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Multiplex communication by BP learning in neural network

Authors: Shinichi Tamura; Yoshi Nishitani; Chie Hosokawa; Tomomitsu Miyoshi; Hajime Sawai; Yuko Mizuno-Matsumoto; Yen-Wei Chen 0001;

Multiplex communication by BP learning in neural network

Abstract

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.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
1
Average
Average
Average
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