Biology

Spike sorting with Kilosort4

M. Pachitariu, S. Sridhar, et al.

Discover the cutting-edge Kilosort4 framework, developed by Marius Pachitariu and his team, which revolutionizes spike sorting in neuroscience. This new version leverages graph-based clustering for unparalleled accuracy in identifying neuron firing times, even in challenging conditions. Learn how it consistently outperforms existing algorithms in realistic simulations!

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Abstract

Spike sorting, crucial in neuroscience, extracts single neuron firing times from local electrical field recordings. This is challenging due to recording non-stationarity and overlapping electrical fields. The Kilosort framework addresses this, and this paper details algorithmic steps across Kilosort versions. Kilosort4, a new version with improved performance using graph-based clustering, is presented. Realistic simulations, using densely sampled electrical fields from real experiments, were used to benchmark Kilosort against other algorithms. Kilosort4 consistently outperformed others, accurately identifying even low-amplitude, small spatial extent neurons under high drift conditions.

Publisher

Nature Methods

Published On

Apr 08, 2024

Authors

Marius Pachitariu, Shashwat Sridhar, Jacob Pennington, Carsen Stringer

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