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Peter Dayan

Gatsby Computational Neuroscience Unit

Neural networks

In my lab, we build mathematical and computational models of neural processing, with a particular emphasis on representation and learning. The main focus is on reinforcement learning and unsupervised learning, covering the ways that animals come to choose appropriate actions in the face of rewards and punishments, and the ways and goals of the process by which they come to form neural representations of the world. The models are informed and constrained by neurobiological, psychological and ethological data.


Substantial evidence suggests that the activity of dopaminergic neurons in the vertebrate mid-brain reports information on errors in ongoing predictions that animals make about the delivery of reward in behavioral learning tasks. The project is to extend models of this to investigate the role of serotonin and the interaction between dopamine and serotonin in such tasks.


Dayan, P (1998)
A hierarchical model of visual rivalry.
Neural Computation, 10, 1119-1136.

Schultz, W, Dayan, P \& Montague, PR (1997)
A neural substrate of prediction and reward.
Science, 275, 1593-1599.

Montague, PR, Dayan, P \& Sejnowski, TK (1996)
A framework for mesencephalic dopamine systems based on predictive Hebbian learning.
Journal of Neuroscience, 16, 1936-1947.


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