dorsal/arxiv
View SchemaEfficient spike-sorting of multi-state neurons using inter-spike intervals information
| Authors | Matthieu Delescluse, Christophe Pouzat |
|---|---|
| Categories | |
| ArXiv ID | q-bio/0505053 |
| URL | https://arxiv.org/abs/q-bio/0505053 |
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.
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"abstract": "We demonstrate the efficacy of a new spike-sorting method based on a Markov\nChain Monte Carlo (MCMC) algorithm by applying it to real data recorded from\nPurkinje cells (PCs) in young rat cerebellar slices. This algorithm is unique\nin its capability to estimate and make use of the firing statistics as well as\nthe spike amplitude dynamics of the recorded neurons. PCs exhibit multiple\ndischarge states, giving rise to multimodal interspike interval (ISI)\nhistograms and to correlations between successive ISIs. The amplitude of the\nspikes generated by a PC in an \"active\" state decreases, a feature typical of\nmany neurons from both vertebrates and invertebrates. These two features\nconstitute a major and recurrent problem for all the presently available\nspike-sorting methods. We first show that a Hidden Markov Model with 3\nlog-Normal states provides a flexible and satisfying description of the complex\nfiring of single PCs. We then incorporate this model into our previous MCMC\nbased spike-sorting algorithm (Pouzat et al, 2004, J. Neurophys. 91, 2910-2928)\nand test this new algorithm on multi-unit recordings of bursting PCs. We show\nthat our method successfully classifies the bursty spike trains fired by PCs by\nusing an independent single unit recording from a patch-clamp pipette.",
"arxiv_id": "q-bio/0505053",
"authors": [
"Matthieu Delescluse",
"Christophe Pouzat"
],
"categories": [
"q-bio.QM",
"math.ST",
"physics.bio-ph",
"physics.data-an",
"stat.TH"
],
"title": "Efficient spike-sorting of multi-state neurons using inter-spike intervals information",
"url": "https://arxiv.org/abs/q-bio/0505053"
},
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