D.sort: template based automatic spike sorting tool

Abstract

Understanding how populations of neurons represent and compute internal or external variables requires precise and objective metrics for tracing the individual spikes that belong to a given neuron. Despite recent progress in the development of accurate and fast spike sorting tools, the scarcity of ground truth data makes it difficult to settle on the best performing spike sorting algorithm. Besides, the use of different configurations of electrodes and ways to acquire signal (e.g. anesthetized, head fixed, freely behaving animal recordings, tetrode vs. silicone probes, etc.) makes it even harder to develop a universal spike sorting tool that will perform well without human intervention. Some of the prevalent problems in spike sorting are: units separating due to drift, clustering bursting cells, and dealing with nonstationarity in background noise. The last is particularly problematic in freely behaving animals where the noises from the electrophysiological activity of hundreds or thousands of neurons are intermixed with noise arising from movement artifacts. We address these problems by developing a new spike sorting tool that is based on a template matching algorithm. The spike waveform templates are used to perform normalized cross correlation (NCC) with an acquired signal for spike detection. The normalization addresses problems with drift, bursting, and nonstationarity of noise and provides normative scoring to compare different units in terms of cluster quality. Our spike sorting algorithm, D.sort, runs on the graphic processing unit (GPU) to accelerate computations. D.sort is a freely available software package (https://github.com/1804MB/Kvistiani-lab_Dsort).