Graph structure modeling for multi-neuronal spike data

Shotaro Akaho, Sho Higuchi, Taishi Iwasaki, Hideitsu Hino, Masami Tatsuno, Noboru Murata

    Research output: Contribution to journalArticle

    Abstract

    We propose a method to extract connectivity between neurons for extracellularly recorded multiple spike trains. The method removes pseudo-correlation caused by propagation of information along an indirect pathway, and is also robust against the influence from unobserved neurons. The estimation algorithm consists of iterations of a simple matrix inversion, which is scalable to large data sets. The performance is examined by synthetic spike data.

    Original languageEnglish
    Article number012012
    JournalJournal of Physics: Conference Series
    Volume699
    Issue number1
    DOIs
    Publication statusPublished - 2016 Apr 6

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    neurons
    spikes
    iteration
    inversions
    propagation

    ASJC Scopus subject areas

    • Physics and Astronomy(all)

    Cite this

    Graph structure modeling for multi-neuronal spike data. / Akaho, Shotaro; Higuchi, Sho; Iwasaki, Taishi; Hino, Hideitsu; Tatsuno, Masami; Murata, Noboru.

    In: Journal of Physics: Conference Series, Vol. 699, No. 1, 012012, 06.04.2016.

    Research output: Contribution to journalArticle

    Akaho, Shotaro ; Higuchi, Sho ; Iwasaki, Taishi ; Hino, Hideitsu ; Tatsuno, Masami ; Murata, Noboru. / Graph structure modeling for multi-neuronal spike data. In: Journal of Physics: Conference Series. 2016 ; Vol. 699, No. 1.
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