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A Bayesian Clustering Method for Tracking Neural Signals Over Successive Intervals

Wolf, Michael T. and Burdick, Joel W. (2009) A Bayesian Clustering Method for Tracking Neural Signals Over Successive Intervals. IEEE Transactions on Biomedical Engineering, 56 (11). pp. 2649-2659. ISSN 0018-9294. https://resolver.caltech.edu/CaltechAUTHORS:20091110-122339888

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Abstract

This paper introduces a new, unsupervised method for sorting and tracking the action potentials of individual neurons in multiunit extracellular recordings. Presuming the data are divided into short, sequential recording intervals, the core of our strategy relies upon an extension of a traditional mixture model approach that incorporates clustering results from the preceding interval in a Bayesian manner, while still allowing for signal nonstationarity and changing numbers of recorded neurons. As a natural byproduct of the sorting method, current and prior signal clusters can be matched over time in order to track persisting neurons. We also develop techniques to use prior data to appropriately seed the clustering algorithm and select the model class. We present results in a principal components space; however, the algorithm may be applied in any feature space where the distribution of a neuron's spikes may be modeled as Gaussian. Applications of this signal classification method to recordings from macaque parietal cortex show that it provides significantly more consistent clustering and tracking results than traditional methods based on expectation–maximization optimization of mixture models. This consistent tracking ability is crucial for intended applications of the method.


Item Type:Article
Related URLs:
URLURL TypeDescription
http://dx.doi.org/10.1109/TBME.2009.2027604DOIArticle
Additional Information:© 2009 IEEE. Manuscript received March 31, 2008; revised December 22, 2008 and March 4, 2009; first published July 28, 2009; current version published October 16, 2009. This work was supported in part by the National Institutes of Health (NIH) under Grant R01 EY015545 and in part by the Rose Hills Foundation. The authors thank R. Andersen and his laboratory, particularly G. Mulliken, for collaboration and test data, and Z. Nadasdy and N. Hudson for several helpful discussions.
Funders:
Funding AgencyGrant Number
NIHR01 EY015545
Rose Hills FoundationUNSPECIFIED
Subject Keywords:Bayesian classification; clustering, expectation– maximization (EM); multitarget tracking; neuron tracking; spike sorting.
Issue or Number:11
Record Number:CaltechAUTHORS:20091110-122339888
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20091110-122339888
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:16643
Collection:CaltechAUTHORS
Deposited By: Jason Perez
Deposited On:13 Nov 2009 16:55
Last Modified:03 Oct 2019 01:15

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