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J Neurosci Methods. 1998 Oct 1;84(1-2):155-66.

Classification of non-stationary neural signals.

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Department of Electrical and Computer Engineering, Vanderbilt University, Nashville, TN 37235, USA.


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.

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