Summary
2007 International Symposium on Nonlinear Theory and its Applications
2007
Session Number:19AM2-B
Session:
Number:19AM2-B-4
Emergence of self-organized structures in a neural network using two types of STDP learning rules
Hideyuki Kato, Koji Han-nuki, Takayuki Kimura, Tohru Ikeguchi,
pp.429-432
Publication Date:2007/9/16
Online ISSN:2188-5079
DOI:10.34385/proc.41.19AM2-B-4
Summary:
Recent numerical experiments reveal that in a neural network with the spike-timing dependent synaptic plasticity (STDP) learning emerges a functional complex network structure. The functional complex network structure has the small-world property and the scale-free property. However, experimental settings in the previous report are physiologically inappropriate. In addition, although there are two types of STDP learning rules, only one rule is used in the previous experiments. Thus, in this paper, we analyzed how the neural network structure selforganize and what kinds of complex neural network structure emerge with physiologically appropriate settings, if the two types of STDP learning, the additive and the multiplicative learning rules, are applied.