Stability of asymmetric Hopfield networks.
In this paper, we discuss dynamical behaviors of recurrently asymmetrically connected neural networks in detail. We propose an effective approach to study global and local stability of the networks. Many of well known existing results are unified in our framework, which gives much better test conditions for global and local stability. Sufficient conditions for the uniqueness of the equilibrium point and its stability conditions are given, too.
Chen
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Lab of Nonlinear Science, Institute of Mathematics, Fudan University, Shanghai, P.R. China.
Amari
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SI