Signal transmission in multilayer asynchronous neural networks

被引：0

作者：

Kobayashi, Wataru ^{[1
]}

Oku, Makito ^{[2
]}

Aihara, Kazuyuki ^{[1
,2
]}

机构：

[1] Univ Tokyo, Grad Sch Engn, Tokyo 1538505, Japan

[2] Univ Tokyo, Grad Sch Informat Sci & Technol, Tokyo 1538505, Japan

来源：

PROCEEDINGS OF THE SIXTEENTH INTERNATIONAL SYMPOSIUM ON ARTIFICIAL LIFE AND ROBOTICS (AROB 16TH '11) | 2011年

关键词：

Neural Networks; Asynchronous States; Active Decorrelation;

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：

It is believed that common input to nearby neurons leads to their synchronous spiking. However, recent studies have shown that recurrent neural networks can generate an asynchronous state characterized by low mean spiking correlations despite substantial amounts of shared input. The asynchronous state is generated by the interaction of excitatory and inhibitory populations, which is called active decorrelation. Here, we investigate the advantage of the active decorrelation on signal transmission in multilayer neural networks. The results of numerical simulations show that the active decorrelation is suitable for transmission of rate code because it can suppress the layer-by-layer growth of correlation.

引用

页码：334 / 337

页数：4

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