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Peter Dayan
Peter S. Dayan
Person information
- affiliation: Max Planck Institute for Biological Cybernetics, Tübingen, Germany
- affiliation (former): University College London, Gatsby Computational Neuroscience Unit
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2020 – today
- 2024
- [i19]Tankred Saanum, Peter Dayan, Eric Schulz:
Predicting the Future with Simple World Models. CoRR abs/2401.17835 (2024) - [i18]Kevin Shen, Surabhi S. Nath, Aenne Brielmann, Peter Dayan:
Simplicity in Complexity. CoRR abs/2403.03134 (2024) - [i17]Surabhi S. Nath, Peter Dayan, Claire Stevenson:
Characterising the Creative Process in Humans and Large Language Models. CoRR abs/2405.00899 (2024) - [i16]Nitay Alon, Lion Schulz, Joseph M. Barnby, Jeffrey S. Rosenschein, Peter Dayan:
Detecting and Deterring Manipulation in a Cognitive Hierarchy. CoRR abs/2405.01870 (2024) - 2023
- [j89]Kevin Lloyd
, Peter Dayan:
Reframing dopamine: A controlled controller at the limbic-motor interface. PLoS Comput. Biol. 19(10) (2023) - [c71]Noémi Élteto, Peter Dayan:
Habits of Mind: Reusing Action Sequences for Efficient Planning. CogSci 2023 - [c70]Valerio Rubino, Mani Hamidi, Peter Dayan, Charley M. Wu:
Compositionality under time pressure. CogSci 2023 - [c69]Tankred Saanum, Noémi Élteto, Peter Dayan, Marcel Binz, Eric Schulz:
Reinforcement Learning with Simple Sequence Priors. NeurIPS 2023 - [i15]Tankred Saanum, Noémi Élteto, Peter Dayan, Marcel Binz, Eric Schulz:
Reinforcement Learning with Simple Sequence Priors. CoRR abs/2305.17109 (2023) - [i14]Noémi Élteto, Peter Dayan:
Habits of Mind: Reusing Action Sequences for Efficient Planning. CoRR abs/2306.05298 (2023) - [i13]Chris Gagne, Peter Dayan:
The Inner Sentiments of a Thought. CoRR abs/2307.01784 (2023) - 2022
- [j88]Georgy Antonov
Optimism and pessimism in optimised replay. PLoS Comput. Biol. 18(1) (2022) - [j87]Rachit Dubey
The pursuit of happiness: A reinforcement learning perspective on habituation and comparisons. PLoS Comput. Biol. 18(8) (2022) - [j86]Christopher Gagne
Biased belief priors versus biased belief updating: Differential correlates of depression and anxiety. PLoS Comput. Biol. 18(8) (2022) - [j85]Elena Zamfir
Interactions between attributions and beliefs at trial-by-trial level: Evidence from a novel computer game task. PLoS Comput. Biol. 18(9): 1009920 (2022) - [j84]Sahiti Chebolu
Vigilance, arousal, and acetylcholine: Optimal control of attention in a simple detection task. PLoS Comput. Biol. 18(10): 1010642 (2022) - [j83]Noémi Élteto
Tracking human skill learning with a hierarchical Bayesian sequence model. PLoS Comput. Biol. 18(11): 1009866 (2022) - [c68]Moein Khajehnejad, Forough Habibollahi, Richard Nock, Ehsan Arabzadeh, Peter Dayan, Amir Dezfouli:
Neural Network Poisson Models for Behavioural and Neural Spike Train Data. ICML 2022: 10974-10996 - 2021
- [j82]Vikki Neville
Using Primary Reinforcement to Enhance Translatability of a Human Affect and Decision-Making Judgment Bias Task. J. Cogn. Neurosci. 33(12): 2523-2535 (2021) - [j81]Vikki Neville
Dissecting the links between reward and loss, decision-making, and self-reported affect using a computational approach. PLoS Comput. Biol. 17(1) (2021) - [j80]Federico Mancinelli, Jonathan P. Roiser
