title: Classification of non-stationary neural signals.
abstract: Although a number of methods have been proposed for classification of individual action potentials embedded in multi-unit activity, they have been challenged by non-stationarity. The waveform shapes of action potentials can change rapidly over time as a result of shifts in membrane conductances during extended burst firing sequences and more slowly over time due to electrode drift. These changes are typically non-Gaussian. We present an algorithm for waveform identification that makes no assumptions on the distribution of these shapes other than the change in waveform shape for a particular neuron should not be discontinuous. We apply this algorithm to the resolution of multi-unit neural signals recorded in the cat visual cortex and we compare this approach to a spike sorting method that is based on the Bayesian likelihood of a spike fitting a particular model (Lewicki, M. Bayesian modeling and classification of neural signals. Neural Comput 1994;6(5):1005-1030.
authors.affiliation: Department of Electrical and Computer Engineering, Vanderbilt University, Nashville, TN 37235, USA.
authors.forename: R K
authors.initials: RK
authors.lastname: Snider
authors.forename: A B
authors.initials: AB
authors.lastname: Bonds
mesh_terms: D000200:Action Potentials / Q000502:physiology*; D000465:Algorithms; D000818:Animals; D001499:Bayes Theorem; D002415:Cats; D002462:Cell Membrane / Q000502:physiology; D004594:Electrophysiology / Q000379:methods; D008959:Models, Neurological; D015233:Models, Statistical; D009474:Neurons / Q000502:physiology*; D016011:Normal Distribution; D015203:Reproducibility of Results; D014793:Visual Cortex / Q000502:physiology*