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SpikeInterface, a unified framework for spike sorting

View ORCID ProfileAlessio P. Buccino, Cole L. Hurwitz, Samuel Garcia, Jeremy Magland, Joshua H. Siegle, Roger Hurwitz, Matthias H. Hennig
doi: https://doi.org/10.1101/796599
Alessio P. Buccino
1Department of Biosystems Science and Engineering, ETH Zurich, Switzerland
6Centre for Integrative Neuroplasticity (CINPLA), University of Oslo, Oslo, Norway
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  • ORCID record for Alessio P. Buccino
  • For correspondence: alessio.buccino@bsse.ethz.ch
Cole L. Hurwitz
2School of Informatics, University of Edinburgh, United Kingdom
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Samuel Garcia
3Centre de Recherche en Neuroscience de Lyon, CNRS, Lyon, France
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Jeremy Magland
4Flatiron Institute, New York City, NY, United States
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Joshua H. Siegle
5Allen Institute for Brain Science, Seattle, WA, United States
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Roger Hurwitz
7Independent Researcher, Portland, Oregon, USA
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Matthias H. Hennig
2School of Informatics, University of Edinburgh, United Kingdom
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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.

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • In this revision, we present a new analysis that substantially extends our previous finding that different spike sorters show surprisingly little agreement when run on the same data. A major new finding we report here is that a consensus sorting derived from multiple sorters is a very effective method to remove false positives from these data sets. We demonstrate this both using synthetic and manually curated data sets. Importantly, the software framework we developed and present here makes this type of analysis easy and accessible to non-experts.

  • https://gui.dandiarchive.org/#/dandiset/5f1df18ef63d62e1dbd0694a

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license.
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Posted August 10, 2020.
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SpikeInterface, a unified framework for spike sorting
Alessio P. Buccino, Cole L. Hurwitz, Samuel Garcia, Jeremy Magland, Joshua H. Siegle, Roger Hurwitz, Matthias H. Hennig
bioRxiv 796599; doi: https://doi.org/10.1101/796599
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SpikeInterface, a unified framework for spike sorting
Alessio P. Buccino, Cole L. Hurwitz, Samuel Garcia, Jeremy Magland, Joshua H. Siegle, Roger Hurwitz, Matthias H. Hennig
bioRxiv 796599; doi: https://doi.org/10.1101/796599

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