Multiplex communication by BP learning in neural network

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 9×9 2D mesh neural network, 9 to 1 multiplex communication is possible with 99% correct rate. Neurons are modeled by integr...

Full description

Saved in:
Bibliographic Details
Published in2016 9th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI) pp. 825 - 828
Main Authors Tamura, Shinichi, Nishitani, Yoshi, Hosokawa, Chie, Miyoshi, Tomomitsu, Sawai, Hajime, Mizuno-Matsumoto, Yuko, Yen-Wei Chen
Format Conference Proceeding
LanguageEnglish
Japanese
Published IEEE 01.10.2016
Subjects
BP learning
Fires
Fluctuation of neuron
Multiplex communication
Neural network
Neural networks
Two dimensional displays
Visualization
Online AccessGet full text

Cover

More Information
Summary: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 9×9 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.
DOI:10.1109/CISP-BMEI.2016.7852824