Quality Metrics to Accompany Spike Sorting of Extracellular Signals

Abstract

Special emphasis is placed on visualization schemes for spike data as well as on a set of metrics to estimate the number of incorrectly categorized spikes. False-negative contributions to a cluster lead to a suppression of inferred spike rates, while false-positive contributions lead to a distortion in the inferred receptive field for the cell. Both errors reduce the estimated information carried by the cell. A matrix of values for these metrics allows readers to assess claims, e.g., the size and reliability of multiple peaks in a receptive field, relative to the level of contamination of the data.