A Model of Primate Visual-Motor Conditional Learning

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Abstract
Observations of behavior and neural activity in premotor cortex of monkeys learning to pair an arbitrary visual stimulus with one of a set of previously learned behaviors are modeled with a network comprising a large number of motor selection columns. Reinforcement learning is used to recognize new visual patterns and acquire the appropriate visual-motor conditions. The architecture employs a distributed representation in which a single pattern is coded by a small subset of columns. A column is initially able to respond to many different inputs; as it learns to trigger a motor program, its responses become more narrowly defined. Each column's output is a set of votes for the various motor programs. The votes for each program are collected by selection units, which drive a winner-take-all circuit to determine whether a particular motor program is executed. The model is successful in reproducing the sequence of behavioral responses given by the subjects, as well as a number of phenomena that have been observed at the single-unit level. Finally, we offer a comparison to the backpropagation learning algorithm that demonstrates key principles which have been designed into our algorithm.