Structural Analysis on STDP Neural Networks Using Complex Network Theory

被引：0
|
作者：
Kato, Hideyuki [1 ]
Ikeguchi, Tohru [1 ,3 ]
Aihara, Kazuyuki [2 ,3 ]
机构：
[1] Saitama Univ, Grad Sch Sci & Engn, Sakura Ku, 255 Shimo Ohkubo, Saitama 3388570, Japan
[2] Univ Tokyo, Grad Sch Informat Sci & Technol, Meguro Ku, Tokyo, Japan
[3] ERATO, JST, Aihara Complex Modelling Project, Meguro Ku, Tokyo, Japan
来源：
ARTIFICIAL NEURAL NETWORKS - ICANN 2009, PT I | 2009年 / 5768卷
关键词：
SYNAPTIC PLASTICITY; NEURONS;
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Synaptic plasticity is one of essential and central functions for the memory, the learning, and the development of the brains. Triggered by recent physiological experiments, the basic mechanisms of the spike-timing-dependent plasticity (STDP) have been widely analyzed in model studies. In this paper, we analyze complex structures in neural networks evolved by the STDP. In particular, we introduce the complex network theory to analyze spatiotemporal network structures constructed through the STDP. As a result, we show that nonrandom structures emerge in the neural network through the STDP.
引用
收藏
页码：306 / +
页数：2
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