2023 - Research.com Computer Science in Germany Leader Award
Neuroscience, Cognitive psychology, Reinforcement learning, Artificial intelligence and Dopamine are his primary areas of study. His work deals with themes such as Temporal difference learning, Classical conditioning, Orbitofrontal cortex, Cognition and Incentive salience, which intersect with Cognitive psychology. Peter Dayan has researched Temporal difference learning in several fields, including PVLV and Neural substrate.
His study in Reinforcement learning is interdisciplinary in nature, drawing from both Convergence, Simple, Reinforcement and Markov chain. His work on Artificial neural network, Representation and Connectionism as part of general Artificial intelligence study is frequently linked to Function, bridging the gap between disciplines. Peter Dayan has included themes like Dorsal raphe nucleus, Neurotransmitter and Serotonin in his Dopamine study.
His primary areas of study are Artificial intelligence, Neuroscience, Cognitive psychology, Reinforcement learning and Machine learning. In most of his Artificial intelligence studies, his work intersects topics such as Pattern recognition. His Neuroscience study frequently links to adjacent areas such as Serotonin.
His Cognitive psychology study combines topics from a wide range of disciplines, such as Cognition, Control and Normative. His Reinforcement learning research is multidisciplinary, incorporating elements of Cognitive science, Reinforcement and Action. He studies Dopaminergic, a branch of Dopamine.
His scientific interests lie mostly in Cognitive psychology, Reinforcement learning, Artificial intelligence, Neuroscience and Control. His Cognitive psychology research includes themes of Value, Internalizing psychopathology, Ventromedial prefrontal cortex, Contingency and Volatility. His Reinforcement learning research is multidisciplinary, incorporating perspectives in Representation, Cognition, Structure, Range and Outcome.
His Artificial intelligence research focuses on Machine learning and how it connects with Model free. His studies link Anticipation with Neuroscience. In his study, Decision problem is inextricably linked to Statistics, which falls within the broad field of Control.
His primary scientific interests are in Reinforcement learning, Cognitive psychology, Artificial intelligence, Function and Control. His Reinforcement learning study combines topics in areas such as Inference, Anxiety, Action selection, Optogenetics and Outcome. His research in Cognitive psychology tackles topics such as Brain activity and meditation which are related to areas like Value.
The various areas that Peter Dayan examines in his Artificial intelligence study include Machine learning and Action. His research integrates issues of Relevance, Sophistication, Pattern recognition, Cognitive map and Normative in his study of Control. Functional magnetic resonance imaging is a primary field of his research addressed under Neuroscience.
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A Neural Substrate of Prediction and Reward
Schultz W;Dayan P;Montague Pr.
Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems
Peter Dayan;L. F. Abbott.
Technical Note : \cal Q -Learning
Christopher J. C. H. Watkins;Peter Dayan.
Machine Learning (1992)
Technical Note Q-Learning
Christopher J.C.H. Watkins;Peter Dayan.
Machine Learning (1992)
Uncertainty-based competition between prefrontal and dorsolateral striatal systems for behavioral control
Nathaniel D Daw;Yael Niv;Yael Niv;Peter Dayan.
Nature Neuroscience (2005)
Dissociable roles of ventral and dorsal striatum in instrumental conditioning
John O'Doherty;Peter Dayan;Johannes Schultz;Ralf Deichmann.
A framework for mesencephalic dopamine systems based on predictive Hebbian learning
PR Montague;P Dayan;TJ Sejnowski.
The Journal of Neuroscience (1996)
Uncertainty, neuromodulation, and attention.
Angela J. Yu;Peter Dayan.
Model-based influences on humans' choices and striatal prediction errors.
Nathaniel D. Daw;Samuel J. Gershman;Ben Seymour;Peter Dayan.
The "Wake-Sleep" Algorithm for Unsupervised Neural Networks
Geoffrey E. Hinton;Peter Dayan;Brendan J. Frey;Radford M. Neal.
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