An automatic spike sorting algorithm based on adaptive spike detection and a mixture of skew-t distributions

Abstract Developing high-density electrodes for recording large ensembles of neurons provides a unique opportunity for understanding the mechanism of the neuronal circuits. Nevertheless, the change of brain tissue around chronically implanted neural electrodes usually causes spike wave-shape distort...

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Autores principales:	Ramin Toosi, Mohammad Ali Akhaee, Mohammad-Reza A. Dehaqani
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/b25c9a4628b1425b9fc796e0d8a33965
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spelling	oai:doaj.org-article:b25c9a4628b1425b9fc796e0d8a339652021-12-02T18:33:57ZAn automatic spike sorting algorithm based on adaptive spike detection and a mixture of skew-t distributions10.1038/s41598-021-93088-w2045-2322https://doaj.org/article/b25c9a4628b1425b9fc796e0d8a339652021-07-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-93088-whttps://doaj.org/toc/2045-2322Abstract Developing high-density electrodes for recording large ensembles of neurons provides a unique opportunity for understanding the mechanism of the neuronal circuits. Nevertheless, the change of brain tissue around chronically implanted neural electrodes usually causes spike wave-shape distortion and raises the crucial issue of spike sorting with an unstable structure. The automatic spike sorting algorithms have been developed to extract spikes from these big extracellular data. However, due to the spike wave-shape instability, there have been a lack of robust spike detection procedures and clustering to overcome the spike loss problem. Here, we develop an automatic spike sorting algorithm based on adaptive spike detection and a mixture of skew-t distributions to address these distortions and instabilities. The adaptive detection procedure applies to the detected spikes, consists of multi-point alignment and statistical filtering for removing mistakenly detected spikes. The detected spikes are clustered based on the mixture of skew-t distributions to deal with non-symmetrical clusters and spike loss problems. The proposed algorithm improves the performance of the spike sorting in both terms of precision and recall, over a broad range of signal-to-noise ratios. Furthermore, the proposed algorithm has been validated on different datasets and demonstrates a general solution to precise spike sorting, in vitro and in vivo.Ramin ToosiMohammad Ali AkhaeeMohammad-Reza A. DehaqaniNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-18 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Ramin Toosi Mohammad Ali Akhaee Mohammad-Reza A. Dehaqani An automatic spike sorting algorithm based on adaptive spike detection and a mixture of skew-t distributions
description	Abstract Developing high-density electrodes for recording large ensembles of neurons provides a unique opportunity for understanding the mechanism of the neuronal circuits. Nevertheless, the change of brain tissue around chronically implanted neural electrodes usually causes spike wave-shape distortion and raises the crucial issue of spike sorting with an unstable structure. The automatic spike sorting algorithms have been developed to extract spikes from these big extracellular data. However, due to the spike wave-shape instability, there have been a lack of robust spike detection procedures and clustering to overcome the spike loss problem. Here, we develop an automatic spike sorting algorithm based on adaptive spike detection and a mixture of skew-t distributions to address these distortions and instabilities. The adaptive detection procedure applies to the detected spikes, consists of multi-point alignment and statistical filtering for removing mistakenly detected spikes. The detected spikes are clustered based on the mixture of skew-t distributions to deal with non-symmetrical clusters and spike loss problems. The proposed algorithm improves the performance of the spike sorting in both terms of precision and recall, over a broad range of signal-to-noise ratios. Furthermore, the proposed algorithm has been validated on different datasets and demonstrates a general solution to precise spike sorting, in vitro and in vivo.
format	article
author	Ramin Toosi Mohammad Ali Akhaee Mohammad-Reza A. Dehaqani
author_facet	Ramin Toosi Mohammad Ali Akhaee Mohammad-Reza A. Dehaqani
author_sort	Ramin Toosi
title	An automatic spike sorting algorithm based on adaptive spike detection and a mixture of skew-t distributions
title_short	An automatic spike sorting algorithm based on adaptive spike detection and a mixture of skew-t distributions
title_full	An automatic spike sorting algorithm based on adaptive spike detection and a mixture of skew-t distributions
title_fullStr	An automatic spike sorting algorithm based on adaptive spike detection and a mixture of skew-t distributions
title_full_unstemmed	An automatic spike sorting algorithm based on adaptive spike detection and a mixture of skew-t distributions
title_sort	automatic spike sorting algorithm based on adaptive spike detection and a mixture of skew-t distributions
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/b25c9a4628b1425b9fc796e0d8a33965
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An automatic spike sorting algorithm based on adaptive spike detection and a mixture of skew-t distributions

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