Multiplexed Spatiotemporal Communication Model in Artificial Neural Networks
Shinichi Tamura,
Yoshi Nishitani,
Takuya Kamimura,
Yasushi Yagi,
Chie Hosokawa,
Tomomitsu Miyoshi,
Hajime Sawai,
Yuko Mizuno-Matsumoto,
Yen-Wei Chen
Issue:
Volume 1, Issue 6, December 2013
Pages:
121-130
Received:
1 December 2013
Published:
30 January 2014
Abstract: It is well known that there is intercommunication among the different areas of the brain. However, till date, the rules of communication have not been successfully analyzed. The spike trains from neuronal cells have been simply treated as density-modulated waves with an activation level of the corresponding neuronal cells, or, at most, they have been analyzed using traditional metrics between sequences. The spike trains from neuronal cells have a random-like pattern that provides few clues regarding a coding rule. Here in a randomly generated artificial 3 × 3 multiplexed spatiotemporal communication neural network composed of threshold elements, we showed that pseudorandom sequences were generated during the simulation, similar to the random sequences generated by the cultured neural network of the rat brain. The transiently generated sequence patterns in the simulation were regarded as reflecting the circuit structure. These randomly shaped circuits generated pseudorandom sequences that functioned as codes for multiplexing communication. Although the circuit weights are randomly generated at present, it will be possible to extend this approach to determine the network weights by learning. This paper provides simulation results that support findings on cultured neural network.
Abstract: It is well known that there is intercommunication among the different areas of the brain. However, till date, the rules of communication have not been successfully analyzed. The spike trains from neuronal cells have been simply treated as density-modulated waves with an activation level of the corresponding neuronal cells, or, at most, they have be...
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