eFEL: electrophysiology feature extraction library
- 1Blue Brain Project, École Polytechnique Fédérale de Lausanne (EPFL), Campus Biotech , Geneva 1202,
- 2Laboratory of Neural Microcircuitry, Brain Mind Institute, École Polytechnique Fédérale de Lausanne (EPFL) , Lausanne 1015,
Abstract
MOTIVATION: Electrophysiological recordings are essential in experimental and computational neuroscience, providing insights into neuronal excitability and network behaviour. Extracting features such as action potential thresholds, widths, and firing patterns is conceptually straightforward, but in practice it is complicated by heterogeneous datasets and software environments, which hinder reproducibility and interoperability. A standardized, efficient, and portable framework is needed to ensure consistent analysis across platforms and alignment with community data standards. RESULTS: We present the Electrophysiology Feature Extraction Library (eFEL), a cross-platform, open-source library that implements standardized definitions for over 90 electrophysiological features. eFEL combines a high-performance C++ core with a Python interface, supporting customizable feature dependencies, caching, and parallelization. It integrates with community standards such as Neurodata Without Borders and works seamlessly with common electrophysiology formats and simulation environments. Since its initial release in 2015, eFEL has been used in published studies spanning single-cell analysis, model optimization, multimodal fitting, and circuit simulations. eFEL provides a FAIR-compliant, versatile resource for reproducible electrophysiological data analysis. AVAILABILITY AND IMPLEMENTATION: The eFEL library is publicly available at https://github.com/openbraininstitute/eFEL and the associated study data and scripts have been deposited in Zenodo at https://zenodo.org/records/17241835.
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BibTeX
@article{mandge2026efel,
title = {{eFEL: electrophysiology feature extraction library}},
author = {Darshan Mandge and Mustafa Anıl Tuncel and Aurélien Jaquier and İlkan Kılıç and Tanguy Damart and Henry Markram and Werner A. H. Van Geit and Rajnish Ranjan},
journal = {Bioinformatics},
year = {2026},
month = may,
doi = {10.1093/bioinformatics/btag328},
url = {https://doi.org/10.1093/bioinformatics/btag328},
note = {CC BY},
}
RIS
TY - JOUR
TI - eFEL: electrophysiology feature extraction library
AU - Darshan Mandge
AU - Mustafa Anıl Tuncel
AU - Aurélien Jaquier
AU - İlkan Kılıç
AU - Tanguy Damart
AU - Henry Markram
AU - Werner A. H. Van Geit
AU - Rajnish Ranjan
PY - 2026
DA - 2026/05/20
JO - Bioinformatics
DO - 10.1093/bioinformatics/btag328
UR - https://doi.org/10.1093/bioinformatics/btag328
AB - MOTIVATION: Electrophysiological recordings are essential in experimental and computational neuroscience, providing insights into neuronal excitability and network behaviour. Extracting features such as action potential thresholds, widths, and firing patterns is conceptually straightforward, but in practice it is complicated by heterogeneous datasets and software environments, which hinder reproducibility and interoperability. A standardized, efficient, and portable framework is needed to ensure consistent analysis across platforms and alignment with community data standards. RESULTS: We present the Electrophysiology Feature Extraction Library (eFEL), a cross-platform, open-source library that implements standardized definitions for over 90 electrophysiological features. eFEL combines a high-performance C++ core with a Python interface, supporting customizable feature dependencies, caching, and parallelization. It integrates with community standards such as Neurodata Without Borders and works seamlessly with common electrophysiology formats and simulation environments. Since its initial release in 2015, eFEL has been used in published studies spanning single-cell analysis, model optimization, multimodal fitting, and circuit simulations. eFEL provides a FAIR-compliant, versatile resource for reproducible electrophysiological data analysis. AVAILABILITY AND IMPLEMENTATION: The eFEL library is publicly available at https://github.com/openbraininstitute/eFEL and the associated study data and scripts have been deposited in Zenodo at https://zenodo.org/records/17241835.
SN - 1367-4803
SN - 1367-4811
ER -