Spike Sorting by Joint Probabilistic Modeling of Neural Spike Trains and Waveforms

This paper details a novel probabilistic method for automatic neural spike sorting which uses stochastic point process models of neural spike trains and parameterized action potential waveforms. A novel likelihood model for observed firing times as the aggregation of hidden neural spike trains is de...

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Main Authors:	Matthews, Brett A., Clements, Mark A.
Format:	Online
Language:	English
Published:	Hindawi Publishing Corporation 2014
Online Access:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4009224/

id	pubmed-4009224
recordtype	oai_dc
spelling	pubmed-40092242014-05-14 Spike Sorting by Joint Probabilistic Modeling of Neural Spike Trains and Waveforms Matthews, Brett A. Clements, Mark A. Research Article This paper details a novel probabilistic method for automatic neural spike sorting which uses stochastic point process models of neural spike trains and parameterized action potential waveforms. A novel likelihood model for observed firing times as the aggregation of hidden neural spike trains is derived, as well as an iterative procedure for clustering the data and finding the parameters that maximize the likelihood. The method is executed and evaluated on both a fully labeled semiartificial dataset and a partially labeled real dataset of extracellular electric traces from rat hippocampus. In conditions of relatively high difficulty (i.e., with additive noise and with similar action potential waveform shapes for distinct neurons) the method achieves significant improvements in clustering performance over a baseline waveform-only Gaussian mixture model (GMM) clustering on the semiartificial set (1.98% reduction in error rate) and outperforms both the GMM and a state-of-the-art method on the real dataset (5.04% reduction in false positive + false negative errors). Finally, an empirical study of two free parameters for our method is performed on the semiartificial dataset. Hindawi Publishing Corporation 2014 2014-04-16 /pmc/articles/PMC4009224/ /pubmed/24829568 http://dx.doi.org/10.1155/2014/643059 Text en Copyright © 2014 B. A. Matthews and M. A. Clements. https://creativecommons.org/licenses/by/3.0/ This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
repository_type	Open Access Journal
institution_category	Foreign Institution
institution	US National Center for Biotechnology Information
building	NCBI PubMed
collection	Online Access
language	English
format	Online
author	Matthews, Brett A. Clements, Mark A.
spellingShingle	Matthews, Brett A. Clements, Mark A. Spike Sorting by Joint Probabilistic Modeling of Neural Spike Trains and Waveforms
author_facet	Matthews, Brett A. Clements, Mark A.
author_sort	Matthews, Brett A.
title	Spike Sorting by Joint Probabilistic Modeling of Neural Spike Trains and Waveforms
title_short	Spike Sorting by Joint Probabilistic Modeling of Neural Spike Trains and Waveforms
title_full	Spike Sorting by Joint Probabilistic Modeling of Neural Spike Trains and Waveforms
title_fullStr	Spike Sorting by Joint Probabilistic Modeling of Neural Spike Trains and Waveforms
title_full_unstemmed	Spike Sorting by Joint Probabilistic Modeling of Neural Spike Trains and Waveforms
title_sort	spike sorting by joint probabilistic modeling of neural spike trains and waveforms
description	This paper details a novel probabilistic method for automatic neural spike sorting which uses stochastic point process models of neural spike trains and parameterized action potential waveforms. A novel likelihood model for observed firing times as the aggregation of hidden neural spike trains is derived, as well as an iterative procedure for clustering the data and finding the parameters that maximize the likelihood. The method is executed and evaluated on both a fully labeled semiartificial dataset and a partially labeled real dataset of extracellular electric traces from rat hippocampus. In conditions of relatively high difficulty (i.e., with additive noise and with similar action potential waveform shapes for distinct neurons) the method achieves significant improvements in clustering performance over a baseline waveform-only Gaussian mixture model (GMM) clustering on the semiartificial set (1.98% reduction in error rate) and outperforms both the GMM and a state-of-the-art method on the real dataset (5.04% reduction in false positive + false negative errors). Finally, an empirical study of two free parameters for our method is performed on the semiartificial dataset.
publisher	Hindawi Publishing Corporation
publishDate	2014
url	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4009224/
_version_	1612085098340941824

Spike Sorting by Joint Probabilistic Modeling of Neural Spike Trains and Waveforms

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