Multiplex communication by BP learning in neural network

被引：0

作者：

Tamura, Shinichi ^{[1
,2
]}

Nishitani, Yoshi ^{[2
]}

Hosokawa, Chie ^{[3
]}

Miyoshi, Tomomitsu ^{[4
]}

Sawai, Hajime ^{[5
]}

Mizuno-Matsumoto, Yuko ^{[6
]}

Chen, Yen-Wei ^{[7
]}

机构：

[1] NBL Technovator Co Ltd, Sennan, Japan

[2] Osaka Univ, Grad Sch Med, Dept Radiol, Suita, Osaka, Japan

[3] AIST, Biomed Res Inst, Ikeda, Osaka, Japan

[4] Osaka Univ, Grad Sch Med, Dept Integrat Physiol, Suita, Osaka, Japan

[5] Osaka Prefecture Univ, Coll Hlth & Human Sci, Habikino, Japan

[6] Univ Hyogo, Grad Sch Appl Informat, Kobe, Hyogo, Japan

[7] Ritsumeikan Univ, Coll Informat Sci & Engn, Kusatsu, Japan

来源：

2016 9TH INTERNATIONAL CONGRESS ON IMAGE AND SIGNAL PROCESSING, BIOMEDICAL ENGINEERING AND INFORMATICS (CISP-BMEI 2016) | 2016年

基金：

日本学术振兴会;

关键词：

Neural network; BP learning; Multiplex communication; Fluctuation of neuron;

D O I：

暂无

中图分类号：

R318 [生物医学工程];

学科分类号：

0831 ;

摘要：

It is a mystery that neural network composed of neurons with fluctuating characteristics can transmit information well reliably. In this paper, we show, in a simulation using a 9x9 2D mesh neural network, 9 to 1 multiplex communication is possible with 99% correct rate. Neurons are modeled by integrate and fire model without leak. Spikes spreads from transmitting neuron groups, propagated as spike waves, and received by receiving neurons. Then, the receiving neurons classify from which neuron group the spike waves come by back propagation neural network (BPN) method.

引用

页码：825 / 828

页数：4

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