Multiplex Communication with Synchronous Shift and Weight Learning in 2D Mesh Neural Network

Takuya Kamimura; Yasushi Yagi; Shinichi Tamura; Yen-Wei Chen

doi:doi:10.11648/j.acis.20150305.11

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Multiplex Communication with Synchronous Shift and Weight Learning in 2D Mesh Neural Network

Takuya Kamimura, Yasushi Yagi, Shinichi Tamura, Yen-Wei Chen

Published in Automation, Control and Intelligent Systems (Volume 3, Issue 5)

Received: 7 August 2015 Accepted: 21 August 2015 Published: 9 September 2015

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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.

Published in	Automation, Control and Intelligent Systems (Volume 3, Issue 5)
DOI	10.11648/j.acis.20150305.11
Page(s)	63-70
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), 2015. Published by Science Publishing Group

Keywords

Brain Information Processing, Neural Circuit, Pseudo-Random Sequence, M-Sequence, Multiplex Communication

References

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[2]	Yuko Mizuno-Matsumoto, Masatsugu Ishijima, Kazuhiro Shinosaki, Takashi Nishikawa, Satoshi Ukai, Yoshitaka Ikejiri, Yoshitsugu Nakagawa, Ryouhei Ishii, Hiromasa Tokunaga, Shinichi Tamura, Susumu Date, Tsuyoshi Inouye, Shinji Shimojo, Masatoshi Takeda: ``Transient Global Amnesia (TGA) in an MEG Study,'' Brain Topography, Vol.13, No.4, pp.269-274, 2001.
[3]	Peter C Hansen, Morten L Kringelbach, Riitta Salmelin, MEG: An Introduction to Methods, Oxford University Press Inc. New York, 2010.
[4]	Shinichi Tamura, Yoshi Nishitani, Takuya Kamimura, Yasushi Yagi, Chie Hosokawa, Tomomitsu Miyoshi, Hajime Sawai, Yuko Mizuno-Matsumoto, Yen-Wei Chen. “Multiplexed Spatiotemporal Communication Model in Artificial Neural Networks,” Automation, Control and Intelligent Systems. Vol. 1, No. 6, 2013, pp. 121-130. doi: 10.11648/j.acis.20130106.11.
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[6]	Yoshi Nishitani, Chie Hosokawa, Yuko Mizuno-Matsumoto, Tomomitsu Miyoshi, Hajime Sawai and Shinichi Tamura, ``Detection of M-sequences from spike sequence in neuronal networks,'' Computational Intelligence and Neuroscience - Special issue on Computational Intelligence in Biomedical Science and Engineering Volume 2012, 2012.
[7]	Shinichi Tamura, Yoshi Nishitani, Chie Hosokawa, Yuko Mizuno-Matsumoto, Takuya Kamimura, Yen-Wei Chen, Tomomitsu Miyoshi, Hajime Sawai. “M-sequence family from cultured neural circuits,'' 6th International Conference on New Trends in Information Science, Service Science and Data Mining (ISSDM 2012), Oct. 23-25, 2012.
[8]	Daniel L. Schacter, Daniel T. Gilbert, Daniel M. Wegner, Psychology (2nd ed.), Worth Publishers, New York, 2011.
[9]	Christophe Lecerf, ”The double loop as a model of a learning neural system,'' Proceedings World Multiconference on Systemics, Cybernetics and Informatics, Vol.1, pp. 587-594, 1998.
[10]	Y. Choe, "Analogical cascade: a theory on the role of the thalamo-cortical loop in brain function," Neurocomputing 52-54, pp.713-719, 2003.
[11]	Takuya Kamimura, Yoshi Nishitani, Yen-Wei Chen, Yasushi Yagi, and Shinichi Tamura, “Copy of neural loop circuits for memory and communication,” Journal of Communications and Information Sciences, Vol.4, No.1, pp.46-56, Jan 2014.
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APA Style

Takuya Kamimura, Yasushi Yagi, Shinichi Tamura, Yen-Wei Chen. (2015). Multiplex Communication with Synchronous Shift and Weight Learning in 2D Mesh Neural Network. Automation, Control and Intelligent Systems, 3(5), 63-70. https://doi.org/10.11648/j.acis.20150305.11

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ACS Style

Takuya Kamimura; Yasushi Yagi; Shinichi Tamura; Yen-Wei Chen. Multiplex Communication with Synchronous Shift and Weight Learning in 2D Mesh Neural Network. Autom. Control Intell. Syst. 2015, 3(5), 63-70. doi: 10.11648/j.acis.20150305.11

