Digital Dynamical Systems of Spike-Trains

This paper studies a simple digital dynamical system that can generate various spike-trains. In order to consider the steady and transient states, we use two basic feature quantities. The first one is the number of co-existing periodic spike-trains that can characterize richness of the steady state. The second one is the concentricity of transition to the periodic spike-trains that can characterize variation of transient phenomena. Performing numerical experiments for two typical examples based on the bifurcating neuron, basic classification of the dynamics is considered.

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