Complex sensory-motor sequence learning based on recurrent state representation and reinforcement learning
✍ Scribed by Peter F. Dominey
- Publisher
- Springer-Verlag
- Year
- 1995
- Tongue
- English
- Weight
- 997 KB
- Volume
- 73
- Category
- Article
- ISSN
- 0340-1200
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✦ Synopsis
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.