Computational dynamics of gradient bistable networks.

V Chinarov; M Menzinger

doi:10.1016/s0303-2647(99)00091-x

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Computational dynamics of gradient bistable networks.

V Chinarov·M Menzinger

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Abstract

We describe a neural-like, homogeneous network consisting of coupled bistable elements and we study its abilities of learning, pattern recognition and computation. The technique allows new possibilities of pattern recognition, including the memorization and perfect recall of several memory patterns, without interference from spurious states. When the coupling strength between elements exceeds a critical value, the network readily converges to a unique attractor. Below this critical value one could perfectly recall all memorized patterns.

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Amino Acid Sequence Models, Chemical Neural Networks, Computer Oligopeptides Protein Folding Journal Article Research Support, Non-U.S. Gov't

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V Chinarov, M Menzinger. 2000. Computational dynamics of gradient bistable networks.. https://doi.org/10.1016/s0303-2647(99)00091-x

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