AUTOMATIC SORTING OF MULTIPLE-UNIT NEURONAL SIGNALS IN THE PRESENCE OF ANISOTROPIC AND NON-GAUSSIAN VARIABILITY

Neuronal noise sources and systematic variability in the shape of a sp ike limit the ability to sort multiple unit waveforms recorded from ne rvous tissue into their single neuron constituents. Here we present a procedure to efficiently sort spikes in the presence of noise that is anisotropic, i.e., dominated by particular frequencies, and whose ampl itude distribution may be non-Gaussian, such as occurs when spike wave forms are a function of interspike interval. Our algorithm uses a hier archical clustering scheme. First, multiple unit records are sorted in to an overly large number of clusters by recursive bisection. Second, these clusters are progressively aggregated into a minimal set of puta tive single units based on both similarities of spike shape as well as the statistics of spike arrival times, such as imposed by the refract ory period. We apply the algorithm to waveforms recorded with chronica lly implanted micro-wire stereotrodes from neocortex of behaving rat. Natural extensions of the algorithm may be used to cluster spike wavef orms from records with many input channels, such as those obtained wit h tetrodes and multiple site optical techniques.