Article

Journal of Computational Neuroscience

, Volume 6, Issue 3, pp 191-214

First online:

A Predictive Reinforcement Model of Dopamine Neurons for Learning Approach Behavior

  • José L. Contreras-VidalAffiliated withMotor Control Laboratory, Arizona State University
  • , Wolfram SchultzAffiliated withInstitute of Physiology, University of Fribourg

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Abstract

A neural network model of how dopamine and prefrontal cortex activity guides short- and long-term information processing within the cortico-striatal circuits during reward-related learning of approach behavior is proposed. The model predicts two types of reward-related neuronal responses generated during learning: (1) cell activity signaling errors in the prediction of the expected time of reward delivery and (2) neural activations coding for errors in the prediction of the amount and type of reward or stimulus expectancies. The former type of signal is consistent with the responses of dopaminergic neurons, while the latter signal is consistent with reward expectancy responses reported in the prefrontal cortex. It is shown that a neural network architecture that satisfies the design principles of the adaptive resonance theory of Carpenter and Grossberg (1987) can account for the dopamine responses to novelty, generalization, and discrimination of appetitive and aversive stimuli. These hypotheses are scrutinized via simulations of the model in relation to the delivery of free food outside a task, the timed contingent delivery of appetitive and aversive stimuli, and an asymmetric, instructed delay response task.

Neural network prefrontal reinforcement learning striatum timing