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A Novel Design Method of Burst Mechanisms of a Piece-Wise Constant Neuron Model Based on Bifurcation Analysis

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Neural Information Processing (ICONIP 2017)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10639))

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Abstract

A piece-wise constant (PWC) neuron model is an electronic circuit neuron model having a PWC vector field. In this paper, a PWC neuron model with a novel controlled voltage source is presented. Due to the nonlinearity of the voltage source, the presented model exhibits various bifurcations. Among such bifurcations, fundamental bifurcations related to burst behaviors are analyzed in this paper. Then, using the bifurcation analyses results, a novel design method of the vector field of the PWC neuron model is presented. It is shown that the presented design method enables the PWC neuron model to reproduce typical occurrence mechanisms of burst behaviors of neurons. Furthermore, the PWC neuron model is implemented as a breadboard prototype and occurrence of a typical burst behavior is validated by a real circuit experiment.

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Acknowledgments

This work was partially supported by JSPS KAKENHI Grant Number 15K00352.

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Authors and Affiliations

  1. Graduate School of Frontier Informatics, Kyoto Sangyo University, Motoyama, Kamigamo, Kita-Ku, Kyoto, 603-8555, Japan

    Chiaki Matsuda & Hiroyuki Torikai

Authors
  1. Chiaki Matsuda
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  2. Hiroyuki Torikai
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Corresponding author

Correspondence to Chiaki Matsuda .

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Editors and Affiliations

  1. Guangdong University of Technology, Guangzhou, China

    Derong Liu

  2. Guangdong University of Technology, Guangzhou, China

    Shengli Xie

  3. South China University of Technology, Guangzhou, China

    Yuanqing Li

  4. Institute of Automation, Chinese Academy of Sciences, Beijing, China

    Dongbin Zhao

  5. King Fahd University of Petroleum and Minerals, Dhahran, Saudi Arabia

    El-Sayed M. El-Alfy

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Matsuda, C., Torikai, H. (2017). A Novel Design Method of Burst Mechanisms of a Piece-Wise Constant Neuron Model Based on Bifurcation Analysis. In: Liu, D., Xie, S., Li, Y., Zhao, D., El-Alfy, ES. (eds) Neural Information Processing. ICONIP 2017. Lecture Notes in Computer Science(), vol 10639. Springer, Cham. https://doi.org/10.1007/978-3-319-70136-3_84

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  • DOI: https://doi.org/10.1007/978-3-319-70136-3_84

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-70135-6

  • Online ISBN: 978-3-319-70136-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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