1. Neuroscience
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SpikeInterface, a unified framework for spike sorting

  1. Alessio Paolo Buccino  Is a corresponding author
  2. Cole Lincoln Hurwitz
  3. Samuel Garcia
  4. Jeremy Magland
  5. Joshua H Siegle
  6. Roger Hurwitz
  7. Matthias H Hennig
  1. ETH Zurich, Switzerland
  2. University of Edinburgh, United Kingdom
  3. Université de Lyon, France
  4. Flatiron Institute, United States
  5. Allen Institute, United States
  6. Independent Researcher, United States
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Cite this article as: eLife 2020;9:e61834 doi: 10.7554/eLife.61834

Abstract

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.

Article and author information

Author details

  1. Alessio Paolo Buccino

    D-BSSE, ETH Zurich, Basel, Switzerland
    For correspondence
    alessio.buccino@bsse.ethz.ch
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-3661-527X
  2. Cole Lincoln Hurwitz

    Institute for Adaptive and Neural Computation, University of Edinburgh, Edinburgh, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-2023-1653
  3. Samuel Garcia

    Centre de Recherche en Neuroscience de Lyon, Université de Lyon, Lyon, France
    Competing interests
    The authors declare that no competing interests exist.
  4. Jeremy Magland

    Center for Computational Mathematics, Flatiron Institute, New York, United States
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-5286-4375
  5. Joshua H Siegle

    MindScope Program, Allen Institute, Seattle, United States
    Competing interests
    The authors declare that no competing interests exist.
  6. Roger Hurwitz

    Independent Researcher, Portland, United States
    Competing interests
    The authors declare that no competing interests exist.
  7. Matthias H Hennig

    Institute for Adaptive and Neural Computation, School of Informatics, University of Edinburgh, Edinburgh, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-7270-5817

Funding

Wellcome Trust (214431/Z/18/Z)

  • Matthias H Hennig

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Reviewing Editor

  1. Laura L Colgin, University of Texas at Austin, United States

Publication history

  1. Received: August 6, 2020
  2. Accepted: November 9, 2020
  3. Accepted Manuscript published: November 10, 2020 (version 1)

Copyright

? 2020, Buccino et al.

This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.

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