Research Topics
| P DayanSummaryAffiliation: University College London Country: UK Publications
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Detail Information
Publications
How to set the switches on this thingPeter Dayan
Gatsby Computational Neuroscience Unit, University College London, 17 Queen Square, London WC1N 3AR, United Kingdom
Curr Opin Neurobiol 22:1068-74. 2012..We review recent work in this area, focusing on cognitive control...- Twenty-five lessons from computational neuromodulationPeter Dayan
Gatsby Computational Neuroscience Unit, 17 Queen Square, London, UK
Neuron 76:240-56. 2012..Here, I offer a computationally focused review of algorithmic and implementational motifs associated with neuromodulators, using decision making in the face of uncertainty as a running example... - Instrumental vigour in punishment and rewardPeter Dayan
Gatsby Computational Neuroscience Unit, UCL, 17 Queen Square, London WC1N 3AR, UK
Eur J Neurosci 35:1152-68. 2012..Finally, we study how to fit these ideas into nascent treatments that extend concepts of opponency between dopamine and serotonin from valence to invigoration... 
Learning and selective attentionP Dayan
Gatsby Computational Neuroscience Unit, University College London, UK
Nat Neurosci 3:1218-23. 2000..Neuromodulatory systems and limbic structures are known to underlie attentional effects in such tasks...- Images, frames, and connectionist hierarchiesPeter Dayan
Gatsby Computational Neuroscience Unit, University College London, London
Neural Comput 18:2293-319. 2006..We meld unsupervised learning notions formulated for multilinear models with tensor product ideas for representing rich information. We apply the model to images of faces... - Pattern formation and cortical mapsPeter Dayan
Gatsby Computational Neuroscience Unit, University College London, Alexandra House, 17 Queen Square, London WCIN 3AR, UK
J Physiol Paris 97:475-89. 2003..Here, we describe and analyse a paradigmatic algorithm for activity-dependent development of the refinement and generation of neuronal selectivities, and relate it to some of the wealth of suggestions in the literature... - Reinforcement learning: the good, the bad and the uglyPeter Dayan
UCL, UK
Curr Opin Neurobiol 18:185-96. 2008..Here we review the latest dispatches from the forefront of this field, and map out some of the territories where lie monsters... - Putting the computation back into computational modelingP Dayan
Gatsby Computational Neuroscience Unit, University College, London, UK
Pharmacopsychiatry 39:S50-1. 2006..Many different varieties of modeling coexist in theoretical neuroscience. Here, we consider the positive and negative implications, for theories of schizophrenia, of a crucial distinction between computational and mathematical modeling... - The misbehavior of value and the discipline of the willPeter Dayan
Gatsby Computational Neuroscience Unit, UCL, 17 Queen Square, London, UK
Neural Netw 19:1153-60. 2006..The misbehavior created by Pavlovian values can be quite debilitating; we discuss how it may be disciplined... - Decision theory, reinforcement learning, and the brainPeter Dayan
University College London, London, England
Cogn Affect Behav Neurosci 8:429-53. 2008.... - Prospective and retrospective temporal difference learningPeter Dayan
Gatsby Computational Neuroscience Unit, UCL, London, WC1N 3AR, UK
Network 20:32-46. 2009..At the heart of the model is the average reward per step, which acts as a baseline for measuring immediate rewards. Relatively subtle changes to this baseline occasioned by the past can markedly influence predictions and thus behavior... - Selective Bayes: attentional load and crowdingPeter Dayan
Gatsby Computational Neuroscience Unit, UCL, 17 Queen Square, London WC1N 3AR, United Kingdom
Vision Res 50:2248-60. 2010..We show how these seeming anomalies can arise from normative Bayesian inference in the face of spatially confounded input... - Reward, motivation, and reinforcement learningPeter Dayan
Gatsby Computational Neuroscience Unit, University College London, 17 Queen Square, WC1N 3AR, London, United Kingdom
Neuron 36:285-98. 2002..We review the data and consider the involvement of a rich collection of different neural systems in various aspects of these forms of conditioning. Dopamine plays a pivotal, but complicated, role... - Serotonin in affective controlPeter Dayan
