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).