Internality and the internalisation of failure: Evidence from a novel task. PLoS Comput. Biol. 17(7) (2021) - [c67]Sebastian A. Bruijns, International Brain Laboratory, Peter Dayan:
Exploring learning trajectories with dynamic infinite hidden Markov models. CogSci 2021 - [c66]Noémi Élteto, Dezso Németh, Karolina Janacsek, Peter Dayan:
Tracking the Unknown: Modeling Long-Term Implicit Skill Acquisition as Non-Parametric Bayesian Sequence Learning. CogSci 2021 - [c65]Lion Schulz, Stephen M. Fleming, Peter Dayan:
Confidence in control: Metacognitive computations for information search. CogSci 2021 - [c64]Sanjeevan Ahilan, Peter Dayan:
Correcting experience replay for multi-agent communication. ICLR 2021 - [c63]Chris Gagne, Peter Dayan:
Two steps to risk sensitivity. NeurIPS 2021: 22209-22220 - [i12]Chris Gagne, Peter Dayan:
Two steps to risk sensitivity. CoRR abs/2111.06803 (2021) - [i11]Chris Gagne, Peter Dayan:
Catastrophe, Compounding & Consistency in Choice. CoRR abs/2111.06804 (2021) - 2020
- [j79]Mustafa Ozkaynak, Noel Metcalf, Daniel M. Cohen
Considerations for Designing EHR-Embedded Clinical Decision Support Systems for Antimicrobial Stewardship in Pediatric Emergency Departments. Appl. Clin. Inform. 11(04): 589-597 (2020) - [j78]Bruno Miranda
Combined model-free and model-sensitive reinforcement learning in non-human primates. PLoS Comput. Biol. 16(6) (2020) - [c62]Pablo Tano, Peter Dayan, Alexandre Pouget:
A Local Temporal Difference Code for Distributional Reinforcement Learning. NeurIPS 2020 - [c61]Eren Sezener, Peter Dayan:
Static and Dynamic Values of Computation in MCTS. UAI 2020: 31-40 - [i10]Eren Sezener, Peter Dayan:
Static and Dynamic Values of Computation in MCTS. CoRR abs/2002.04335 (2020) - [i9]Sanjeevan Ahilan, Peter Dayan:
Correcting Experience Replay for Multi-Agent Communication. CoRR abs/2010.01192 (2020)
2010 – 2019
- 2019
- [j77]Amir Homayoun Javadi, Eva Zita Patai, Eugenia Marin-Garcia
Prefrontal Dynamics Associated with Efficient Detours and Shortcuts: A Combined Functional Magnetic Resonance Imaging and Magnetoencenphalography Study. J. Cogn. Neurosci. 31(8): 1227-1247 (2019) - [j76]Sanjeevan Ahilan
Learning to use past evidence in a sophisticated world model. PLoS Comput. Biol. 15(6) (2019) - [j75]Amir Dezfouli
Models that learn how humans learn: The case of decision-making and its disorders. PLoS Comput. Biol. 15(6) (2019) - [j74]Toby Wise
A computational account of threat-related attentional bias. PLoS Comput. Biol. 15(10) (2019) - [c60]Amir Dezfouli, Hassan Ashtiani, Omar Ghattas, Richard Nock, Peter Dayan, Cheng Soon Ong:
Disentangled behavioural representations. NeurIPS 2019: 2251-2260 - [i8]Sanjeevan Ahilan, Peter Dayan:
Feudal Multi-Agent Hierarchies for Cooperative Reinforcement Learning. CoRR abs/1901.08492 (2019) - 2018
- [j73]Mustafa Ozkaynak, Danny Wu, Katia Hannah, Peter Dayan, Rakesh Mistry:
Examining Workflow in a Pediatric Emergency Department to Develop a Clinical Decision Support for an Antimicrobial Stewardship Program. Appl. Clin. Inform. 09(02): 248-260 (2018) - [j72]Ruth Masterson Creber
