statistics. NIPS 2003
51EEManeesh Sahani, Peter Dayan: Doubly Distributional Population Codes: Simultaneous Representation of Uncertainty and Multiplicity. Neural Computation 15(10): 2255-2279 (2003)
2002
50EEAngela J. Yu, Peter Dayan: Expected and Unexpected Uncertainty: ACh and NE in the Neocortex. NIPS 2002: 157-164
49EESzabolcs Káli, Peter Dayan: Replay, Repair and Consolidation. NIPS 2002: 19-26
48EEPeter 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)
46EEKenji Doya, Peter Dayan, Michael E. Hasselmo: Introduction for 2002 Special Issue: Computational Models of Neuromodulation. Neural Networks 15(4-6): 475-477 (2002)
45EESham Kakade, Peter Dayan: Dopamine: generalization and bonuses. Neural Networks 15(4-6): 549-559 (2002)
44EENathaniel D. Daw, Sham Kakade, Peter Dayan: Opponent interactions between serotonin and dopamine. Neural Networks 15(4-6): 603-616 (2002)
43EEAngela J. Yu, Peter Dayan: Acetylcholine in cortical inference. Neural Networks 15(4-6): 719-730 (2002)
2001
42EEPeter Dayan: Motivated Reinforcement Learning. NIPS 2001: 11-18
41EEPeter Dayan, Angela J. Yu: ACh, Uncertainty, and Cortical Inference. NIPS 2001: 189-196
40EESzabolcs 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
34EESham 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
31EERichard S. Zemel, Peter Dayan: Distributional Population Codes and Multiple Motion Models. NIPS 1998: 174-182
30EEZhaoping Li, Peter Dayan: Computational Differences between Asymmetrical and Symmetrical Networks. NIPS 1998: 274-280
29EEFriedrich 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
22EEPeter Dayan, Geoffrey E. Hinton: Using Expectation-Maximization for Reinforcement Learning. Neural Computation 9(2): 271-278 (1997)
21EERadford M. Neal, Peter Dayan: Factor Analysis Using Delta-Rule Wake-Sleep Learning. Neural Computation 9(8): 1781-1803 (1997)
1996
20EESatinder P. Singh, Peter Dayan: Analytical Mean Squared Error Curves in Temporal Difference Learning. NIPS 1996: 1054-1060
19EEMaximilian Riesenhuber, Peter Dayan: Neural Models for Part-Whole Hierarchies. NIPS 1996: 17-26
18EEPeter Dayan: A Hierarchical Model of Visual Rivalry. NIPS 1996: 48-54
17EERichard 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)
15EEPeter Dayan, Geoffrey E. Hinton: Varieties of Helmholtz Machine. Neural Networks 9(8): 1385-1403 (1996)
1995
14EETerrence J. Sejnowski, Peter Dayan, P. Read Montague: Predictive Hebbian Learning. COLT 1995: 15-18
13EEPeter Dayan, Satinder P. Singh: Improving Policies without Measuring Merits. NIPS 1995: 1059-1065
12EEBrendan J. Frey, Geoffrey E. Hinton, Peter Dayan: Does the Wake-sleep Algorithm Produce Good Density Estimators? NIPS 1995: 661-667
11EEPeter Dayan, Geoffrey E. Hinton, Radford M. Neal, Richard S. Zemel: The Helmholtz machine. Neural Computation 7(5): 889-904 (1995)
1994
10EEGeoffrey 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
8EEP. Read Montague, Peter Dayan, Terrence J. Sejnowski: Foraging in an Uncertain Environment Using Predictive Hebbian Learning. NIPS 1993: 598-605
7EENicol N. Schraudolph, Peter Dayan, Terrence J. Sejnowski: Temporal Difference Learning of Position Evaluation in the Game of Go. NIPS 1993: 817-824
1992
6EEPeter Dayan, Geoffrey E. Hinton: Feudal Reinforcement Learning. NIPS 1992: 271-278
5EEP. 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
2EEPeter Dayan, Geoffrey J. Goodhill: Perturbing Hebbian Rules. NIPS 1991: 19-26
1990
1EEPeter Dayan: Navigating Through Temporal Difference. NIPS 1990: 464-470

Coauthor Index

1L. F. Abbott [33]
2Miguel Á. Carreira-Perpiñán [61]
3Nathaniel D. Daw [44] [59] [63]
4Gregoire Deback [48]
5Kenji Doya [46]
6David J. Foster [24] [47]
7Brendan J. Frey [12]
8Geoffrey J. Goodhill [2] [61]
9Aaron J. Gruber [53] [66]
10Boris S. Gutkin [53] [66]
11Michael E. Hasselmo [46]
12Michael Häusser [52]
13Geoffrey E. Hinton [6] [10] [11] [12] [15] [22]
14Quentin J. M. Huys [55] [68] [70] [71]
15Sham M. Kakade (Sham Kakade) [34] [35] [39] [44] [45]
16Szabolcs Káli [37] [40] [49]
17Brooks King-Casas [73]
18Máté Lengyel [54] [67] [69]
19Zhaoping Li [30] [36] [62]
20Theresa Long [23]
21P. Read Montague [5] [8] [14] [73]
22Richard G. M. Morris [24]
23Rama Natarajan [55] [68] [70]
24Radford M. Neal [11] [21]
25Yael Niv [59] [63]
26Steven J. Nowlan [5]
27Alexandre Pouget [17] [27]
28Debajyoti Ray [73]
29Michael Revow [10]
30Maximilian Riesenhuber [19]
31Maneesh Sahani [48] [51]
32Nicol N. Schraudolph [7]
33Odelia Schwartz [57] [60] [64]
34Terrence J. Sejnowski [5] [7] [8] [9] [14] [16] [57] [60] [64]
35Ben Seymour [63]
36Satinder P. Singh [13] [20] [28]
37Sara A. Solla [53] [66]
38Friedrich T. Sommer [29]
39Joshua T. Vogelstein [71]
40Christopher J. C. H. Watkins [4]
41Angela J. Yu [41] [43] [50] [56] [58]
42Richard S. Zemel [11] [17] [25] [27] [31] [55] [68] [70]

Colors in the list of coauthors

Copyright © Sun May 17 03:24:02 2009 by Michael Ley (ley@uni-trier.de)