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  An online spike detection and spike classification algorithm capable of instantaneous resolution of overlapping spikes

Franke, F., Natora, M., Boucsein, C., Munk, M., & Obermayer, K. (2010). An online spike detection and spike classification algorithm capable of instantaneous resolution of overlapping spikes. Journal of Computational Neuroscience, 29(1-2), 127-148. doi:10.1007/s10827-009-0163-5.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0013-BE98-D Version Permalink: http://hdl.handle.net/21.11116/0000-0002-6AC7-7
Genre: Journal Article

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Franke, F, Author
Natora, M, Author
Boucsein , C, Author
Munk, MHJ1, 2, Author              
Obermayer, K, Author
Affiliations:
1Department Physiology of Cognitive Processes, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497798              
2Max Planck Institute for Biological Cybernetics, Max Planck Society, Spemannstrasse 38, 72076 Tübingen, DE, ou_1497794              

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 Abstract: 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-sta tionary 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.

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 Dates: 2010-08
 Publication Status: Published in print
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 Rev. Method: -
 Identifiers: DOI: 10.1007/s10827-009-0163-5
BibTex Citekey: 6144
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Title: Journal of Computational Neuroscience
Source Genre: Journal
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Publ. Info: Boston : Kluwer Academic Publishers
Pages: - Volume / Issue: 29 (1-2) Sequence Number: - Start / End Page: 127 - 148 Identifier: ISSN: 0929-5313
CoNE: https://pure.mpg.de/cone/journals/resource/954925568787