Automatic classification of neural spike activity: an application of minimum distance classifiers

Electrophysiological recordings of extracellular neuronal activity often produce complex pattern caused both by the simultaneous firing of many neurons in the proximity of the recording electrode and by the superimposition of biological and instrumental noise onto the neural signals. This pattern complexity requires a fast evaluation of the classification results by the experimenter in order to decide how to proceed with the experiment. Euclidean and Mahalanobis minimum distance classifier methods, used in this context, follow a similar approach to the classification problem. A procedure is described by which both methods are applied, tested, and compared using simulated spike populations. The same procedure can be followed when analyzing real spike recordings.