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AMA Style

Takuya Kamimura, Yasushi Yagi, Shinichi Tamura, Yen-Wei Chen. Multiplex Communication with Synchronous Shift and Weight Learning in 2D Mesh Neural Network. Autom Control Intell Syst. 2015;3(5):63-70. doi: 10.11648/j.acis.20150305.11

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@article{10.11648/j.acis.20150305.11,
  author = {Takuya Kamimura and Yasushi Yagi and Shinichi Tamura and Yen-Wei Chen},
  title = {Multiplex Communication with Synchronous Shift and Weight Learning in 2D Mesh Neural Network},
  journal = {Automation, Control and Intelligent Systems},
  volume = {3},
  number = {5},
  pages = {63-70},
  doi = {10.11648/j.acis.20150305.11},
  url = {https://doi.org/10.11648/j.acis.20150305.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.acis.20150305.11},
  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.},
 year = {2015}
}

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TY  - JOUR
T1  - Multiplex Communication with Synchronous Shift and Weight Learning in 2D Mesh Neural Network
AU  - Takuya Kamimura
AU  - Yasushi Yagi
AU  - Shinichi Tamura
AU  - Yen-Wei Chen
Y1  - 2015/09/09
PY  - 2015
N1  - https://doi.org/10.11648/j.acis.20150305.11
DO  - 10.11648/j.acis.20150305.11
T2  - Automation, Control and Intelligent Systems
JF  - Automation, Control and Intelligent Systems
JO  - Automation, Control and Intelligent Systems
SP  - 63
EP  - 70
PB  - Science Publishing Group
SN  - 2328-5591
UR  - https://doi.org/10.11648/j.acis.20150305.11
AB  - 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.
VL  - 3
IS  - 5
ER  -

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Author Information

Takuya Kamimura

NBL Technovator Co., Ltd. Shindachimakino, Sennan, Japan
Yasushi Yagi

ISIR, Osaka University, Mihogaoka, Ibaraki City, Osaka, Japan
Shinichi Tamura

NBL Technovator Co., Ltd. Shindachimakino, Sennan, Japan
Yen-Wei Chen

Ritsumeikan University, Nojihigashi, Kusatsu-shi, Shiga, Japan

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Plain Text BibTeX RIS

APA Style

Takuya Kamimura, Yasushi Yagi, Shinichi Tamura, Yen-Wei Chen. (2015). Multiplex Communication with Synchronous Shift and Weight Learning in 2D Mesh Neural Network. Automation, Control and Intelligent Systems, 3(5), 63-70. https://doi.org/10.11648/j.acis.20150305.11

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ACS Style

Takuya Kamimura; Yasushi Yagi; Shinichi Tamura; Yen-Wei Chen. Multiplex Communication with Synchronous Shift and Weight Learning in 2D Mesh Neural Network. Autom. Control Intell. Syst. 2015, 3(5), 63-70. doi: 10.11648/j.acis.20150305.11

Copy | Download

AMA Style

Takuya Kamimura, Yasushi Yagi, Shinichi Tamura, Yen-Wei Chen. Multiplex Communication with Synchronous Shift and Weight Learning in 2D Mesh Neural Network. Autom Control Intell Syst. 2015;3(5):63-70. doi: 10.11648/j.acis.20150305.11

Copy | Download

@article{10.11648/j.acis.20150305.11,
  author = {Takuya Kamimura and Yasushi Yagi and Shinichi Tamura and Yen-Wei Chen},
  title = {Multiplex Communication with Synchronous Shift and Weight Learning in 2D Mesh Neural Network},
  journal = {Automation, Control and Intelligent Systems},
  volume = {3},
  number = {5},
  pages = {63-70},
  doi = {10.11648/j.acis.20150305.11},
  url = {https://doi.org/10.11648/j.acis.20150305.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.acis.20150305.11},
  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.},
 year = {2015}
}

Copy | Download

TY  - JOUR
T1  - Multiplex Communication with Synchronous Shift and Weight Learning in 2D Mesh Neural Network
AU  - Takuya Kamimura
AU  - Yasushi Yagi
AU  - Shinichi Tamura
AU  - Yen-Wei Chen
Y1  - 2015/09/09
PY  - 2015
N1  - https://doi.org/10.11648/j.acis.20150305.11
DO  - 10.11648/j.acis.20150305.11
T2  - Automation, Control and Intelligent Systems
JF  - Automation, Control and Intelligent Systems
JO  - Automation, Control and Intelligent Systems
SP  - 63
EP  - 70
PB  - Science Publishing Group
SN  - 2328-5591
UR  - https://doi.org/10.11648/j.acis.20150305.11
AB  - 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.
VL  - 3
IS  - 5
ER  -

Copy | Download