Academic Journal
eFEL: electrophysiology feature extraction library.
| Title: | eFEL: electrophysiology feature extraction library. |
|---|---|
| Authors: | Mandge, Darshan, Tuncel, Anıl, Jaquier, Aurélien, Kilic, Ilkan, Damart, Tanguy, Markram, Henry, Geit, Werner Van, Ranjan, Rajnish |
| Source: | Bioinformatics; Jun2026, Vol. 42 Issue 6, p1-11, 11p |
| Subject Terms: | Electrophysiology, Feature extraction, Open source software, Software libraries (Computer programming), Data analysis, Reproducible research, Computational neuroscience, Neurosciences |
| 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. [ABSTRACT FROM AUTHOR] |
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| Database: | Complementary Index |
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| Header | DbId: edb DbLabel: Complementary Index An: 195099912 RelevancyScore: 1082 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 1082.4189453125 |
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| Items | – Name: Title Label: Title Group: Ti Data: eFEL: electrophysiology feature extraction library. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Mandge%2C+Darshan%22">Mandge, Darshan</searchLink><br /><searchLink fieldCode="AR" term="%22Tuncel%2C+Anıl%22">Tuncel, Anıl</searchLink><br /><searchLink fieldCode="AR" term="%22Jaquier%2C+Aurélien%22">Jaquier, Aurélien</searchLink><br /><searchLink fieldCode="AR" term="%22Kilic%2C+Ilkan%22">Kilic, Ilkan</searchLink><br /><searchLink fieldCode="AR" term="%22Damart%2C+Tanguy%22">Damart, Tanguy</searchLink><br /><searchLink fieldCode="AR" term="%22Markram%2C+Henry%22">Markram, Henry</searchLink><br /><searchLink fieldCode="AR" term="%22Geit%2C+Werner+Van%22">Geit, Werner Van</searchLink><br /><searchLink fieldCode="AR" term="%22Ranjan%2C+Rajnish%22">Ranjan, Rajnish</searchLink> – Name: TitleSource Label: Source Group: Src Data: Bioinformatics; Jun2026, Vol. 42 Issue 6, p1-11, 11p – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Electrophysiology%22">Electrophysiology</searchLink><br /><searchLink fieldCode="DE" term="%22Feature+extraction%22">Feature extraction</searchLink><br /><searchLink fieldCode="DE" term="%22Open+source+software%22">Open source software</searchLink><br /><searchLink fieldCode="DE" term="%22Software+libraries+%28Computer+programming%29%22">Software libraries (Computer programming)</searchLink><br /><searchLink fieldCode="DE" term="%22Data+analysis%22">Data analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Reproducible+research%22">Reproducible research</searchLink><br /><searchLink fieldCode="DE" term="%22Computational+neuroscience%22">Computational neuroscience</searchLink><br /><searchLink fieldCode="DE" term="%22Neurosciences%22">Neurosciences</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: 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. [ABSTRACT FROM AUTHOR] – Name: Abstract Label: Group: Ab Data: <i>Copyright of Bioinformatics is the property of Oxford University Press / USA and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1093/bioinformatics/btag328 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 11 StartPage: 1 Subjects: – SubjectFull: Electrophysiology Type: general – SubjectFull: Feature extraction Type: general – SubjectFull: Open source software Type: general – SubjectFull: Software libraries (Computer programming) Type: general – SubjectFull: Data analysis Type: general – SubjectFull: Reproducible research Type: general – SubjectFull: Computational neuroscience Type: general – SubjectFull: Neurosciences Type: general Titles: – TitleFull: eFEL: electrophysiology feature extraction library. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Mandge, Darshan – PersonEntity: Name: NameFull: Tuncel, Anıl – PersonEntity: Name: NameFull: Jaquier, Aurélien – PersonEntity: Name: NameFull: Kilic, Ilkan – PersonEntity: Name: NameFull: Damart, Tanguy – PersonEntity: Name: NameFull: Markram, Henry – PersonEntity: Name: NameFull: Geit, Werner Van – PersonEntity: Name: NameFull: Ranjan, Rajnish IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 06 Text: Jun2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 13674803 Numbering: – Type: volume Value: 42 – Type: issue Value: 6 Titles: – TitleFull: Bioinformatics Type: main |
| ResultId | 1 |