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Toward a modern theory of adaptive networks: Expectation and prediction

Toward a modern theory of adaptive networks: Expectation and prediction Many adaptive neural network (AN) theories are based on neuronlike adaptive elements that can behave as single unit analogs of associative conditioning. This article describes a similar adaptive element, but one that is more closely in accord with the facts of animal learning theory than elements commonly studied in AN research. It is suggested that an essential feature of classical conditioning that has been largely overlooked by AN theorists is its predictive nature. The adaptive element learns to increase its response rate in anticipation of increased stimulation, producing a CR before the occurrence of the UCS. The element also is in strong agreement with the behavioral data regarding the effects of stimulus context, since it is a temporally refined extension of the model proposed by R. A. Rescorla and A. R. Wagner (1972). Computer simulation demonstrates that the element becomes sensitive to the most reliable, nonredundant, and earliest predictors of reinforcement. The model is discussed in light of recent advances in the physiology and biochemistry of synaptic mechanisms. (2½ p ref) http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Psychological Review American Psychological Association

Toward a modern theory of adaptive networks: Expectation and prediction

Psychological Review , Volume 88 (2): 36 – Mar 1, 1981

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References (79)

Publisher
American Psychological Association
Copyright
Copyright © 1981 American Psychological Association
ISSN
0033-295x
eISSN
1939-1471
DOI
10.1037/0033-295X.88.2.135
Publisher site
See Article on Publisher Site

Abstract

Many adaptive neural network (AN) theories are based on neuronlike adaptive elements that can behave as single unit analogs of associative conditioning. This article describes a similar adaptive element, but one that is more closely in accord with the facts of animal learning theory than elements commonly studied in AN research. It is suggested that an essential feature of classical conditioning that has been largely overlooked by AN theorists is its predictive nature. The adaptive element learns to increase its response rate in anticipation of increased stimulation, producing a CR before the occurrence of the UCS. The element also is in strong agreement with the behavioral data regarding the effects of stimulus context, since it is a temporally refined extension of the model proposed by R. A. Rescorla and A. R. Wagner (1972). Computer simulation demonstrates that the element becomes sensitive to the most reliable, nonredundant, and earliest predictors of reinforcement. The model is discussed in light of recent advances in the physiology and biochemistry of synaptic mechanisms. (2½ p ref)

Journal

Psychological ReviewAmerican Psychological Association

Published: Mar 1, 1981

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