Complex sensory-motor sequence learning based on recurrent state representation and reinforcement learning.
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.
Dominey
P F
PF
Vision et Motricité, INSERM Unité 94, Bron, France.
D000818:Animals; D003198:Computer Simulation; D006801:Humans; D008962:Models, Theoretical; D016571:Neural Networks, Computer; D011597:Psychomotor Performance / Q000502:physiology*