Abstract
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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Horimoto, N., Saito, T. (2013). Digital Dynamical Systems of Spike-Trains. In: Lee, M., Hirose, A., Hou, ZG., Kil, R.M. (eds) Neural Information Processing. ICONIP 2013. Lecture Notes in Computer Science, vol 8227. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-42042-9_24
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DOI: https://doi.org/10.1007/978-3-642-42042-9_24
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