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

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.

journal_name

Exp Brain Res

authors

Suri RE,Schultz W

doi

10.1007/s002210050467

subject

Has Abstract

pub_date

1998-08-01 00:00:00

pages

350-4

issue

3

eissn

0014-4819

issn

1432-1106

journal_volume

121

pub_type

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