Applying the RE-AIM Framework for the Evaluation of a Clinical Decision Support Tool for Pediatric Head Trauma: A Mixed-Methods Study. Appl. Clin. Inform. 09(03): 693-703 (2018) - [j71]Francesco Rigoli, Benjamin Chew
Learning Contextual Reward Expectations for Value Adaptation. J. Cogn. Neurosci. 30(1) (2018) - [j70]Kevin Lloyd
Interrupting behaviour: Minimizing decision costs via temporal commitment and low-level interrupts. PLoS Comput. Biol. 14(1) (2018) - [j69]Andreas Hula
A model of risk and mental state shifts during social interaction. PLoS Comput. Biol. 14(2) (2018) - [j68]Brendan A. Bicknell
Control of neurite growth and guidance by an inhibitory cell-body signal. PLoS Comput. Biol. 14(6) (2018) - [j67]Michael Moutoussis
Change, stability, and instability in the Pavlovian guidance of behaviour from adolescence to young adulthood. PLoS Comput. Biol. 14(12) (2018) - [c59]Jack W. Rae, Chris Dyer, Peter Dayan, Timothy P. Lillicrap:
Fast Parametric Learning with Activation Memorization. ICML 2018: 4225-4234 - [c58]Amir Dezfouli, Richard W. Morris, Fabio T. Ramos, Peter Dayan, Bernard W. Balleine:
Integrated accounts of behavioral and neuroimaging data using flexible recurrent neural network models. NeurIPS 2018: 4233-4242 - [i7]Jack W. Rae, Chris Dyer, Peter Dayan, Timothy P. Lillicrap:
Fast Parametric Learning with Activation Memorization. CoRR abs/1803.10049 (2018) - [i6]Theofanis Karaletsos, Peter Dayan, Zoubin Ghahramani:
Probabilistic Meta-Representations Of Neural Networks. CoRR abs/1810.00555 (2018) - 2017
- [j66]Tobias U. Hauser
Increased decision thresholds enhance information gathering performance in juvenile Obsessive-Compulsive Disorder (OCD). PLoS Comput. Biol. 13(4) (2017) - [c57]David Raposo, Peter Dayan, Demis Hassabis, Peter W. Battaglia:
A model of structure learning, inference, and generation for scene understanding. CogSci 2017 - [i5]Ivo Danihelka, Balaji Lakshminarayanan, Benigno Uria, Daan Wierstra, Peter Dayan:
Comparison of Maximum Likelihood and GAN-based training of Real NVPs. CoRR abs/1705.05263 (2017) - 2016
- [j65]Marguerite Swietlik, Sara J. Deakyne Davies, Jeffrey Hoffman, Robert Grundmeier, Marilyn D. Paterno, Beatriz H. Rocha, Molly Schaeffer, Deepika Pabbathi, Evaline Alessandrini, Dustin Ballard, Howard Goldberg, Nathan Kuppermann, Peter Dayan, Eric Tham:
Clinical Decision Support for a Multicenter Trial of Pediatric Head Trauma. Appl. Clin. Inform. 07(02): 534-542 (2016) - [j64]Howard S. Goldberg
Use of a remote clinical decision support service for a multicenter trial to implement prediction rules for children with minor blunt head trauma. Int. J. Medical Informatics 87: 101-110 (2016) - [j63]Francesco Rigoli, Benjamin Chew
The Dopaminergic Midbrain Mediates an Effect of Average Reward on Pavlovian Vigor. J. Cogn. Neurosci. 28(9): 1303-1317 (2016) - [j62]Francesco Rigoli, Robb B. Rutledge
The influence of contextual reward statistics on risk preference. NeuroImage 128: 74-84 (2016) - [j61]Francesco Rigoli, Benjamin Chew
Multiple value signals in dopaminergic midbrain and their role in avoidance contexts. NeuroImage 135: 197-203 (2016) - [j60]Michael Moutoussis
How People Use Social Information to Find out What to Want in the Paradigmatic Case of Inter-temporal Preferences. PLoS Comput. Biol. 12(7) (2016) - [i4]Neil R. Bramley, Peter Dayan, Thomas L. Griffiths, David A. Lagnado:
