DARTsort: A modular drift tracking spike sorter for high-density multi-electrode probes

Abstract

With the advent of high-density, multi-electrode probes, there has been a renewed interest in developing robust and scalable algorithms for spike sorting. Current spike sorting approaches, however, struggle to deal with noisy recordings and probe motion (drift). Here we introduce a modular and interpretable spike sorting pipeline, DARTsort (Drift Aware Registration and Tracking), that builds upon recent advances in denoising, spike localization, and drift estimation. DARTsort integrates a precise estimate of probe drift over time into a model of the spiking signal. This allows our method to be robust to drift across a variety of probe geometries. We show that our spike sorting algorithm outperforms a current state-of-the-art spike sorting algorithm, Kilosort 2.5, on simulated datasets with different drift types and noise levels. Open-source code can be found at https://github.com/cwindolf/dartsort.