Gatsby Computational Neuroscience Unit, University College London, London WC1N3AR, UK
Annu Rev Neurosci 32:95-126. 2009..Finally, we suggest that it is only a partial reflection of dopamine because of essential asymmetries between the natural statistics of rewards and punishments... - Dopamine, reinforcement learning, and addictionP Dayan
Gatsby Computational Neuroscience Unit, UCL, London, UK
Pharmacopsychiatry 42:S56-65. 2009..We then use this as a framework to sketch various notions of the neuromodulator's possible participation in initiation and compulsion. We end with some pointers towards future theoretical developments... - Goal-directed control and its antipodesPeter Dayan
UCL, 17 Queen Square, London WC1N 3AR, UK
Neural Netw 22:213-9. 2009..Our overall aim is to reconnect some presently far-flung relations... - Neuroscience. MatchmakingNathaniel D Daw
Gatsby Computational Neuroscience Unit, University College London, London WC1N 3AR, UK
Science 304:1753-4. 2004 - Change-based inference for invariant discriminationReza Moazzezi
Gatsby Computational Neuroscience Unit, Alexandra House, 17 Queen Square, London, WC1N 3AR, UK
Network 19:236-52. 2008.... - Temporal difference models describe higher-order learning in humansBen Seymour
Wellcome Department of Imaging Neuroscience, 12 Queen Square, London WC1N 3BG, UK
Nature 429:664-7. 2004..Taken with existing data on reward learning, our results suggest a critical role for the ventral striatum in integrating complex appetitive and aversive predictions to coordinate behaviour... - Pre-attentive visual selectionLi Zhaoping
University College London, Department of Psychology, United Kingdom
Neural Netw 19:1437-9. 2006 - Phasic norepinephrine: a neural interrupt signal for unexpected eventsPeter Dayan
Gatsby Computational Neuroscience Unit, University College London, London, UK
Network 17:335-50. 2006..We quantify this idea in a Bayesian model of a well-studied visual discrimination task, demonstrating that the model captures a rich repertoire of noradrenergic responses at the sub-second temporal resolution... 
Differential encoding of losses and gains in the human striatumBen Seymour
Wellcome Trust Centre for Neuroimaging, UCL, London, United Kingdom
J Neurosci 27:4826-31. 2007....- A novel method for automated classification of epileptiform activity in the human electroencephalogram-based on independent component analysisMarzia De Lucia
Medical Physics and Clinical Neurophysiology, University College London, London, UK
Med Biol Eng Comput 46:263-72. 2008..Our algorithm performed well at detecting epileptiform events in this preliminary test and offers a flexible tool that is intended to be generalized to the simultaneous classification of many waveforms in the EEG... - Human pavlovian-instrumental transferDeborah Talmi
Wellcome Trust Centre for Neuroimaging, Institute of Neurology, UCL, WC1N 3BG London, United Kingdom
J Neurosci 28:360-8. 2008..Our data dovetails well with the animal literature and sheds light on the neural control of vigor... - A common mechanism for adaptive scaling of reward and noveltyNico Bunzeck
Institute of Cognitive Neuroscience, University College London, London WC1N 3AR, United Kingdom
Hum Brain Mapp 31:1380-94. 2010..These findings demonstrate a new mechanism for adjusting gain and sensitivity in declarative memory in accordance with contextual probabilities and expectancies of future events... - Flexible shaping: how learning in small steps helpsKai A Krueger
Gatsby Computational Neuroscience Unit, UCL, 17 Queen Square, London WC1N 3AR, United Kingdom
Cognition 110:380-94. 2009..Further, it leads to internal representations that are more robust to task manipulations such as reversals. We use the model to investigate some of the elements of successful shaping... - Acquisition and extinction in autoshapingSham Kakade
Gatsby Computational Neuroscience Unit, University College London, England
Psychol Rev 109:533-44. 2002.... - Uncertainty, neuromodulation, and attentionAngela J Yu
Gatsby Computational Neuroscience Unit, London, United Kingdom
Neuron 46:681-92. 2005..Moreover, the model suggests a class of attentional cueing tasks that involve both neuromodulators and shows how their interactions may be part-antagonistic, part-synergistic... - Matching storage and recall: hippocampal spike timing-dependent plasticity and phase response curvesMáté Lengyel