Formalizing Neurath's Ship: Approximate Algorithms for Online Causal Learning. CoRR abs/1609.04212 (2016) - 2015
- [j59]Loic Matthey, Paul M. Bays
A Probabilistic Palimpsest Model of Visual Short-term Memory. PLoS Comput. Biol. 11(1) (2015) - [j58]Giles W. Story
Anticipation and Choice Heuristics in the Dynamic Consumption of Pain Relief. PLoS Comput. Biol. 11(3) (2015) - [j57]Andreas Hula
Monte Carlo Planning Method Estimates Planning Horizons during Interactive Social Exchange. PLoS Comput. Biol. 11(6) (2015) - [j56]Thomas E. Akam, Rui Costa, Peter Dayan
Simple Plans or Sophisticated Habits? State, Transition and Learning Interactions in the Two-Step Task. PLoS Comput. Biol. 11(12) (2015) - [j55]Kevin Lloyd
Tamping Ramping: Algorithmic, Implementational, and Computational Explanations of Phasic Dopamine Signals in the Accumbens. PLoS Comput. Biol. 11(12) (2015) - [c56]Neil Bramley, Peter Dayan, David A. Lagnado:
Staying afloat on Neurath's boat - Heuristics for sequential causal learning. CogSci 2015 - 2014
- [j54]Cristina Savin
Optimal Recall from Bounded Metaplastic Synapses: Predicting Functional Adaptations in Hippocampal Area CA3. PLoS Comput. Biol. 10(2) (2014) - [j53]Ritwik K. Niyogi, Peter Shizgal
Some Work and Some Play: Microscopic and Macroscopic Approaches to Labor and Leisure. PLoS Comput. Biol. 10(12) (2014) - [j52]Peter Dayan
Rationalizable Irrationalities of Choice. Top. Cogn. Sci. 6(2): 204-228 (2014) - [c55]Arthur Guez, Nicolas Heess, David Silver, Peter Dayan:
Bayes-Adaptive Simulation-based Search with Value Function Approximation. NIPS 2014: 451-459 - [i3]Arthur Guez, David Silver, Peter Dayan:
Better Optimism By Bayes: Adaptive Planning with Rich Models. CoRR abs/1402.1958 (2014) - 2013
- [j51]Arthur Guez, David Silver, Peter Dayan:
Scalable and Efficient Bayes-Adaptive Reinforcement Learning Based on Monte-Carlo Tree Search. J. Artif. Intell. Res. 48: 841-883 (2013) - [j50]Barbara Sheehan, Lise E. Nigrovic
Informing the design of clinical decision support services for evaluation of children with minor blunt head trauma in the emergency department: A sociotechnical analysis. J. Biomed. Informatics 46(5): 905-913 (2013) - [j49]Jonathan J. Hunt, Peter Dayan
Sparse Coding Can Predict Primary Visual Cortex Receptive Field Changes Induced by Abnormal Visual Input. PLoS Comput. Biol. 9(5) (2013) - [c54]Samuel Gershman, Joshua B. Tenenbaum, Alexandre Pouget, Matthew M. Botvinick, Peter Dayan:
Structured cognitive representations and complex inference in neural systems. CogSci 2013 - [c53]Cristina Savin, Peter Dayan, Máté Lengyel:
Correlations strike back (again): the case of associative memory retrieval. NIPS 2013: 288-296 - [p1]Peter Dayan:
Exploration from Generalization Mediated by Multiple Controllers. Intrinsically Motivated Learning in Natural and Artificial Systems 2013: 73-91 - 2012
- [j48]Marc Guitart-Masip
Go and no-go learning in reward and punishment: Interactions between affect and effect. NeuroImage 62(1): 154-166 (2012) - [j47]Ruben Coen Cagli
Cortical Surround Interactions and Perceptual Salience via Natural Scene Statistics. PLoS Comput. Biol. 8(3) (2012) - [j46]Quentin J. M. Huys
