Predicting Complex Behavior in Sparse Asymmetric Networks

Part of Advances in Neural Information Processing Systems 5 (NIPS 1992)

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Ali Minai, William Levy


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.