Quantifying Neural Correlations Using Lempel-ziv Complexity

نویسندگان

  • Jean-Luc Blanc
  • Nicolas Schmidt
  • Loic Bonnier
  • Annick Lesne
چکیده

Spike train analysis generally focuses on two aims: (1) the estimate of the neuronal information quantity, and (2) the quantification of spikes or bursts synchronization. We introduce here a new multivariate index based on LempelZiv complexity for spike train analysis. This index, called mutual Lempel-Ziv complexity (MLZC), can both measure spikes correlations and estimate the information carried in spike trains (i.e. characterize the dynamic process). Using simulated spike trains from a Poisson process, we show that the MLZC is able to quantify spike correlations. In addition, using bursting activity generated by electrically coupled Hindmarsh-Rose neurons, the MLZC is able to quantify and characterize bursts synchronization, when classical measures fail.

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تاریخ انتشار 2008