Predicting Complex Behavior in Sparse Asymmetric Networks
Abstract
Recurrent networks of threshold elements have been studied inten(cid:173) sively as associative memories and pattern-recognition devices. While most research has concentrated on fully-connected symmetric net(cid:173) works. which relax to stable fixed points. asymmetric networks show richer dynamical behavior. and can be used as sequence generators or flexible pattern-recognition devices. In this paper. we approach the problem of predicting the complex global behavior of a class of ran(cid:173) dom asymmetric networks in terms of network parameters. These net(cid:173) works can show fixed-point. cyclical or effectively aperiodic behavior. depending on parameter values. and our approach can be used to set parameters. as necessary. to obtain a desired complexity of dynamics. The approach also provides qualitative insight into why the system behaves as it does and suggests possible applications.
Cite
Text
Minai and Levy. "Predicting Complex Behavior in Sparse Asymmetric Networks." Neural Information Processing Systems, 1992.Markdown
[Minai and Levy. "Predicting Complex Behavior in Sparse Asymmetric Networks." Neural Information Processing Systems, 1992.](https://mlanthology.org/neurips/1992/minai1992neurips-predicting/)BibTeX
@inproceedings{minai1992neurips-predicting,
title = {{Predicting Complex Behavior in Sparse Asymmetric Networks}},
author = {Minai, Ali A. and Levy, William B.},
booktitle = {Neural Information Processing Systems},
year = {1992},
pages = {556-563},
url = {https://mlanthology.org/neurips/1992/minai1992neurips-predicting/}
}