TU Berlin

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:

A Maximum Entropy Test for Evaluating Higher-Order Correlations in Spike Counts
Citation key Onken20120
Author Onken, A. and Dragoi, V. and Obermayer, K.
Year 2012
DOI 10.1371/journal.pcbi.1002539
Journal PLoS Computational Biology
Volume 8
Abstract Evaluating the importance of higher-order correlations of neural spike counts has been notoriously hard. A large number of samples are typically required in order to estimate higher-order correlations and resulting information theoretic quantities. In typical electrophysiology data sets with many experimental conditions, however, the number of samples in each condition is rather small. Here we describe a method that allows to quantify evidence for higher-order correlations in exactly these cases. We construct a family of reference distributions: maximum entropy distributions, which are constrained only by marginals and by linear correlations as quantified by the Pearson correlation coefficient. We devise a Monte Carlo goodness-of-fit test, which tests - for a given divergence measure of interest - whether the experimental data lead to the rejection of the null hypothesis that it was generated by one of the reference distributions. Applying our test to artificial data shows that the effects of higher-order correlations on these divergence measures can be detected even when the number of samples is small. Subsequently, we apply our method to spike count data which were recorded with multielectrode arrays from the primary visual cortex of anesthetized cat during an adaptation experiment. Using mutual information as a divergence measure we find that there are spike count bin sizes at which the maximum entropy hypothesis can be rejected for a substantial number of neuronal pairs. These results demonstrate that higher-order correlations can matter when estimating information theoretic quantities in V1. They also show that our test is able to detect their presence in typical in-vivo data sets, where the number of samples is too small to estimate higher-order correlations directly.
Bibtex Type of Publication Selected:main selected:spikes selected:publications
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