A Caltech Library Service

Bayesian clustering and tracking of neuronal signals for autonomous neural interfaces

Wolf, Michael T. and Burdick, Joel W. (2008) Bayesian clustering and tracking of neuronal signals for autonomous neural interfaces. In: 47th IEEE Conference on Decision and Control. IEEE , Piscataway, NJ, pp. 1992-1999. ISBN 978-1-4244-3123-6.

[img] PDF - Published Version
See Usage Policy.


Use this Persistent URL to link to this item:


This paper introduces a new, unsupervised method for sorting and tracking the non-stationary spike signals of individual neurons in multi-unit extracellular recordings. While this method may be applied to a variety of problems that arise in the field of neural interfaces, its development is motivated by a new class of autonomous neural recording devices. The core of the proposed strategy relies upon an extension of a traditional expectation-maximization (EM) mixture model optimization to incorporate clustering results from the preceding recording interval in a Bayesian manner. Explicit filtering equations for the case of a Gaussian mixture are derived. Techniques using prior data to seed the EM iterations and to select the appropriate model class are also developed. As a natural byproduct of the sorting method, current and prior signal clusters can be matched over time in order to track persisting neurons. 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.

Item Type:Book Section
Related URLs:
URLURL TypeDescription
Additional Information:© 2008 IEEE. This work was completed at the California Institute of Technology with support from the National Institutes of Health and the Rose Hills Foundation.
Funding AgencyGrant Number
Rose Hills FoundationUNSPECIFIED
Record Number:CaltechAUTHORS:20170417-173457520
Persistent URL:
Official Citation:M. T. Wolf and J. W. Burdick, "Bayesian clustering and tracking of neuronal signals for autonomous neural interfaces," 2008 47th IEEE Conference on Decision and Control, Cancun, 2008, pp. 1992-1999. doi: 10.1109/CDC.2008.4739362
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:76622
Deposited On:18 Apr 2017 01:48
Last Modified:15 Nov 2021 17:01

Repository Staff Only: item control page