SpikeInterface, a unified framework for spike sorting

Abstract
Data availability
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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.

Data availability

All data generated or analysed during this study are included in the manuscript and supporting files. The datasets are uploaded to the DANDI archive, dataset 000034 (https://gui.dandiarchive.org/#/dandiset/000034). The source code for generating all figures is also publicly available at: https://spikeinterface.github.io/

The following previously published data sets were used

1. Marques-Smith A
2. Neto JP
3. Lopes G
4. Nogueira J
5. Marques-Smith A
6. Neto JP
7. Lopes G
8. Nogueira J
9. Calcaterra L
10. Frazão J
11. Kim D
12. Phillips MG
13. Dimitriadis G
14. Kampff AR
(2018) Simultaneous patch-clamp and dense CMOS probe extracellular recordings from the same cortical neuron in anaesthetized rats
CRCNS, spe-1.

http://dx.doi.org/10.6080/K0J67F4T

Article and author information

Author details

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.

"This ORCID iD identifies the author of this article:" 0000-0003-3661-527X
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.

"This ORCID iD identifies the author of this article:" 0000-0002-2023-1653
Samuel Garcia

Centre de Recherche en Neuroscience de Lyon, Université de Lyon, Lyon, France

Competing interests
The authors declare that no competing interests exist.
Jeremy Magland

Center for Computational Mathematics, Flatiron Institute, New York, United States

Competing interests
The authors declare that no competing interests exist.

"This ORCID iD identifies the author of this article:" 0000-0002-5286-4375
Joshua H Siegle

MindScope Program, Allen Institute, Seattle, United States

Competing interests
The authors declare that no competing interests exist.
Roger Hurwitz

Independent Researcher, Portland, United States

Competing interests
The authors declare that no competing interests exist.
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.

"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.

Copyright

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.