Bayesian modeling and classification of neural signals

Abstract

Identifying and classifying action potential shapes in extracellular neural waveforms have long been the subject of research, and although several algorithms for this purpose have been successfully applied, their use has been limited by some outstanding problems. The first is how to determine shapes of the action potentials in the waveform and, second, how to decide how many shapes are distinct. A harder problem is that action potentials frequently overlap making difficult both the determination of the shapes and the classification of the spikes. In this report, a solution to each of these problems is obtained by applying Bayesian probability theory. By defining a probabilistic model of the waveform, the probability of both the form and number of spike shapes can be quantified. In addition, this framework is used to obtain an efficient algorithm for the decomposition of arbitrarily complex overlap sequences. 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.

Additional Information

© 1994 Massachusetts Institute of Technology. Received June 29, 1993; accepted January 11, 1994. Posted Online April 4, 2008. I thank David MacKay for helpful discussions and encouragement during the early stages of this work, Jamie Mazer for many conversations and extensive help with the development of the software, and Matt Wilson for classifying the synthesized data set with Brainwave. Thanks also to Allison Doupe and Ken Miller for helpful feedback on the manuscript. This work was supported by Caltech fellowships and an NIH Research Training Grant

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