Summary

Proceedings of the 2012 International Symposium on Nonlinear Theory and its Applications

2012

Session Number:C1L-C

Session:

Number:570

Bifurcation-based learning of a PWC spiking neuron model

Yutaro Yamashita,  Hiroyuki Torikai,  

pp.570-573

Publication Date:

Online ISSN:2188-5079

DOI:10.15248/proc.1.570

PDF download (446.2KB)

A piece-wise constant (ab. PWC) spiking neuron model (ab. PWN), which can reproduce various bifurcations observed in standard neuron models, is introduced. Using knowledge of bifurcations of the PWN, a heuristic but powerful learning method for the PWN is proposed. It is shown that the PWN can learn a typical response of the Izhikevich model which is also observed in not only other standard neuron models but also biological neurons.

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