Quantifying Neural Correlations Using Lempel-Ziv Complexity - Archive ouverte HAL
Conference Papers Year : 2008

Quantifying Neural Correlations Using Lempel-Ziv Complexity

Jean-Luc Blanc
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Nicolas Schmidt
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Loic Bonnier
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Laurent Pezard
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Annick Lesne

Abstract

Spike train analysis generally focus on two purposes: (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 Lempel-Ziv complexity for spike train analysis. This index, called mutual Lempel-Ziv complexity (MLZC), can measure both spikes correlations and estimate the information quantity of spike trains (i.e. characterize the dynamic state). 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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Dates and versions

hal-00331599 , version 1 (17-10-2008)

Identifiers

  • HAL Id : hal-00331599 , version 1

Cite

Jean-Luc Blanc, Nicolas Schmidt, Loic Bonnier, Laurent Pezard, Annick Lesne. Quantifying Neural Correlations Using Lempel-Ziv Complexity. Deuxième conférence française de Neurosciences Computationnelles, "Neurocomp08", Oct 2008, Marseille, France. ⟨hal-00331599⟩
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