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Spike Train Pattern Discovery Using Interval Structure Alignment

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Book cover Neural Information Processing (ICONIP 2015)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 9490))

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Abstract

A method of finding frequently occurring patterns in spike trains is proposed. Due to stochastic fluctuation, patterns do not appear in exactly the same way. Therefore spike train subsequences are clustered using a similarity measure based on a positive definite kernel for alignments of local interval structures. By applying the method to synthetic data, spike trains generated from different neuron types are classified correctly. For synthetic and real data, patterns that repeatedly appear were successfully extracted.

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Notes

  1. 1.

    http://www.iip.ist.i.kyoto-u.ac.jp/member/cuturi/GA.html.

  2. 2.

    http://crcns.org.

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Acknowledgements

This work was supported in part by JSPS KAKENHI Grant Numbers 21700121, 25280110, and 25540159.

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Authors and Affiliations

  1. University of Tsukuba, Tsukuba, Japan

    Taro Tezuka

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  1. Taro Tezuka
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Correspondence to Taro Tezuka .

Editor information

Editors and Affiliations

  1. University of Istanbul, Istanbul, Turkey

    Sabri Arik

  2. University at Qatar, Doha, Qatar

    Tingwen Huang

  3. Tunku Abdul Rahman University College, Kuala Lumpur, Malaysia

    Weng Kin Lai

  4. University of Science Technology, Wuhan, China

    Qingshan Liu

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© 2015 Springer International Publishing Switzerland

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Tezuka, T. (2015). Spike Train Pattern Discovery Using Interval Structure Alignment. In: Arik, S., Huang, T., Lai, W., Liu, Q. (eds) Neural Information Processing. ICONIP 2015. Lecture Notes in Computer Science(), vol 9490. Springer, Cham. https://doi.org/10.1007/978-3-319-26535-3_28

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  • DOI: https://doi.org/10.1007/978-3-319-26535-3_28

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-26534-6

  • Online ISBN: 978-3-319-26535-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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