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METHODS article

Front. Neuroinform.
Volume 18 - 2024 | doi: 10.3389/fninf.2024.1448161
This article is part of the Research Topic Addressing Large Scale Computing Challenges in Neuroscience: Current Advances and Future Directions View all 4 articles

Systems Neuroscience Computing in Python (SyNCoPy): A Python Package for Large-scale Analysis of Electrophysiological Data

Provisionally accepted
Gregor Moenke Gregor Moenke 1*Tim Schaefer Tim Schaefer 1Mohsen Parto-Dezfouli Mohsen Parto-Dezfouli 1Diljit Singh Kajal Diljit Singh Kajal 1Stefan Fuertinger Stefan Fuertinger 1Joscha Tapani Schmiedt Joscha Tapani Schmiedt 2Pascal Fries Pascal Fries 1,3,4
  • 1 Ernst Strüngmann Institute for Neuroscience, Max Planck Society, Frankfurt am Main, Bavaria, Germany
  • 2 Institute of Brain Research, Faculty of Biology and Chemistry, University of Bremen, Bremen, Bremen, Germany
  • 3 Max Planck Institute for Biological Cybernetics, Tübingen, Baden-Württemberg, Germany
  • 4 Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, Netherlands

The final, formatted version of the article will be published soon.

    We introduce an open-source Python package for the analysis of large-scale electrophysiological data, named SyNCoPy, which stands for Systems Neuroscience Computing in Python. The package includes signal processing analyses across time (e.g. time-lock analysis), frequency (e.g. power spectrum), and connectivity (e.g. coherence) domains. It enables user-friendly data analysis on both laptop-based and high performance computing systems. SyNCoPy is designed to facilitate trial-parallel workflows (parallel processing of trials) making it an ideal tool for large-scale analysis of electrophysiological data. Based on parallel processing of trials, the software can support very large-scale datasets via innovative out-of-core computation techniques. It also provides seamless interoperability with other standard software packages through a range of file format importers and exporters and open file formats. The naming of the user functions closely follows the well-established FieldTrip framework, which is an open-source Matlab toolbox for advanced analysis of electrophysiological data.

    Keywords: Neuroscience, Electrophysiology, Software, time-frequency analysis, connectivity, Granger causality, Magnetoencephalography (MEG), Electroencephalography (EEG)

    Received: 12 Jun 2024; Accepted: 05 Nov 2024.

    Copyright: © 2024 Moenke, Schaefer, Parto-Dezfouli, Kajal, Fuertinger, Schmiedt and Fries. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Gregor Moenke, Ernst Strüngmann Institute for Neuroscience, Max Planck Society, Frankfurt am Main, 60528, Bavaria, Germany

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.