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Experimental Brain Research

, Volume 121, Issue 3, pp 350–354 | Cite as

Learning of sequential movements by neural network model with dopamine-like reinforcement signal

  • Roland E. Suri
  • W. Schultz
RESEARCH NOTE

Abstract 

Dopamine neurons appear to code an error in the prediction of reward. They are activated by unpredicted rewards, are not influenced by predicted rewards, and are depressed when a predicted reward is omitted. After conditioning, they respond to reward-predicting stimuli in a similar manner. With these characteristics, the dopamine response strongly resembles the predictive reinforcement teaching signal of neural network models implementing the temporal difference learning algorithm. This study explored a neural network model that used a reward-prediction error signal strongly resembling dopamine responses for learning movement sequences. A different stimulus was presented in each step of the sequence and required a different movement reaction, and reward occurred at the end of the correctly performed sequence. The dopamine-like predictive reinforcement signal efficiently allowed the model to learn long sequences. By contrast, learning with an unconditional reinforcement signal required synaptic eligibility traces of longer and biologically less-plausible durations for obtaining satisfactory performance. Thus, dopamine-like neuronal signals constitute excellent teaching signals for learning sequential behavior.

Key words Basal ganglia Teaching signal Temporal difference Synaptic plasticity Eligibility 

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Copyright information

© Springer-Verlag Berlin Heidelberg 1998

Authors and Affiliations

  • Roland E. Suri
    • 1
  • W. Schultz
    • 1
  1. 1.Institute of Physiology, University of Fribourg, CH-1700 Fribourg, Switzerland e-mail: Wolfram.Schultz@unifr.ch, Tel.: +41-26-300 8611, Fax: +41-26-300 9675CH

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