Bonsai Trees in Your Head: How the Pavlovian System Sculpts Goal-Directed Choices by Pruning Decision Trees. PLoS Comput. Biol. 8(3) (2012) - [j45]Ting Xiang, Debajyoti Ray, Terry Lohrenz, Peter Dayan
Computational Phenotyping of Two-Person Interactions Reveals Differential Neural Response to Depth-of-Thought. PLoS Comput. Biol. 8(12) (2012) - [c52]Jonathan Malmaud, Joshua B. Tenenbaum, Peter Dayan, Laurence T. Maloney, Edward Vul, Nick Chater:
Computational, Cognitive, and Neural Models of Decision-making Biases. CogSci 2012 - [c51]Magda Osman, Maarten Speekenbrink, Peter Dayan, Masataka Watanabe, Nigel Harvey:
Dynamic decision making: neuronal, computational, and cognitive underpinnings. CogSci 2012 - [c50]Barbara Sheehan, Lise E. Nigrovic, Peter S. Dayan, Nathan Kuppermann, Dustin W. Ballard, Evaline Alessandrini, Lalit Bajaj, Howard Goldberg, Jeffrey Hoffman, Steven R. Offerman, Dustin G. Mark, Marguerite Swietlik, Eric Tham, Leah Tzimenatos, David R. Vinson:
An Activity Theory Based Analysis of Work Activities in the Emergency Department. Nursing Informatics 2012 - [c49]Arthur Guez, David Silver, Peter Dayan:
Efficient Bayes-Adaptive Reinforcement Learning using Sample-Based Search. NIPS 2012: 1034-1042 - [i2]Arthur Guez, David Silver, Peter Dayan:
Efficient Bayes-Adaptive Reinforcement Learning using Sample-Based Search. CoRR abs/1205.3109 (2012) - 2011
- [j44]Marc Guitart-Masip
Vigor in the Face of Fluctuating Rates of Reward: An Experimental Examination. J. Cogn. Neurosci. 23(12): 3933-3938 (2011) - [j43]Duncan Mortimer, Peter Dayan
Bayes-Optimal Chemotaxis. Neural Comput. 23(2): 336-373 (2011) - [j42]Quentin J. M. Huys
Disentangling the Roles of Approach, Activation and Valence in Instrumental and Pavlovian Responding. PLoS Comput. Biol. 7(4) (2011) - [c48]Cristina Savin, Peter Dayan, Máté Lengyel:
Two is better than one: distinct roles for familiarity and recollection in retrieving palimpsest memories. NIPS 2011: 1305-1313 - [i1]Jeremy L. Wyatt, Peter Dayan, Ales Leonardis, Jan Peters:
Exploration and Curiosity in Robot Learning and Inference (Dagstuhl Seminar 11131). Dagstuhl Reports 1(3): 67-95 (2011) - 2010
- [j41]Reza Moazzezi, Peter Dayan
Change-Based Inference in Attractor Nets: Linear Analysis. Neural Comput. 22(12): 3036-3061 (2010) - [j40]Ulrik R. Beierholm
Pavlovian-Instrumental Interaction in 'Observing Behavior'. PLoS Comput. Biol. 6(9) (2010)
2000 – 2009
- 2009
- [j39]Peter Dayan
Goal-directed control and its antipodes. Neural Networks 22(3): 213-219 (2009) - [c47]Ruben Coen Cagli, Peter Dayan, Odelia Schwartz:
Statistical Models of Linear and Nonlinear Contextual Interactions in Early Visual Processing. NIPS 2009: 369-377 - [c46]Jean-Pascal Pfister, Peter Dayan, Máté Lengyel:
Know Thy Neighbour: A Normative Theory of Synaptic Depression. NIPS 2009: 1464-1472 - 2008
- [j38]Marzia De Lucia
A novel method for automated classification of epileptiform activity in the human electroencephalogram-based on independent component analysis. Medical Biol. Eng. Comput. 46(3): 263-272 (2008) - [j37]Rama Natarajan, Quentin J. M. Huys
Encoding and Decoding Spikes for Dynamic Stimuli. Neural Comput. 20(9): 2325-2360 (2008) - [j36]Peter Dayan
Serotonin, Inhibition, and Negative Mood. PLoS Comput. Biol. 4(2) (2008) - [c45]Peter Dayan:
