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bioRxiv · 10.1101/2025.11.12.687966

Efficient and reproducible pipelines for spike sorting large-scale electrophysiology data

Buccino, A. P.·Sridhar, A.·Feng, D.·Svoboda, K.·Siegle, J. H.
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Abstract

The scale of in vivo electrophysiology has expanded in recent years, with simultaneous recordings across thousands of electrodes now becoming routine. These advances have enabled a wide range of discoveries, but they also impose substantial computational demands. Spike sorting, the procedure that extracts spikes from extracellular voltage measurements, remains a major bottleneck: a dataset collected in a few hours can take days to spike sort on a single machine, and the field lacks rigorous validation of the many spike sorting algorithms and preprocessing steps that are in use. Advancing the speed and accuracy of spike sorting is essential to fully realize the potential of large-scale electrophysiology. Here, we present an end-to-end spike sorting pipeline that leverages parallelization to scale to large datasets. The same workflow can run reproducibly on individual workstations, high-performance computing clusters, or cloud environments, with computing resources tailored to each processing step to reduce costs and execution times. In addition, we introduce a benchmarking pipeline, also optimized for parallel processing, that enables systematic comparison of multiple sorting pipelines. Using this framework, we show that Kilosort4, a widely used spike sorting algorithm, outperforms Kilosort2.5 (Pachitariu et al. 2024). We also show that 7x lossy compression, which substantially reduces the cost of data storage, has minimal impact on spike sorting performance. Together, these pipelines address the urgent need for scalable and transparent spike sorting of electrophysiology data, preparing the field for the coming flood of multi-thousand-channel experiments.

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Buccino, A. P., Sridhar, A., Feng, D., Svoboda, K., Siegle, J. H.. 2025-11-13. Efficient and reproducible pipelines for spike sorting large-scale electrophysiology data. https://doi.org/10.1101/2025.11.12.687966

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