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Neural Information ProcessingSpike Sorting and Spike Train Analysis

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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 Automated Online Positioning System and Simulation Environment for Multi-Electrodes in Extracellular Recordings
Citation key Franke2010
Author Franke, F. and Natora, M. and Meier, P. and Hagen, E. and Pettersen, K.H. and Linden, H. and Einevoll, G.T. and Obermayer, K.
Title of Book Proceedings of 32nd Annual International Conference of the IEEE EMBS
Pages 593 – 597
Year 2010
ISBN 978-1-4244-4123-5
ISSN 1094-687X
DOI 10.1109/IEMBS.2010.5626631
Journal 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology
Volume 2010
Month August
Editor IEEE
Abstract Extracellular recordings are a key tool to record the activity of neurons in vivo. Especially in the case of experiments with behaving animals, however, the tedious procedure of electrode placement can take a considerable amount of expensive and restricted experimental time. Furthermore, due to tissue drifts and other sources of variability in the recording setup, the position of the electrodes with respect to the recorded neurons can change causing low recording quality. The contributions of this work are threefold. We introduce a quality measure for the recording position of the electrode which should be maximized during recordings and is especially suitable for the use of multi-electrodes. An automated positioning system based on this quality measure is proposed. The system is able to find favorable recording positions and adapts the electrode position smoothly to changes of the neuron positions. Finally, we evaluate the system using a new simulator for extracellular recordings based on realistically reconstructed 3D neurons.
Bibtex Type of Publication Selected:spikes
Link to publication Link to original publication Download Bibtex entry


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