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Spike Clustering and Neuron Tracking over Successive Time Windows

Wolf, Michael T. and Burdick, Joel W. (2007) Spike Clustering and Neuron Tracking over Successive Time Windows. In: 3rd International IEEE/EMBS Conference on Neural Engineering, 2007. CNE '07. IEEE , Piscataway, NJ, pp. 659-665. ISBN 1424407923.

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This paper introduces a new methodology for tracking signals from individual neurons over time in multiunit extracellular recordings. The core of our strategy relies upon an extension of a traditional mixture model approach, with parameter optimization via expectation-maximimization (EM), to incorporate clustering results from the preceding time period in a Bayesian manner. EM initialization is also achieved by utilizing these prior clustering results. After clustering, we match the current and prior clusters to track persisting neurons. Applications of this spike sorting method to recordings from macaque parietal cortex show that it provides significantly more consistent clustering and tracking results.

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Additional Information:© 2007 IEEE. Issue Date: 2-5 May 2007; date of current version: 11 June 2007. We thank Richard Andersen and the members of his lab, particularly Grant Mulliken for collaboration and test data. This work is funded by the National Institutes of Health, grant R01 EY015545.
Funding AgencyGrant Number
NIHR01 EY015545
Subject Keywords:Bayes methods; expectation-maximisation algorithm; neural nets; neurophysiology
Record Number:CaltechAUTHORS:20100504-153301506
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Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:18128
Deposited By: Jason Perez
Deposited On:03 Jun 2010 21:41
Last Modified:03 Oct 2019 01:38

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