Classification of non-stationary neural signals

Although a number of methods have been proposed for classification of indiv idual action potentials embedded in multi-unit activity, they have been cha llenged by non-stationarity. The waveform shapes of action potentials can c hange rapidly over time as a result of shifts in membrane conductances duri ng extended burst firing sequences and more slowly over time due to electro de 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 ne uron should not be discontinuous. We apply this algorithm to the resolution of multi-unit neural signals recorded in the cat visual cortex and we comp are this approach to a spike sorting method that is based on the Bayesian l ikelihood of a spike fitting a particular model (Lewicki, M. Bayesian model ing and classification of neural signals. Neural Comput 1994;6(5):1005-1030 ). (C) 1998 Elsevier Science B.V. All rights reserved.