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

Moenke, G; Schaefer, T; Parto-Dezfouli, M; Kajal, DS; Fuertinger, S; Schmiedt, JT; Fries, P

doi:10.1101/2024.04.15.589590

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

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Fries, P
Department of Computational Neuroscience, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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https://www.biorxiv.org/content/10.1101/2024.04.15.589590v1.full.pdf
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引用

Moenke, G., Schaefer, T., Parto-Dezfouli, M., Kajal, D., Fuertinger, S., Schmiedt, J., & Fries, P. (submitted). Systems Neuroscience Computing in Python (SyNCoPy): A Python Package for Large-scale Analysis of Electrophysiological Data.

引用: https://hdl.handle.net/21.11116/0000-000F-2F89-3

要旨

We introduce an open-source Python package for the analysis of large-scale electrophysiological data called SyNCoPy, 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.