Load and Attentional Bayes. NIPS 2008: 369-376 - [c44]Quentin J. M. Huys, Joshua T. Vogelstein, Peter Dayan:
Psychiatry: Insights into depression through normative decision-making models. NIPS 2008: 729-736 - [c43]Debajyoti Ray, Brooks King-Casas, P. Read Montague, Peter Dayan:
Bayesian Model of Behaviour in Economic Games. NIPS 2008: 1345-1352 - 2007
- [j35]Peter Dayan
Bilinearity, rules, and prefrontal cortex. Frontiers Comput. Neurosci. 1: 1 (2007) - [j34]Quentin J. M. Huys
Fast Population Coding. Neural Comput. 19(2): 404-441 (2007) - [c42]Máté Lengyel, Peter Dayan:
Hippocampal Contributions to Control: The Third Way. NIPS 2007: 889-896 - 2006
- [j33]Aaron J. Gruber
Dopamine modulation in the basal ganglia locks the gate to working memory. J. Comput. Neurosci. 20(2): 153-166 (2006) - [j32]Peter Dayan
Images, Frames, and Connectionist Hierarchies. Neural Comput. 18(10): 2293-2319 (2006) - [j31]Odelia Schwartz, Terrence J. Sejnowski, Peter Dayan
Soft Mixer Assignment in a Hierarchical Generative Model of Natural Scene Statistics. Neural Comput. 18(11): 2680-2718 (2006) - [j30]Peter Dayan
The misbehavior of value and the discipline of the will. Neural Networks 19(8): 1153-1160 (2006) - [j29]Zhaoping Li, Peter Dayan
Pre-attentive visual selection. Neural Networks 19(9): 1437-1439 (2006) - [c41]Máté Lengyel, Peter Dayan:
Uncertainty, phase and oscillatory hippocampal recall. NIPS 2006: 833-840 - 2005
- [c40]Miguel Á. Carreira-Perpiñán, Peter Dayan, Geoffrey J. Goodhill:
Differential Priors for Elastic Nets. IDEAL 2005: 335-342 - [c39]Peter Dayan, Angela J. Yu:
Norepinephrine and Neural Interrupts. NIPS 2005: 243-250 - [c38]Yael Niv, Nathaniel D. Daw, Peter Dayan:
How fast to work: Response vigor, motivation and tonic dopamine. NIPS 2005: 1019-1026 - [c37]Odelia Schwartz, Terrence J. Sejnowski, Peter Dayan:
A Bayesian Framework for Tilt Perception and Confidence. NIPS 2005: 1201-1208 - 2004
- [c36]Máté Lengyel, Peter Dayan:
Rate- and Phase-coded Autoassociative Memory. NIPS 2004: 769-776 - [c35]Odelia Schwartz, Terrence J. Sejnowski, Peter Dayan:
Assignment of Multiplicative Mixtures in Natural Images. NIPS 2004: 1217-1224 - [c34]Angela J. Yu, Peter Dayan:
Inference, Attention, and Decision in a Bayesian Neural Architecture. NIPS 2004: 1577-1584 - [c33]Richard S. Zemel, Quentin J. M. Huys, Rama Natarajan, Peter Dayan:
Probabilistic Computation in Spiking Populations. NIPS 2004: 1609-1616 - 2003
- [j28]Maneesh Sahani, Peter Dayan
Doubly Distributional Population Codes: Simultaneous Representation of Uncertainty and Multiplicity. Neural Comput. 15(10): 2255-2279 (2003) - [c32]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: 1271-1278 - [c31]Peter Dayan, Michael Häusser:
Plasticity Kernels and Temporal Statistics. NIPS 2003: 1303-1310 - 2002
- [j27]David J. Foster, Peter Dayan
Structure in the Space of Value Functions. Mach. Learn. 49(2-3): 325-346 (2002) - [j26]Kenji Doya
Introduction for 2002 Special Issue: Computational Models of Neuromodulation. Neural Networks 15(4-6): 475-477 (2002) - [j25]Sham M. Kakade, Peter Dayan
Dopamine: generalization and bonuses. Neural Networks 15(4-6): 549-559 (2002) - [j24]Nathaniel D. Daw