Gatsby Computational Neuroscience Unit, University College London, 17 Queen Square, London WC1N 3AR, UK
Nat Neurosci 8:1677-83. 2005..We show through simulation that such memories are competent analog autoassociators and demonstrate directly that the attributes of phase response curves of CA3 pyramidal cells recorded in vitro qualitatively conform with the theory... - Temporal difference models and reward-related learning in the human brainJOHN P O'DOHERTY
Wellcome Department of Imaging Neuroscience, Institute of Neurology, University College London, WC1N 3BG, London, United Kingdom
Neuron 38:329-37. 2003.... - Uncertainty-based competition between prefrontal and dorsolateral striatal systems for behavioral controlNathaniel D Daw
Gatsby Computational Neuroscience Unit, University College London, Alexandra House, 17 Queen Square, London WC1N 3AR, UK
Nat Neurosci 8:1704-11. 2005..This provides a unifying account of a wealth of experimental evidence about the factors favoring dominance by either system... - Dissociable roles of ventral and dorsal striatum in instrumental conditioningJohn O'Doherty
Wellcome Department of Imaging Neuroscience, Institute of Neurology, University College London, London WC1N 3BG, UK
Science 304:452-4. 2004..Our results suggest partly dissociable contributions of the ventral and dorsal striatum, with the former corresponding to the critic and the latter corresponding to the actor... - Acetylcholine in cortical inferenceAngela J Yu
Gatsby Computational Neuroscience Unit, University College London, UK
Neural Netw 15:719-30. 2002..We illustrate our proposal by means of an hierarchical hidden Markov model, showing that cholinergic modulation of contextual information leads to appropriate perceptual inference... - Cortical substrates for exploratory decisions in humansNathaniel D Daw
Gatsby Computational Neuroscience Unit, University College London UCL, Alexandra House, 17 Queen Square, London WC1N 3AR, UK
Nature 441:876-9. 2006.... - Dopamine: generalization and bonusesSham Kakade
Gatsby Computational Neuroscience Unit, University College London, UK
Neural Netw 15:549-59. 2002..We interpret this additional role for dopamine in terms of the mechanistic attentional and psychomotor effects of dopamine, having the computational role of guiding exploration... - Serotonin, inhibition, and negative moodPeter Dayan
Gatsby Computational Neuroscience Unit, University College London, London, United Kingdom
PLoS Comput Biol 4:e4. 2008.... - Nonlinear ideal observation and recurrent preprocessing in perceptual learningL Zhaoping
Department of Psychology, University College, London WC1E 6BT, UK
Network 14:233-47. 2003..Since, psychophysically, hyperacuity typically improves greatly over the course of perceptual learning, we discuss our model in the light of results on the speed and nature of learning... - Fast population codingQuentin J M Huys
Gatsby Computational Neuroscience Unit, University College London, London WC1N 3AR, UK
Neural Comput 19:404-41. 2007..We suggest this as an appropriate foundation for understanding time-varying population codes. Furthermore, we show how adaptation to temporal stimulus statistics emerges directly from the demands of simple decoding... - Dopamine, learning, and impulsivity: a biological account of attention-deficit/hyperactivity disorderJonathan Williams
Department of Child and Adolescent Psychiatry, Institute of Psychiatry, De Crespigny Park, Denmark Hill, London, UK
J Child Adolesc Psychopharmacol 15:160-79; discussion 157-9. 2005..In this study, we tested the theory that both the persistence and the variability of impulsivity could be the result of abnormalities in learning mechanisms and environment... - A Bayesian formulation of behavioral controlQuentin J M Huys
Gatsby Computational Neuroscience Unit, UCL, 17 Queen Square, London WC1N3AR, UK
Cognition 113:314-28. 2009..These results are discussed with reference to depression and animal models thereof... - Opponent interactions between serotonin and dopamineNathaniel D Daw
Computer Science Department and Center for the Neural Basis of Cognition, School of Computer Science, Carnegie Mellon University, Pittsburgh PA 15213 3891, USA
Neural Netw 15:603-16. 2002.... - Space and time in visual contextOdelia Schwartz
