Journal of Computational Neuroscience

, Volume 6, Issue 3, pp 191–214

A Predictive Reinforcement Model of Dopamine Neurons for Learning Approach Behavior

Authors

  • José L. Contreras-Vidal
    • Motor Control LaboratoryArizona State University
  • Wolfram Schultz
    • Institute of PhysiologyUniversity of Fribourg
Article

DOI: 10.1023/A:1008862904946

Cite this article as:
Contreras-Vidal, J.L. & Schultz, W. J Comput Neurosci (1999) 6: 191. doi:10.1023/A:1008862904946

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 networkprefrontalreinforcement learningstriatumtiming

Copyright information

© Kluwer Academic Publishers 1999