Opponent interactions between serotonin and dopamine. Neural Networks 15(4-6): 603-616 (2002) - [j23]Angela J. Yu, Peter Dayan
Acetylcholine in cortical inference. Neural Networks 15(4-6): 719-730 (2002) - [c30]Szabolcs Káli, Peter Dayan:
Replay, Repair and Consolidation. NIPS 2002: 19-26 - [c29]Angela J. Yu, Peter Dayan:
Expected and Unexpected Uncertainty: ACh and NE in the Neocortex. NIPS 2002: 157-164 - [c28]Peter Dayan, Maneesh Sahani, Gregoire Deback:
Adaptation and Unsupervised Learning. NIPS 2002: 221-228 - 2001
- [j22]Szabolcs Káli
A familiarity-based learning procedure for the establishment of place fields in area CA3 of the rat hippocampus. Neurocomputing 38-40: 691-695 (2001) - [c27]Peter Dayan:
Motivated Reinforcement Learning. NIPS 2001: 11-18 - [c26]Peter Dayan, Angela J. Yu:
ACh, Uncertainty, and Cortical Inference. NIPS 2001: 189-196 - 2000
- [c25]Szabolcs Káli, Peter Dayan:
Hippocampally-Dependent Consolidation in a Hierarchical Model of Neocortex. NIPS 2000: 24-30 - [c24]Zhaoping Li, Peter Dayan:
Position Variance, Recurrence and Perceptual Learning. NIPS 2000: 31-37 - [c23]Sham M. Kakade, Peter Dayan:
Dopamine Bonuses. NIPS 2000: 131-137 - [c22]Peter Dayan:
Competition and Arbors in Ocular Dominance. NIPS 2000: 203-209 - [c21]Peter Dayan, Sham M. Kakade:
Explaining Away in Weight Space. NIPS 2000: 451-457
1990 – 1999
- 1999
- [j21]L. F. Abbott, Peter Dayan:
The Effect of Correlated Variability on the Accuracy of a Population Code. Neural Comput. 11(1): 91-101 (1999) - [j20]Peter Dayan:
Recurrent Sampling Models for the Helmholtz Machine. Neural Comput. 11(3): 653-677 (1999) - [c20]Sham M. Kakade, Peter Dayan:
Acquisition in Autoshaping. NIPS 1999: 24-30 - 1998
- [j19]Satinder Singh, Peter Dayan
Analytical Mean Squared Error Curves for Temporal Difference Learning. Mach. Learn. 32(1): 5-40 (1998) - [j18]Richard S. Zemel, Peter Dayan, Alexandre Pouget
Probabilistic Interpretation of Population Codes. Neural Comput. 10(2): 403-430 (1998) - [j17]Peter Dayan:
A Hierarchical Model of Binocular Rivalry. Neural Comput. 10(5): 1119-1135 (1998) - [j16]Friedrich T. Sommer, Peter Dayan
Bayesian retrieval in associative memories with storage errors. IEEE Trans. Neural Networks 9(4): 705-713 (1998) - [c19]Richard S. Zemel, Peter Dayan:
Distributional Population Codes and Multiple Motion Models. NIPS 1998: 174-182 - [c18]Zhaoping Li, Peter Dayan:
Computational Differences between Asymmetrical and Symmetrical Networks. NIPS 1998: 274-280 - 1997
- [j15]Peter Dayan, Geoffrey E. Hinton:
Using Expectation-Maximization for Reinforcement Learning. Neural Comput. 9(2): 271-278 (1997) - [j14]Radford M. Neal, Peter Dayan:
Factor Analysis Using Delta-Rule Wake-Sleep Learning. Neural Comput. 9(8): 1781-1803 (1997) - [j13]Geoffrey E. Hinton, Peter Dayan, Michael Revow:
Modeling the manifolds of images of handwritten digits. IEEE Trans. Neural Networks 8(1): 65-74 (1997) - [c17]Richard S. Zemel, Peter Dayan:
Combining Probabilistic Population Codes. IJCAI 1997: 1114-1119 - [c16]Peter Dayan, Theresa Long:
Statistical Models of Conditioning. NIPS 1997: 117-123 - [c15]David J. Foster, Richard G. M. Morris, Peter Dayan:
Hippocampal Model of Rat Spatial Abilities Using Temporal Difference Learning. NIPS 1997: 145-151 - 1996
- [j12]Peter Dayan, Terrence J. Sejnowski:
