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Peer-reviewedCC BYeLife

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

  • Alessio Paolo Buccino1,2
  • Arjun Sridhar1,2
  • David H. Feng1,2
  • Karel Svoboda1,2
  • Joshua H. Siegle1,2
  1. 1Allen Institute for Neural Dynamics, Seattle, WA, USA
  2. 2Allen Institute for Neural Dynamics
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Abstract

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.

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Cite

BibTeXRIS
BibTeX
@article{buccino2026efficient,
  title = {{Efficient and reproducible pipelines for spike sorting large-scale electrophysiology data}},
  author = {Alessio Paolo Buccino and Arjun Sridhar and David H. Feng and Karel Svoboda and Joshua H. Siegle},
  journal = {eLife},
  year = {2026},
  month = jul,
  doi = {10.7554/elife.110170.3},
  url = {https://doi.org/10.7554/elife.110170.3},
  note = {CC BY},
}
RIS
TY  - JOUR
TI  - Efficient and reproducible pipelines for spike sorting large-scale electrophysiology data
AU  - Alessio Paolo Buccino
AU  - Arjun Sridhar
AU  - David H. Feng
AU  - Karel Svoboda
AU  - Joshua H. Siegle
PY  - 2026
DA  - 2026/07/13
JO  - eLife
DO  - 10.7554/elife.110170.3
UR  - https://doi.org/10.7554/elife.110170.3
AB  - 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.
SN  - 2050-084X
ER  -