Pattern-recognition By An Artificial Network Derived From Biologic Neuronal Systems
Abstract
A novel artificial neural network, derived from neurobiological observations, is described and examples of its performance are presented. This DYnamically STable Associative Learning (DYSTAL) network associatively learns both correlations and anticorrelations, and can be configured to classify or restore patterns with only a change in the number of output units. DYSTAL exhibits some particularly desirable properties: computational effort scales linearly with the number of connections, i.e., it is 0(N) in complexity; performance of the network is stable with respect to network parameters over wide ranges of their values and over the size of the input field; storage of a very large number of patterns is possible; patterns need not be orthogonal; network connections are not restricted to multi-layer feed-forward or any other specific structure; and, for a known set of deterministic input patterns, the network weights can be computed, a priori, in closed form. The network has been associatively trained to perform the XOR function as well as other classification tasks. The network has also been trained to restore patterns obscured by binary or analog noise. Neither global nor local feedback connections are required during learning; hence the network is particularly suitable for hardware (VLSI) implementation. © 1990 Springer-Verlag.
Recommended Citation
D. L. Alkon et al., "Pattern-recognition By An Artificial Network Derived From Biologic Neuronal Systems," Biological Cybernetics, vol. 62, no. 5, pp. 363 - 376, Springer, Mar 1990.
The definitive version is available at https://doi.org/10.1007/BF00197642
Department(s)
Mathematics and Statistics
International Standard Serial Number (ISSN)
1432-0770; 0340-1200
Document Type
Article - Journal
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2023 Springer, All rights reserved.
Publication Date
01 Mar 1990
PubMed ID
2331490