Exploration Bonuses and Dual Control. Mach. Learn. 25(1): 5-22 (1996) - [j11]Peter Dayan
Varieties of Helmholtz Machine. Neural Networks 9(8): 1385-1403 (1996) - [c14]Maximilian Riesenhuber, Peter Dayan:
Neural Models for Part-Whole Hierarchies. NIPS 1996: 17-26 - [c13]Peter Dayan:
A Hierarchical Model of Visual Rivalry. NIPS 1996: 48-54 - [c12]Richard S. Zemel, Peter Dayan, Alexandre Pouget:
Probabilistic Interpretation of Population Codes. NIPS 1996: 676-684 - [c11]Satinder Singh, Peter Dayan:
Analytical Mean Squared Error Curves in Temporal Difference Learning. NIPS 1996: 1054-1060 - 1995
- [j10]Peter Dayan, Richard S. Zemel:
Competition and Multiple Cause Models. Neural Comput. 7(3): 565-579 (1995) - [j9]Peter Dayan, Geoffrey E. Hinton, Radford M. Neal, Richard S. Zemel:
The Helmholtz machine. Neural Comput. 7(5): 889-904 (1995) - [c10]Terrence J. Sejnowski, Peter Dayan, P. Read Montague:
Predictive Hebbian Learning. COLT 1995: 15-18 - [c9]Brendan J. Frey, Geoffrey E. Hinton, Peter Dayan:
Does the Wake-sleep Algorithm Produce Good Density Estimators? NIPS 1995: 661-667 - [c8]Peter Dayan, Satinder Singh:
Improving Policies without Measuring Merits. NIPS 1995: 1059-1065 - 1994
- [j8]Peter Dayan, Terrence J. Sejnowski:
TD(lambda) Converges with Probability 1. Mach. Learn. 14(1): 295-301 (1994) - [c7]Geoffrey E. Hinton, Michael Revow, Peter Dayan:
Recognizing Handwritten Digits Using Mixtures of Linear Models. NIPS 1994: 1015-1022 - 1993
- [j7]Peter Dayan, Terrence J. Sejnowski:
The Variance of Covariance Rules for Associative Matrix Memories and Reinforcement Learning. Neural Comput. 5(2): 205-209 (1993) - [j6]Peter Dayan:
Arbitrary Elastic Topologies and Ocular Dominance. Neural Comput. 5(3): 392-401 (1993) - [j5]Peter Dayan
Improving Generalization for Temporal Difference Learning: The Successor Representation. Neural Comput. 5(4): 613-624 (1993) - [c6]P. Read Montague, Peter Dayan, Terrence J. Sejnowski:
Foraging in an Uncertain Environment Using Predictive Hebbian Learning. NIPS 1993: 598-605 - [c5]Nicol N. Schraudolph, Peter Dayan, Terrence J. Sejnowski:
Temporal Difference Learning of Position Evaluation in the Game of Go. NIPS 1993: 817-824 - 1992
- [j4]Christopher J. C. H. Watkins, Peter Dayan
Technical Note Q-Learning. Mach. Learn. 8: 279-292 (1992) - [j3]Peter Dayan
The Convergence of TD(lambda) for General lambda. Mach. Learn. 8: 341-362 (1992) - [c4]Peter Dayan, Geoffrey E. Hinton:
Feudal Reinforcement Learning. NIPS 1992: 271-278 - [c3]P. Read Montague, Peter Dayan, Steven J. Nowlan, Terrence J. Sejnowski:
Using Aperiodic Reinforcement for Directed Self-Organization During Development. NIPS 1992: 969-976 - 1991
- [j2]Peter Dayan
Optimising synaptic learning rules in linear associative memories. Biol. Cybern. 65(4): 253-265 (1991) - [c2]Peter Dayan, Geoffrey J. Goodhill:
Perturbing Hebbian Rules. NIPS 1991: 19-26 - 1990
- [j1]David J. Willshaw, Peter Dayan:
Optimal Plasticity from Matrix Memories: What Goes Up Must Come Down. Neural Comput. 2(1): 85-93 (1990) - [c1]Peter Dayan:
Navigating Through Temporal Difference. NIPS 1990: 464-470
Coauthor Index
Ray Dolan
aka: Raymond J. Dolan
aka: Raymond J. Dolan
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