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Additional tests of Amit's attractor neural networks

Published online by Cambridge University Press:  04 February 2010

Ralph E. Hoffman
Ralph E. Hoffman
Affiliation:
Department of Psychiatry, Yale University School of Medicine, Box 208038, New Haven, CT 06520-8038. hoffman@biomed.med.yale.edu
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Abstract

Further tests of Amit's model are indicated. One strategy is to use the apparent coding sparseness of the model to make predictions about coding sparseness in Miyashita's network. A second approach is to use memory overload to induce false positive responses in modules and biological systems. In closing, the importance of temporal coding and timing requirements in developing biologically plausible attractor networks is mentioned.

Type
Open Peer Commentary
Information
Behavioral and Brain Sciences , Volume 18 , Issue 4 , December 1995 , pp. 634 - 635
Copyright
Copyright © Cambridge University Press 1995

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