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

Overview

1. Allen Institute for Neural Dynamics Seattle United States

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. We also show that 7× 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.

Reproduced under the paper's license (CC BY), from the paper cited above.

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Data

Datasets cited

• hub.docker.com/u/spikeinterface, at hub.docker.com; found in the text, “Enabling technologies”

Data availability

Hybrid data for evaluating spike sorters and lossy compression are available at the following URL: https://registry.opendata.aws/allen-nd-ephys-hybrid-evaluation/. The code for the presented pipelines is available on GitHub2022; Spike sorting pipeline: https://github.com/AllenNeuralDynamics/aind-ephys-pipeline (Buccino et al., 2026a); Evaluation pipeline: https://github.com/AllenNeuralDynamics/aind-ephys-hybrid-benchmark (Buccino, 2026b). The figures from the results section can have been generated with the following Code Ocean capsule: https://codeocean.allenneuraldynamics.org/capsule/9554286/tree/v4. The spike sorting pipeline is also available in the CodeOcean Public Collections: https://codeocean.allenneuraldynamics.org/capsule/4364160/tree/v9.

The following dataset was generated:

Allen Institute 2026Allen Institute for Neural Dynamics - Extracellular Electrophysiology Hybrid Evaluation BenchmarkRegistry of Open Data on AWSallen-nd-ephys-hybrid-evaluation

Reproduced under the paper's license (CC BY), from the paper cited above.

Versions

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Version 3, 28 September 2026

• Funding: added Harvard University

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, pages, dates, 5 authors, 5 keywords, 7 MeSH terms, 58 references.

Cite

This paper

Buccino, A. P., Sridhar, A., Feng, D., Svoboda, K., & Siegle, J. H. (2026). Efficient and reproducible pipelines for spike sorting large-scale electrophysiology data. eLife, 15, RP110170. https://doi.org/10.7554/elife.110170

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