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Pynapple: a toolbox for data analysis in neuroscience

Guillaume Viejo, Daniel Levenstein, Sofia Skromne Carrasco, View ORCID ProfileDhruv Mehrotra, Sara Mahallati, Gilberto R Vite, Henry Denny, Lucas Sjulson, Francesco P Battaglia, Adrien Peyrache
doi: https://doi.org/10.1101/2022.12.06.519376
Guillaume Viejo
1Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, Canada
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Daniel Levenstein
1Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, Canada
2MILA – Quebec IA Institute
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Sofia Skromne Carrasco
1Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, Canada
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Dhruv Mehrotra
1Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, Canada
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  • ORCID record for Dhruv Mehrotra
Sara Mahallati
1Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, Canada
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Gilberto R Vite
1Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, Canada
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Henry Denny
1Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, Canada
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Lucas Sjulson
3Departments of Psychiatry and Neuroscience, Albert Einstein College of Medicine, Bronx, NY
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Francesco P Battaglia
4Donders Institute for Brain, Cognition and Behaviour, Radboud University, 6525AJ Nijmegen, The Netherlands
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Adrien Peyrache
1Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, Canada
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  • For correspondence: adrien.peyrache@mcgill.ca
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Abstract

Datasets collected in neuroscientific studies are of ever-growing complexity, often combining high dimensional time series data from multiple data acquisition modalities. Handling and manipulating these various data streams in an adequate programming environment is crucial to ensure reliable analysis, and to facilitate sharing of reproducible analysis pipelines. Here, we present Pynapple, a lightweight python package designed to process a broad range of time-resolved data in systems neuroscience. The core feature of this package is a small number of versatile objects that support the manipulation of any data streams and task parameters. The package includes a set of methods to read common data formats and allows users to easily write their own. The resulting code is easy to read and write, avoids low-level data processing and other error-prone steps, and is fully open source. Libraries for higher-level analyses are developed within the Pynapple framework but are contained within in a collaborative repository of specialized and continuously updated analysis routines. This provides flexibility while ensuring long-term stability of the core package. In conclusion, Pynapple provides a common framework for data analysis in neuroscience.

Highlights

  • An open-source framework for data analysis in systems neuroscience.

  • Easy-to-use object-oriented programming for data manipulation.

  • A lightweight and standalone package ensuring long-term backward compatibility.

Competing Interest Statement

The authors have declared no competing interest.

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-NC 4.0 International license.
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Posted December 07, 2022.
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Pynapple: a toolbox for data analysis in neuroscience
Guillaume Viejo, Daniel Levenstein, Sofia Skromne Carrasco, Dhruv Mehrotra, Sara Mahallati, Gilberto R Vite, Henry Denny, Lucas Sjulson, Francesco P Battaglia, Adrien Peyrache
bioRxiv 2022.12.06.519376; doi: https://doi.org/10.1101/2022.12.06.519376
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Pynapple: a toolbox for data analysis in neuroscience
Guillaume Viejo, Daniel Levenstein, Sofia Skromne Carrasco, Dhruv Mehrotra, Sara Mahallati, Gilberto R Vite, Henry Denny, Lucas Sjulson, Francesco P Battaglia, Adrien Peyrache
bioRxiv 2022.12.06.519376; doi: https://doi.org/10.1101/2022.12.06.519376

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