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International Conference on Neural Information Processing

ICONIP 2018: Neural Information Processing pp 535–544Cite as

Skewed and Long-Tailed Distributions of Spiking Activity in Coupled Network Modules with Log-Normal Synaptic Weight Distribution

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 11301))

Abstract

Recent studies with neuroimaging modalities have been elucidating a structure of a whole network of the brain and its functional activity. The characteristics of various functional neural activities and network structures exhibit skewed and long-tailed distributions. However, it remains unclear how heavy-tailed structural distribution affects functional distribution. In this study, we constructed spiking neural networks composed of two modules with excitatory post-synaptic potential (EPSP) following log-normal distribution. Through the evaluation of multi-scale entropy analysis and its surrogate data analysis, we reveal that the long-tailed synaptic weight distribution enhances the complexity of spiking activity at large temporal scales and emerges non-linear dynamics. Furthermore, we compared distribution of residence time in each spiking pattern between cases with/without large EPSPs. The results show that strong synapses are crucial in the heavy-tailed distribution of residence time.

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Acknowledgment

This work was supported by JSPS KAKENHI for Early-Career Scientists (grant number: 18K18124).

Author information

Authors and Affiliations

  1. Department of Computer Science, Chiba Institute of Technology, 2-17-1 Tsudanuma, Narashino, Chiba, 275-0016, Japan

    Sou Nobukawa

  2. Graduate School of Applied Informatics, University of Hyogo, 7-1-28 Chuo-ku, Kobe, Hyogo, 650-8588, Japan

    Haruhiko Nishimura

  3. Department of Management Information Science, Fukui University of Technology, 3-6-1 Gakuen, Fukui, Fukui, 910-8505, Japan

    Teruya Yamanishi

Authors
  1. Sou Nobukawa
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  2. Haruhiko Nishimura
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  3. Teruya Yamanishi
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Corresponding author

Correspondence to Sou Nobukawa .

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Editors and Affiliations

  1. The Chinese Academy of Sciences, Beijing, China

    Long Cheng

  2. City University of Hong Kong, Kowloon, Hong Kong

    Andrew Chi Sing Leung

  3. Kobe University, Kobe, Japan

    Seiichi Ozawa

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Nobukawa, S., Nishimura, H., Yamanishi, T. (2018). Skewed and Long-Tailed Distributions of Spiking Activity in Coupled Network Modules with Log-Normal Synaptic Weight Distribution. In: Cheng, L., Leung, A., Ozawa, S. (eds) Neural Information Processing. ICONIP 2018. Lecture Notes in Computer Science(), vol 11301. Springer, Cham. https://doi.org/10.1007/978-3-030-04167-0_48

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  • DOI: https://doi.org/10.1007/978-3-030-04167-0_48

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-04166-3

  • Online ISBN: 978-3-030-04167-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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