Revealing ensemble state transition patterns in multi-electrode neuronal recordings using hidden Markov models
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
To link to this article DOI: 10.1109/TNSRE.2011.2157360
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.