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

作者：Julien Boussard, Charlie Windolf, Cole Hurwitz, Hyun Dong Lee, Yu Han, Olivier Winter, Liam Paninski · 发表于：bioRxiv (Cold Spring Harbor Laboratory) · 年份：2023 · DOI：10.1101/2023.08.11.553023 · 被引用次数：16 · 研究领域：Advanced Memory and Neural Computing、Neural dynamics and brain function、Electrowetting and Microfluidic Technologies

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, DART sort ( D rift A ware R egistration and T racking), 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 .