Identifying and classifying action potential shapes in extracellular
neural waveforms has 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.
compressed postscript (25 pages, 630kB)
For further work on modeling the shape of action potentials see:
D. MacKay and R. Takeuchi: ``Interpolation models with multiple hyperparameters.'' newint.ps.gz, abstract