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International Conference on Neural Information Processing

ICONIP 2016: Neural Information Processing pp 264–271Cite as

Computational Performance of Echo State Networks with Dynamic Synapses

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 9947))

Abstract

The echo state network is a framework for temporal data processing, such as recognition, identification, classification and prediction. The echo state network generates spatiotemporal dynamics reflecting the history of an input sequence in the dynamical reservoir and constructs mapping from the input sequence to the output one in the readout. In the conventional dynamical reservoir consisting of sparsely connected neuron units, more neurons are required to create more time delay. In this study, we introduce the dynamic synapses into the dynamical reservoir for controlling the nonlinearity and the time constant. We apply the echo state network with dynamic synapses to several benchmark tasks. The results show that the dynamic synapses are effective for improving the performance in time series prediction tasks.

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Acknowledgments

This work was partially supported by JSPS KAKENHI Grant Number 16K00326 (GT), 26280093 (KA).

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Authors and Affiliations

  1. Graduate School of Information Science and Technology, The University of Tokyo, Tokyo, 113-8656, Japan

    Ryota Mori, Gouhei Tanaka & Kazuyuki Aihara

  2. Graduate School of Engineering, The University of Tokyo, Tokyo, 113-8656, Japan

    Gouhei Tanaka, Ryosho Nakane, Akira Hirose & Kazuyuki Aihara

  3. Institue of Industrial Science, The University of Tokyo, Tokyo, 153-8505, Japan

    Gouhei Tanaka & Kazuyuki Aihara

Authors
  1. Ryota Mori
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  2. Gouhei Tanaka
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  3. Ryosho Nakane
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  4. Akira Hirose
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  5. Kazuyuki Aihara
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Corresponding author

Correspondence to Ryota Mori .

Editor information

Editors and Affiliations

  1. The University of Tokyo, Tokyo, Japan

    Akira Hirose

  2. Kobe University, Kobe, Japan

    Seiichi Ozawa

  3. Okinawa Institute of Science and Technology Graduate University, Onna, Japan

    Kenji Doya

  4. Nara Institute of Science and Technology, Ikoma, Japan

    Kazushi Ikeda

  5. Kyungpook National University, Daegu, Korea (Republic of)

    Minho Lee

  6. Chinese Academy of Sciences, Beijing, China

    Derong Liu

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© 2016 Springer International Publishing AG

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Mori, R., Tanaka, G., Nakane, R., Hirose, A., Aihara, K. (2016). Computational Performance of Echo State Networks with Dynamic Synapses. In: Hirose, A., Ozawa, S., Doya, K., Ikeda, K., Lee, M., Liu, D. (eds) Neural Information Processing. ICONIP 2016. Lecture Notes in Computer Science(), vol 9947. Springer, Cham. https://doi.org/10.1007/978-3-319-46687-3_29

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  • DOI: https://doi.org/10.1007/978-3-319-46687-3_29

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-46686-6

  • Online ISBN: 978-3-319-46687-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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