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1 LPC - Laboratoire de physiologie cérébrale

Abstract : We demonstrate the efficacy of a new spike-sorting method based on a Markov Chain Monte Carlo MCMC algorithm by applying it to real data recorded from Purkinje cells PCs in young rat cerebellar slices. This algorithm is unique in its capability to estimate and make use of the firing statistics as well as the spike amplitude dynamics of the recorded neurons. PCs exhibit multiple discharge states, giving rise to multimodal interspike interval ISI histograms and to correlations between successive ISIs. The amplitude of the spikes generated by a PC in an -active- state decreases, a feature typical of many neurons from both vertebrates and invertebrates. These two features constitute a major and recurrent problem for all the presently available spike-sorting methods. We first show that a Hidden Markov Model with 3 log-Normal states provides a flexible and satisfying description of the complex firing of single PCs. We then incorporate this model into our previous MCMC based spike-sorting algorithm Pouzat et al, 2004, J. Neurophys. 91, 2910-2928 and test this new algorithm on multi-unit recordings of bursting PCs. We show that our method successfully classifies the bursty spike trains fired by PCs by using an independent single unit recording from a patch-clamp pipette.

keyword : Markov Chain Monte Carlo Multi-Electrode Hidden Markov Model Purkinje Cell

Autor: Matthieu Delescluse - Christophe Pouzat -

Fuente: https://hal.archives-ouvertes.fr/


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