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Neural Information ProcessingSpike Sorting and Spike Train Analysis

Neuronale Informationsverarbeitung

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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:

Spike Sorting of Multi-Site Electrode Recordings
Citation key Vollgraf2005
Author Vollgraf, R. and Munk, M. and Obermayer, K.
Pages 85 – 113
Year 2005
Journal Network - Computation in Neural Systems
Volume 16
Abstract We derive an optimal linear filter, in order to reduce the distortions of the peak amplitudes of action potentials in extra-cellular multitrode recordings, which are due to background activity and overlapping spikes. This filter is learned very efficiently from the raw recordings in an unsupervised manner, and responds to the average wave form with an impulse of minimal width. The average wave form does not have to be known in advance, but is learned together with the optimal filter. The peak amplitude of a filtered wave form is a more reliable estimate for the amplitude of an action potential than the peak of the biphasic wave form and can improve the accuracy of the event detection and clustering procedures. We demonstrate a spike sorting application, in which events are detected using the Mahalanobis distance in the $N$-dimensional space of filtered recordings as a distance measure, and the event amplitudes of the filtered recordings are clustered in order to assign events to individual units. This method is fast and robust, and we show its performance by applying it to real tetrode recordings of spontaneous activity in the visual cortex of an anesthetized cat and to realistic artificial data derived therefrom.
Bibtex Type of Publication Selected:spikes
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