Consensus-Based Sorting of Neuronal Spike Waveforms

Optimizing spike-sorting algorithms is difficult because sorted clusters can rarely be checked against independently obtained “ground truth” data. In most spike-sorting algorithms in use today, the optimality of a clustering solution is assessed relative to some assumption on the distribution of the...

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Main Authors:	Fournier, Julien, Mueller, Christian M., Shein-Idelson, Mark, Hemberger, Mike, Laurent, Gilles
Format:	Online
Language:	English
Published:	Public Library of Science 2016
Online Access:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4990262/

id	pubmed-4990262
recordtype	oai_dc
spelling	pubmed-49902622016-08-29 Consensus-Based Sorting of Neuronal Spike Waveforms Fournier, Julien Mueller, Christian M. Shein-Idelson, Mark Hemberger, Mike Laurent, Gilles Research Article Optimizing spike-sorting algorithms is difficult because sorted clusters can rarely be checked against independently obtained “ground truth” data. In most spike-sorting algorithms in use today, the optimality of a clustering solution is assessed relative to some assumption on the distribution of the spike shapes associated with a particular single unit (e.g., Gaussianity) and by visual inspection of the clustering solution followed by manual validation. When the spatiotemporal waveforms of spikes from different cells overlap, the decision as to whether two spikes should be assigned to the same source can be quite subjective, if it is not based on reliable quantitative measures. We propose a new approach, whereby spike clusters are identified from the most consensual partition across an ensemble of clustering solutions. Using the variability of the clustering solutions across successive iterations of the same clustering algorithm (template matching based on K-means clusters), we estimate the probability of spikes being clustered together and identify groups of spikes that are not statistically distinguishable from one another. Thus, we identify spikes that are most likely to be clustered together and therefore correspond to consistent spike clusters. This method has the potential advantage that it does not rely on any model of the spike shapes. It also provides estimates of the proportion of misclassified spikes for each of the identified clusters. We tested our algorithm on several datasets for which there exists a ground truth (simultaneous intracellular data), and show that it performs close to the optimum reached by a support vector machine trained on the ground truth. We also show that the estimated rate of misclassification matches the proportion of misclassified spikes measured from the ground truth data. Public Library of Science 2016-08-18 /pmc/articles/PMC4990262/ /pubmed/27536990 http://dx.doi.org/10.1371/journal.pone.0160494 Text en © 2016 Fournier et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
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	Fournier, Julien Mueller, Christian M. Shein-Idelson, Mark Hemberger, Mike Laurent, Gilles
spellingShingle	Fournier, Julien Mueller, Christian M. Shein-Idelson, Mark Hemberger, Mike Laurent, Gilles Consensus-Based Sorting of Neuronal Spike Waveforms
author_facet	Fournier, Julien Mueller, Christian M. Shein-Idelson, Mark Hemberger, Mike Laurent, Gilles
author_sort	Fournier, Julien
title	Consensus-Based Sorting of Neuronal Spike Waveforms
title_short	Consensus-Based Sorting of Neuronal Spike Waveforms
title_full	Consensus-Based Sorting of Neuronal Spike Waveforms
title_fullStr	Consensus-Based Sorting of Neuronal Spike Waveforms
title_full_unstemmed	Consensus-Based Sorting of Neuronal Spike Waveforms
title_sort	consensus-based sorting of neuronal spike waveforms
description	Optimizing spike-sorting algorithms is difficult because sorted clusters can rarely be checked against independently obtained “ground truth” data. In most spike-sorting algorithms in use today, the optimality of a clustering solution is assessed relative to some assumption on the distribution of the spike shapes associated with a particular single unit (e.g., Gaussianity) and by visual inspection of the clustering solution followed by manual validation. When the spatiotemporal waveforms of spikes from different cells overlap, the decision as to whether two spikes should be assigned to the same source can be quite subjective, if it is not based on reliable quantitative measures. We propose a new approach, whereby spike clusters are identified from the most consensual partition across an ensemble of clustering solutions. Using the variability of the clustering solutions across successive iterations of the same clustering algorithm (template matching based on K-means clusters), we estimate the probability of spikes being clustered together and identify groups of spikes that are not statistically distinguishable from one another. Thus, we identify spikes that are most likely to be clustered together and therefore correspond to consistent spike clusters. This method has the potential advantage that it does not rely on any model of the spike shapes. It also provides estimates of the proportion of misclassified spikes for each of the identified clusters. We tested our algorithm on several datasets for which there exists a ground truth (simultaneous intracellular data), and show that it performs close to the optimum reached by a support vector machine trained on the ground truth. We also show that the estimated rate of misclassification matches the proportion of misclassified spikes measured from the ground truth data.
publisher	Public Library of Science
publishDate	2016
url	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4990262/
_version_	1613630103818862592

Consensus-Based Sorting of Neuronal Spike Waveforms

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