Self-excitation of neurons leads to multiperiodicity of discrete-time neural networks with distributed delays

Abstract

In this paper, we investigate the interesting multiperiodicity of discrete-time neural networks with excitatory self-connections and distributed delays. Due to self-excitation of neurons, we construct 2 N close regions in state space for N -dimensional networks and attain the coexistence of 2 N periodic sequence solutions in these close regions. Meanwhile we estimate exponential attracting domain for each periodic sequence solution and apply our results to discrete-time analogues of periodic or autonomous neural networks. Under self-excitation of neurons, numerical simulations are performed to illustrate the effectiveness of our results. CitationHuang Z K, Wang X H, Sannay M. Self-excitation of neurons leads to multiperiodicity of discrete-time neural networks with distributed delays.

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