Multiplex communication by BP learning in neural network
Shinichi Tamura, Yoshi Nishitani, Chie Hosokawa, Tomomitsu Miyoshi, Hajime Sawai, Yuko Mizuno‐Matsumoto, Yen‐Wei Chen · 2016
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.