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Spike Sorting and Spike Train Analysis

In this project we develop new methods for the fast and reliable analysis of extracellular data. We currently focus on the detection and classification of action potentials in voltage traces which are recorded simultaneously, for example, using multi-electrode and multi-tetrode arrays. Our approach makes use of linear filters to find a new representation of the data and to optimally enhance the signal-to-noise ratio, makes use of source separation techniques to decorrelate filter outputs. The developed methods are able to separate overlapping spikes and can adapt to non-stationary data. Therefore, they are well suited for acute recordings, where they allow for on-line spike-sorting and -analysis. Together with Dr. M. Munk (MPI for Biological Cybernetics) our methods are currently being evaluated on data recorded from awake behaving monkeys during visual working memory tasks. Current collaboration partners include but are not limited to the University of Freiburg, the University of Oslo, the Max Planck Institute for Biological Cybernetics (Tübingen), Thomas RECORDING GmbH (Gießen) and the German Neuroscience Node (GNode).

Acknowledgements: Research is funded by BMBF (via the Bernstein Center and a Bernstein Collaboration) and the Technische Universität Berlin.

Selected Publications:

An Online Spike Detection and Spike Classification Algorithm Capable of Instantaneous Resolution of Overlapping Spikes
Citation key Franke2010b0
Author Franke, F. and Natora, M. and Boucsein, C. and Munk, M. and Obermayer, K.
Pages 127 – 148
Year 2010
DOI 10.1007/s10827-009-0163-5
Journal Journal of Computional Neuroscience
Publisher Springer
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-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.
Bibtex Type of Publication Selected:main selected:spikes selected:publications
Link to publication Download Bibtex entry

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