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SpikeInterface, a unified framework for spike sorting

Research output: Contribution to journalArticlepeer-review

  • Alessio P. Buccino
  • Cole L. Hurwitz
  • Samuel Garcia
  • Jeremy Magland
  • Joshua H. Siegle
  • Roger Hurwitz
  • Matthias H. Hennig

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    Licence: Creative Commons: Attribution (CC-BY)

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Original languageEnglish
Article numbere61834
Number of pages24
Early online date10 Nov 2020
Publication statusPublished - 30 Nov 2020


Much development has been directed towards improving the performance and automation of spike sorting. This continuous development, while essential, has contributed to an over-saturation of new, incompatible tools that hinders rigorous benchmarking and complicates reproducible analysis. To address these limitations, we developed SpikeInterface, a Python framework designed to unify preexisting spike sorting technologies into a single codebase and to facilitate straightforward comparison and adoption of different approaches. With a few lines of code, researchers can reproducibly run, compare, and benchmark most modern spike sorting algorithms; pre-process, post-process, and visualize extracellular datasets; validate, curate, and export sorting outputs; and more. In this paper, we provide an overview of SpikeInterface and, with applications to real and simulated datasets, demonstrate how it can be utilized to reduce the burden of manual curation and to more comprehensively benchmark automated spike sorters.

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