Taller Peaks: an improved spike detection algorithm that simultaneously reduces type I and type II errors for Wave clus

Abstract

Wave clus is an unsupervised spike detection and sorting algorithm that has been used in dozens of experimental studies as a spike sorting tool. It is often used as a benchmark for comparing the performance of new spike sorting algorithms. For these reasons, the spike detection performance of Wave clus is important for both experimental and computational studies that involve spike sorting. Two measures of spike detection performance are the number of false positive detections (type I error) and the number of missed spikes (type II error). Here, a new spike detection algorithm is proposed that reduces the number of misses and false positives of Wave clus in a widely used simulated data set across the entire range of commonly used detection thresholds. The algorithm accepts a spike if its amplitude is larger than the amplitude of its two immediate neighbors, where an immediate neighbor is the nearest peak of the same polarity within ±1 refractory period. The simultaneous reduction that is achieved in the number of false positives and misses is important for experimental and computational studies that use Wave clus as a spike sorting tool or as a benchmark. A software patch that incorporates the algorithm into Wave clus as an optional spike detection algorithm is provided.

Wave clus is an unsupervised spike detection and sorting algorithm that has been used in dozens of experimental studies as a spike sorting tool. It is often used as a benchmark for comparing the performance of new spike sorting algorithms. For these reasons, the spike detection performance of Wave clus is important for both experimental and computational studies that involve spike sorting. Two measures of spike detection performance are the number of false positive detections (type I error) and the number of missed spikes (type II error). Here, a new spike detection algorithm is proposed that reduces the number of misses and false positives of Wave clus in a widely used simulated data set across the entire range of commonly used detection thresholds. The algorithm accepts a spike if its amplitude is larger than the amplitude of its two immediate neighbors, where an immediate neighbor is the nearest peak of the same polarity within ±1 refractory period. The simultaneous reduction that is achieved in the number of false positives and misses is important for experimental and computational studies that use Wave clus as a spike sorting tool or as a benchmark. A software patch that incorporates the algorithm into Wave clus as an optional spike detection algorithm is provided.