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An online spike detection and spike classification algorithm capable of instantaneous resolution of overlapping spikes
For the analysis of neuronal cooperativity, simultaneously recorded extracellular signals from neighboring neurons need to be sorted reliably by a spike sorting method. Many algorithms have been developed to this end, however, to date, none of them manages to fulfill a set of demanding requirements....
Autores principales: | , , , , |
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Formato: | Texto |
Lenguaje: | English |
Publicado: |
Springer US
2009
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2950077/ https://www.ncbi.nlm.nih.gov/pubmed/19499318 http://dx.doi.org/10.1007/s10827-009-0163-5 |
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author | Franke, Felix Natora, Michal Boucsein, Clemens Munk, Matthias H. J. Obermayer, Klaus |
author_facet | Franke, Felix Natora, Michal Boucsein, Clemens Munk, Matthias H. J. Obermayer, Klaus |
author_sort | Franke, Felix |
collection | PubMed |
description | For the analysis of neuronal cooperativity, simultaneously recorded extracellular signals from neighboring neurons need to be sorted reliably by a spike sorting method. Many algorithms have been developed to this end, however, to date, none of them manages to fulfill a set of demanding requirements. In particular, it is desirable to have an algorithm that operates online, detects and classifies overlapping spikes in real time, and that adapts to non-stationary data. Here, we present a combined spike detection and classification algorithm, which explicitly addresses these issues. Our approach makes use of linear filters to find a new representation of the data and to optimally enhance the signal-to-noise ratio. We introduce a method called “Deconfusion” which de-correlates the filter outputs and provides source separation. Finally, a set of well-defined thresholds is applied and leads to simultaneous spike detection and spike classification. By incorporating a direct feedback, the algorithm adapts to non-stationary data and is, therefore, well suited for acute recordings. We evaluate our method on simulated and experimental data, including simultaneous intra/extra-cellular recordings made in slices of a rat cortex and recordings from the prefrontal cortex of awake behaving macaques. We compare the results to existing spike detection as well as spike sorting methods. We conclude that our algorithm meets all of the mentioned requirements and outperforms other methods under realistic signal-to-noise ratios and in the presence of overlapping spikes. |
format | Text |
id | pubmed-2950077 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2009 |
publisher | Springer US |
record_format | MEDLINE/PubMed |
spelling | pubmed-29500772010-10-07 An online spike detection and spike classification algorithm capable of instantaneous resolution of overlapping spikes Franke, Felix Natora, Michal Boucsein, Clemens Munk, Matthias H. J. Obermayer, Klaus J Comput Neurosci Article For the analysis of neuronal cooperativity, simultaneously recorded extracellular signals from neighboring neurons need to be sorted reliably by a spike sorting method. Many algorithms have been developed to this end, however, to date, none of them manages to fulfill a set of demanding requirements. In particular, it is desirable to have an algorithm that operates online, detects and classifies overlapping spikes in real time, and that adapts to non-stationary data. Here, we present a combined spike detection and classification algorithm, which explicitly addresses these issues. Our approach makes use of linear filters to find a new representation of the data and to optimally enhance the signal-to-noise ratio. We introduce a method called “Deconfusion” which de-correlates the filter outputs and provides source separation. Finally, a set of well-defined thresholds is applied and leads to simultaneous spike detection and spike classification. By incorporating a direct feedback, the algorithm adapts to non-stationary data and is, therefore, well suited for acute recordings. We evaluate our method on simulated and experimental data, including simultaneous intra/extra-cellular recordings made in slices of a rat cortex and recordings from the prefrontal cortex of awake behaving macaques. We compare the results to existing spike detection as well as spike sorting methods. We conclude that our algorithm meets all of the mentioned requirements and outperforms other methods under realistic signal-to-noise ratios and in the presence of overlapping spikes. Springer US 2009-06-05 2010 /pmc/articles/PMC2950077/ /pubmed/19499318 http://dx.doi.org/10.1007/s10827-009-0163-5 Text en © The Author(s) 2009 https://creativecommons.org/licenses/by-nc/4.0/ This article is distributed under the terms of the Creative Commons Attribution Noncommercial License which permits any noncommercial use, distribution, and reproduction in any medium, provided the original author(s) and source are credited. |
spellingShingle | Article Franke, Felix Natora, Michal Boucsein, Clemens Munk, Matthias H. J. Obermayer, Klaus An online spike detection and spike classification algorithm capable of instantaneous resolution of overlapping spikes |
title | An online spike detection and spike classification algorithm capable of instantaneous resolution of overlapping spikes |
title_full | An online spike detection and spike classification algorithm capable of instantaneous resolution of overlapping spikes |
title_fullStr | An online spike detection and spike classification algorithm capable of instantaneous resolution of overlapping spikes |
title_full_unstemmed | An online spike detection and spike classification algorithm capable of instantaneous resolution of overlapping spikes |
title_short | An online spike detection and spike classification algorithm capable of instantaneous resolution of overlapping spikes |
title_sort | online spike detection and spike classification algorithm capable of instantaneous resolution of overlapping spikes |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2950077/ https://www.ncbi.nlm.nih.gov/pubmed/19499318 http://dx.doi.org/10.1007/s10827-009-0163-5 |
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