Albert Einstein College of Medicine, Jack and Pearl Resnick Campus, 1300 Morris Park Avenue, Bronx, New York 10461 718 430 2000, USA
Nat Rev Neurosci 8:522-35. 2007..We review the empirical literature and discuss the computational and statistical ideas that are battling to explain these conundrums, and thereby gain favour as more general accounts of cortical processing... - Doubly distributional population codes: simultaneous representation of uncertainty and multiplicityManeesh Sahani
W M Keck Foundation Center for Integrative Neurosciences, Univ of Calif, San Francisco, CA 94143 0732, USA
Neural Comput 15:2255-79. 2003..We present and validate a more powerful proposal for the way that population activity may encode uncertainty, both distinctly from and simultaneously with multiplicity... - Off-line replay maintains declarative memories in a model of hippocampal-neocortical interactionsSzabolcs Káli
Institute of Experimental Medicine, Hungarian Academy of Sciences, P O BOX 67, Budapest 1450, Hungary
Nat Neurosci 7:286-94. 2004..Hippocampal storage and replay also has a constructive role in the recall of structured, semantic information... - Inference and computation with population codesAlexandre Pouget
Department of Brain and Cognitive Sciences, Meliora Hall, University of Rochester, Rochester, NY 14627, USA
Annu Rev Neurosci 26:381-410. 2003..g., the probability density function over the orientation of a contour). This paper reviews both approaches, with a particular emphasis on the latter, which we see as a very promising framework for future modeling and experimental work... - Touché: the feeling of choicePeter E Latham
Nat Neurosci 8:408-9. 2005 - Choice valuesYael Niv
Nat Neurosci 9:987-8. 2006 - Encoding and decoding spikes for dynamic stimuliRama Natarajan
Department of Computer Science, University of Toronto, Toronto, Ontario, Canada
Neural Comput 20:2325-60. 2008..We show that this network can be learned in a supervised manner by a simple local learning rule... - Adaptation across the cortical hierarchy: low-level curve adaptation affects high-level facial-expression judgmentsHong Xu
Department of Neuroscience, Columbia University, New York, New York 10032, USA
J Neurosci 28:3374-83. 2008..By showing that adaptation can propagate up the cortical hierarchy, our findings also challenge existing functional accounts of adaptation... - A normative perspective on motivationYael Niv
Interdisciplinary Center for Neural Computation, Hebrew University, Jerusalem, Israel
Trends Cogn Sci 10:375-81. 2006..However, in novel states, we propose that outcome-independent, global effects of the utilities can 'energize' habitual actions... - Tonic dopamine: opportunity costs and the control of response vigorYael Niv
Interdisciplinary Center for Neural Computation, The Hebrew University of Jerusalem, Jerusalem, 91904, Israel
Psychopharmacology (Berl) 191:507-20. 2007.... - Dopamine modulation in the basal ganglia locks the gate to working memoryAaron J Gruber
Biomedical Engineering, Northwestern University, Chicago, IL, USA
J Comput Neurosci 20:153-66. 2006..Dopamine's involvement in affective processing endows this gating with specificity to motivational salience. We model a spatial working memory task and show that these combined effects of dopamine lead to superior performance... - Soft mixer assignment in a hierarchical generative model of natural scene statisticsOdelia Schwartz
Howard Hughes Medical Institute, Computational Neurobiology Lab, Salk Institute for Biological Studies, La Jolla, CA 92037, USA
Neural Comput 18:2680-718. 2006..We also show how our model helps interrelate a wide range of models of image statistics and cortical processing... - Persecutory delusions and the conditioned avoidance paradigm: towards an integration of the psychology and biology of paranoiaMichael Moutoussis
Tolworth Hospital, Red Lion Road, Surbiton, UK
Cogn Neuropsychiatry 12:495-510. 2007..In this study, we reappraise the psychological significance of the CAR model of antipsychotic drug action; and we relate this to contemporary psychological theories of paranoia... - A temporal difference account of avoidance learningMichael Moutoussis
Tolworth Hospital, Surbiton, England
Network 19:137-60. 2008..These postulated roles of dopamine in aversive learning can thus account for many of the effects of dopaminergic modulation seen in laboratory models of psychopathological processes...