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

doi:doi:10.11648/j.acis.20130106.11

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

Published in Automation, Control and Intelligent Systems (Volume 1, Issue 6)

Received: 1 December 2013 Accepted: Published: 30 January 2014

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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.

Published in	Automation, Control and Intelligent Systems (Volume 1, Issue 6)
DOI	10.11648/j.acis.20130106.11
Page(s)	121-130
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

M-Sequence, Neural Network, Pseudo Random Sequence, Spatiotemporal Communication, Spike Train

References

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APA Style

Shinichi Tamura, Yoshi Nishitani, Takuya Kamimura, Yasushi Yagi, Chie Hosokawa, et al. (2014). Multiplexed Spatiotemporal Communication Model in Artificial Neural Networks. Automation, Control and Intelligent Systems, 1(6), 121-130. https://doi.org/10.11648/j.acis.20130106.11

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ACS Style

Shinichi Tamura; Yoshi Nishitani; Takuya Kamimura; Yasushi Yagi; Chie Hosokawa, et al. Multiplexed Spatiotemporal Communication Model in Artificial Neural Networks. Autom. Control Intell. Syst. 2014, 1(6), 121-130. doi: 10.11648/j.acis.20130106.11

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AMA Style

Shinichi Tamura, Yoshi Nishitani, Takuya Kamimura, Yasushi Yagi, Chie Hosokawa, et al. Multiplexed Spatiotemporal Communication Model in Artificial Neural Networks. Autom Control Intell Syst. 2014;1(6):121-130. doi: 10.11648/j.acis.20130106.11

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@article{10.11648/j.acis.20130106.11,
  author = {Shinichi Tamura and Yoshi Nishitani and Takuya Kamimura and Yasushi Yagi and Chie Hosokawa and Tomomitsu Miyoshi and Hajime Sawai and Yuko Mizuno-Matsumoto and Yen-Wei Chen},
  title = {Multiplexed Spatiotemporal Communication Model in Artificial Neural Networks},
  journal = {Automation, Control and Intelligent Systems},
  volume = {1},
  number = {6},
  pages = {121-130},
  doi = {10.11648/j.acis.20130106.11},
  url = {https://doi.org/10.11648/j.acis.20130106.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.acis.20130106.11},
  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.},
 year = {2014}
}

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TY  - JOUR
T1  - Multiplexed Spatiotemporal Communication Model in Artificial Neural Networks
AU  - Shinichi Tamura
AU  - Yoshi Nishitani
AU  - Takuya Kamimura
AU  - Yasushi Yagi
AU  - Chie Hosokawa
AU  - Tomomitsu Miyoshi
AU  - Hajime Sawai
AU  - Yuko Mizuno-Matsumoto
AU  - Yen-Wei Chen
Y1  - 2014/01/30
PY  - 2014
N1  - https://doi.org/10.11648/j.acis.20130106.11
DO  - 10.11648/j.acis.20130106.11
T2  - Automation, Control and Intelligent Systems
JF  - Automation, Control and Intelligent Systems
JO  - Automation, Control and Intelligent Systems
SP  - 121
EP  - 130
PB  - Science Publishing Group
SN  - 2328-5591
UR  - https://doi.org/10.11648/j.acis.20130106.11
AB  - 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.
VL  - 1
IS  - 6
ER  -

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Author Information

Shinichi Tamura

NBL Technovator Co., Ltd, 631 Shindachimakino, Sennan City, 590-0522 Japan; Graduate School of Medicine, Osaka University, Suita, 565-0871 Japan
Yoshi Nishitani

Graduate School of Medicine, Osaka University, Suita, 565-0871 Japan
Takuya Kamimura

ISIR, Osaka University, 8-1 Mihogaoka, Ibaraki City, Osaka, 567-0047 Japan
Yasushi Yagi

ISIR, Osaka University, 8-1 Mihogaoka, Ibaraki City, Osaka, 567-0047 Japan
Chie Hosokawa

AIST Kansai, 1-8-31 Midorigaoka, Ikeda 563-8577 Japan
Tomomitsu Miyoshi

Graduate School of Medicine, Osaka University, Suita, 565-0871 Japan
Hajime Sawai

Graduate School of Medicine, Osaka University, Suita, 565-0871 Japan
Yuko Mizuno-Matsumoto

Graduate School of Applied Informatics, University of Hyogo, Kobe, 650-0047 Japan
Yen-Wei Chen

Graduate School of Sci. and Eng., Ritsumeikan University, Kusatsu, 525-8577 Japan

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APA Style

Shinichi Tamura, Yoshi Nishitani, Takuya Kamimura, Yasushi Yagi, Chie Hosokawa, et al. (2014). Multiplexed Spatiotemporal Communication Model in Artificial Neural Networks. Automation, Control and Intelligent Systems, 1(6), 121-130. https://doi.org/10.11648/j.acis.20130106.11

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ACS Style

Shinichi Tamura; Yoshi Nishitani; Takuya Kamimura; Yasushi Yagi; Chie Hosokawa, et al. Multiplexed Spatiotemporal Communication Model in Artificial Neural Networks. Autom. Control Intell. Syst. 2014, 1(6), 121-130. doi: 10.11648/j.acis.20130106.11

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AMA Style

Shinichi Tamura, Yoshi Nishitani, Takuya Kamimura, Yasushi Yagi, Chie Hosokawa, et al. Multiplexed Spatiotemporal Communication Model in Artificial Neural Networks. Autom Control Intell Syst. 2014;1(6):121-130. doi: 10.11648/j.acis.20130106.11

Copy | Download

@article{10.11648/j.acis.20130106.11,
  author = {Shinichi Tamura and Yoshi Nishitani and Takuya Kamimura and Yasushi Yagi and Chie Hosokawa and Tomomitsu Miyoshi and Hajime Sawai and Yuko Mizuno-Matsumoto and Yen-Wei Chen},
  title = {Multiplexed Spatiotemporal Communication Model in Artificial Neural Networks},
  journal = {Automation, Control and Intelligent Systems},
  volume = {1},
  number = {6},
  pages = {121-130},
  doi = {10.11648/j.acis.20130106.11},
  url = {https://doi.org/10.11648/j.acis.20130106.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.acis.20130106.11},
  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.},
 year = {2014}
}

Copy | Download

TY  - JOUR
T1  - Multiplexed Spatiotemporal Communication Model in Artificial Neural Networks
AU  - Shinichi Tamura
AU  - Yoshi Nishitani
AU  - Takuya Kamimura
AU  - Yasushi Yagi
AU  - Chie Hosokawa
AU  - Tomomitsu Miyoshi
AU  - Hajime Sawai
AU  - Yuko Mizuno-Matsumoto
AU  - Yen-Wei Chen
Y1  - 2014/01/30
PY  - 2014
N1  - https://doi.org/10.11648/j.acis.20130106.11
DO  - 10.11648/j.acis.20130106.11
T2  - Automation, Control and Intelligent Systems
JF  - Automation, Control and Intelligent Systems
JO  - Automation, Control and Intelligent Systems
SP  - 121
EP  - 130
PB  - Science Publishing Group
SN  - 2328-5591
UR  - https://doi.org/10.11648/j.acis.20130106.11
AB  - 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.
VL  - 1
IS  - 6
ER  -

Copy | Download