The EBRAINS NeuroFeatureExtract: An Online Resource for the Extraction of Neural Activity Features From Electrophysiological Data

The description of neural dynamics, in terms of precise characterizations of action potential timings and shape and voltage related measures, is fundamental for a deeper understanding of the neural code and its information content. Not only such measures serve the scientific questions posed by exper...

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Bibliographic Details
Published inFrontiers in neuroinformatics Vol. 15; p. 713899
Main Authors Bologna, Luca L., Smiriglia, Roberto, Curreri, Dario, Migliore, Michele
Format Journal Article
LanguageEnglish
Published Lausanne Frontiers Research Foundation 26.08.2021
Frontiers Media S.A
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Summary:The description of neural dynamics, in terms of precise characterizations of action potential timings and shape and voltage related measures, is fundamental for a deeper understanding of the neural code and its information content. Not only such measures serve the scientific questions posed by experimentalists but are increasingly being used by computational neuroscientists for the construction of biophysically detailed data-driven models. Nonetheless, online resources enabling users to perform such feature extraction operation are lacking. To address this problem, in the framework of the Human Brain Project and the EBRAINS research infrastructure, we have developed and made available to the scientific community the NeuroFeatureExtract, an open-access online resource for the extraction of electrophysiological features from neural activity data. This tool allows to select electrophysiological traces of interest, fetched from public repositories or from users’ own data, and provides ad hoc functionalities to extract relevant features. The output files are properly formatted for further analysis, including data-driven neural model optimization.
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Edited by: Sharon Crook, Arizona State University, United States
Reviewed by: Tuomo Mäki-Marttunen, Simula Research Laboratory, Norway; Ankur Sinha, University College London, United Kingdom
ISSN:1662-5196
1662-5196
DOI:10.3389/fninf.2021.713899