Bayesian modeling and classification of neural signals

Authors:
Michael S. Lewicki
Affiliations:
-
Venue:
Neural Computation
Year:
1994

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Abstract

Identifying and classifying action potential shapes inextracellular neural waveforms have long been the subject ofresearch, and although several algorithms for this purpose havebeen successfully applied, their use has been limited by someoutstanding problems. The first is how to determine shapes of theaction potentials in the waveform and, second, how to decide howmany shapes are distinct. A harder problem is that actionpotentials frequently overlap making difficult both thedetermination of the shapes and the classification of the spikes.In this report, a solution to each of these problems is obtained byapplying Bayesian probability theory. By defining a probabilisticmodel of the waveform, the probability of both the form and numberof spike shapes can be quantified. In addition, this framework isused to obtain an efficient algorithm for the decomposition ofarbitrarily complex overlap sequences. This algorithm can extractmany times more information than previous methods and facilitatesthe extracellular investigation of neuronal classes and ofinteractions within neuronal circuits.