A NEURAL-NETWORK MODEL FOR TEMPORAL SEQUENCE LEARNING AND MOTOR PROGRAMMING

A neural network model for fast learning and storage of temporal seque nces is presented. The recall of a learned sequence is triggered by th e occurrence of an item relating to its identify, and one of the main distinctive features of this model is that the speed at which a sequen ce is repeated can be freely modulated by a control subsystem. The pos sible applications of the model are illustrated by applying it to the production of motor forms. It is shown that any spatial shape memorize d in exteroceptive terms can be reproduced in terms of movement by any of the effector systems of the body and in particular by a simulated jointed arm, at any point in its working space and at any suitable siz e scale. Our theoretical approach reinforces the idea that the structu res responsible for planning a movement in the central nervous system might be largely independent of the motor systems performing this move ment.