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

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.
[5] S. W. Golomb, G. Gong, Signal Design for Good Correlation: For Wireless Communication and Rader, Cambridge University Press. 2005.
[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.
[12] Wulfram Gerstner and Werner Kistler, Spiking Neuron Models. Single Neurons, Populations, Plasticity, Cambridge University Press, 2002.
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Cite This Article
  • 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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    T1  - Multiplex Communication with Synchronous Shift and Weight Learning in 2D Mesh Neural Network
    AU  - Takuya Kamimura
    AU  - Yasushi Yagi
    AU  - Shinichi Tamura
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    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
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    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
  • NBL Technovator Co., Ltd. Shindachimakino, Sennan, Japan

  • ISIR, Osaka University, Mihogaoka, Ibaraki City, Osaka, Japan

  • NBL Technovator Co., Ltd. Shindachimakino, Sennan, Japan

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

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