Complex sensory-motor sequence learning based on recurrent state representation and reinforcement learning.

P F Dominey

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PubMed · 7548314

Complex sensory-motor sequence learning based on recurrent state representation and reinforcement learning.

P F Dominey

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Abstract

A novel neural network model is presented that learns by trial-and-error to reproduce complex sensory-motor sequences. One subnetwork, corresponding to the prefrontal cortex (PFC), is responsible for generating unique patterns of activity that represent the continuous state of sequence execution. A second subnetwork, corresponding to the striatum, associates these state-encoding patterns with the correct response at each point in the sequence execution. From a neuroscience perspective, the model is based on the known cortical and subcortical anatomy of the primate oculomotor system. From a theoretical perspective, the architecture is similar to that of a finite automaton in which outputs and state transitions are generated as a function of inputs and the current state. Simulation results for complex sequence reproduction and sequence discrimination are presented.

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Animals Computer Simulation Humans Models, Theoretical Neural Networks, Computer Psychomotor Performance Journal Article Research Support, Non-U.S. Gov't

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P F Dominey. 1995. Complex sensory-motor sequence learning based on recurrent state representation and reinforcement learning.. https://pubmed.ncbi.nlm.nih.gov/7548314/

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