Revealing ensemble state transition patterns in multi-electrode neuronal recordings using hidden Markov models

Dimitris Xydas, Julia H. Downes, Matthew C Spencer, Mark William Hammond, Slowomir J. Nasuto, Ben J. Whalley, Victor M. Becerra, Kevin Warwick

    Research output: Contribution to journalArticlepeer-review

    Abstract

    In order to harness the computational capacity of dissociated cultured neuronal networks, it is necessary to understand neuronal dynamics and connectivity on a mesoscopic scale. To this end, this paper uncovers dynamic spatiotemporal patterns emerging from electrically stimulated neuronal cultures using hidden Markov models (HMMs) to characterize multi-channel spike trains as a progression of patterns of underlying states of neuronal activity. However, experimentation aimed at optimal choice of parameters for such models is essential and results are reported in detail. Results derived from ensemble neuronal data revealed highly repeatable patterns of state transitions in the order of milliseconds in response to probing stimuli.
    Original languageEnglish
    Pages (from-to)345-355
    JournalIEEE Transactions on Neural Systems and Rehabilitation Engineering
    Volume19
    Issue number4
    DOIs
    Publication statusPublished - Aug 2011

    Keywords

    • cultured neuronal networks, hidden Markov models, multi-channel recordings, neuronal state transitions

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