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

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Xydas, D., Downes, J. H., Spencer, M. C., Hammond, M. W., Nasuto, S. J., Whalley, B. J., Becerra, V. M. and Warwick, K. (2011) Revealing ensemble state transition patterns in multi-electrode neuronal recordings using hidden Markov models. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 19 (4). pp. 345-355. ISSN 1534-4320

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To link to this article DOI: 10.1109/TNSRE.2011.2157360

Abstract/Summary

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.

Item Type:Article
Refereed:Yes
Divisions:Faculty of Science > School of Systems Engineering
ID Code:23197
Uncontrolled Keywords:cultured neuronal networks, hidden Markov models, multi-channel recordings, neuronal state transitions
Publisher:IEEE

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