A Model of Primate Visual-Motor Conditional Learning
Fagg Andrew H.; Arbib Michael A.
Журнал:
Adaptive Behavior
Дата:
1992-06-01
Аннотация:
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.
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