CaltechAUTHORS
  A Caltech Library Service

Bayesian Modeling and Classification of Neural Signals

Lewicki, Michael S. (1994) Bayesian Modeling and Classification of Neural Signals. In: Advances in Neural Information Processing Systems 6. Morgan Kaufmann , San Francisco, CA, pp. 590-597. ISBN 1-55860-322-0. https://resolver.caltech.edu/CaltechAUTHORS:20150305-162148896

[img] PDF - Published Version
See Usage Policy.

1588Kb

Use this Persistent URL to link to this item: https://resolver.caltech.edu/CaltechAUTHORS:20150305-162148896

Abstract

Signal processing and classification algorithms often have limited applicability resulting from an inaccurate model of the signal's underlying structure. We present here an efficient, Bayesian algorithm for modeling a signal composed of the superposition of brief, Poisson-distributed functions. This methodology is applied to the specific problem of modeling and classifying extracellular neural waveforms which are composed of a superposition of an unknown number of action potentials CAPs). Previous approaches have had limited success due largely to the problems of determining the spike shapes, deciding how many are shapes distinct, and decomposing overlapping APs. A Bayesian solution to each of these problems is obtained by inferring a probabilistic model of the waveform. This approach quantifies the uncertainty of the form and number of the inferred AP shapes and is used to obtain an efficient method for decomposing complex overlaps. This algorithm can extract many times more information than previous methods and facilitates the extracellular investigation of neuronal classes and of interactions within neuronal circuits.


Item Type:Book Section
Related URLs:
URLURL TypeDescription
http://papers.nips.cc/paper/777-bayesian-modeling-and-classification-of-neural-signalsOrganizationArticle
Additional Information:© 1994 Morgan Kaufmann. I thank David MacKay for helpful discussions and Jamie Mazer for many conversations and extensive help with the development of the software. This work was supported by Caltech fellowships and an NIH Research Training Grant.
Funders:
Funding AgencyGrant Number
NIHUNSPECIFIED
CaltechUNSPECIFIED
Record Number:CaltechAUTHORS:20150305-162148896
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20150305-162148896
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:55566
Collection:CaltechAUTHORS
Deposited By: Kristin Buxton
Deposited On:06 Mar 2015 05:24
Last Modified:03 Oct 2019 08:06

Repository Staff Only: item control page