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.
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/
- Matthias H Hennig
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
- Laura L Colgin, University of Texas at Austin, United States
© 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.