Abstract
We have previously proposed a multiplex communication system in a neural network. However, this system is designed to force the network to communicate in a multiplexed manner, in which “codes” or “temporal sequences” are inevitably induced. This means that the network has a main loop and coding/decoding circuits, which are somewhat artificial. In this paper, we show that it is also possible to communicate without these artificial guidance aids by multiplexing in a 2D mesh-type neural network, where learning procedures are used to find paths from an originating neuron to a destination neuron. We also provide statistics from these neural networks to show that random sequences occur more frequently than non-random sequences.
Highlights
Investigation of the manner in which information is communicated in the brain is an important theme in brain science
We have previously developed a time-shift map for analysis of the transmission of electroencephalogram (EEG) or magnetoencephalogram (MEG) signals in the brain [1], and have shown that it is effective for diagnosis of transient global amnesia (TGA) [2]
2 α test: Using a model after the weight learning process, we set the state of α in Fig. 8 to be 1 at time t=0 and watch the state transition up to t=63
Summary
Investigation of the manner in which information is communicated in the brain is an important theme in brain science. The question that arises here is how the neuron cells locate the target cells to send the required signal or, alternatively, how the responsible cells can obtain the necessary signals from the due cells, even when they are at remote locations This is a problem of finding communication links in a neuronal network. We have observed the M-sequence family occurring in spike trains from cultured neural networks significantly more frequently than would occur by chance [6, 7] It remains to be seen whether it is possible to communicate in a multiplexing manner in more natural neural networks without using an artificial shape to force multiplexing. While our previous network was tested by generating numerous random networks without a learning function, here we provide a learning function for the networks to enhance the communication links
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