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naplib-python: Neural Acoustic Data Processing and Analysis Tools in Python

Recently, the computational neuroscience community has pushed for more transparent and reproducible methods across the field. In the interest of unifying the domain of auditory neuroscience, naplib-python provides an intuitive and general data structure for handling all neural recordings and stimuli...

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Detalles Bibliográficos
Autores principales: Mischler, Gavin, Raghavan, Vinay, Keshishian, Menoua, Mesgarani, Nima
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Cornell University 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10104195/
https://www.ncbi.nlm.nih.gov/pubmed/37064534
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author Mischler, Gavin
Raghavan, Vinay
Keshishian, Menoua
Mesgarani, Nima
author_facet Mischler, Gavin
Raghavan, Vinay
Keshishian, Menoua
Mesgarani, Nima
author_sort Mischler, Gavin
collection PubMed
description Recently, the computational neuroscience community has pushed for more transparent and reproducible methods across the field. In the interest of unifying the domain of auditory neuroscience, naplib-python provides an intuitive and general data structure for handling all neural recordings and stimuli, as well as extensive preprocessing, feature extraction, and analysis tools which operate on that data structure. The package removes many of the complications associated with this domain, such as varying trial durations and multi-modal stimuli, and provides a general-purpose analysis framework that interfaces easily with existing toolboxes used in the field.
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spelling pubmed-101041952023-04-15 naplib-python: Neural Acoustic Data Processing and Analysis Tools in Python Mischler, Gavin Raghavan, Vinay Keshishian, Menoua Mesgarani, Nima ArXiv Article Recently, the computational neuroscience community has pushed for more transparent and reproducible methods across the field. In the interest of unifying the domain of auditory neuroscience, naplib-python provides an intuitive and general data structure for handling all neural recordings and stimuli, as well as extensive preprocessing, feature extraction, and analysis tools which operate on that data structure. The package removes many of the complications associated with this domain, such as varying trial durations and multi-modal stimuli, and provides a general-purpose analysis framework that interfaces easily with existing toolboxes used in the field. Cornell University 2023-04-04 /pmc/articles/PMC10104195/ /pubmed/37064534 Text en https://creativecommons.org/licenses/by-nc-nd/4.0/This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (https://creativecommons.org/licenses/by-nc-nd/4.0/) , which allows reusers to copy and distribute the material in any medium or format in unadapted form only, for noncommercial purposes only, and only so long as attribution is given to the creator.
spellingShingle Article
Mischler, Gavin
Raghavan, Vinay
Keshishian, Menoua
Mesgarani, Nima
naplib-python: Neural Acoustic Data Processing and Analysis Tools in Python
title naplib-python: Neural Acoustic Data Processing and Analysis Tools in Python
title_full naplib-python: Neural Acoustic Data Processing and Analysis Tools in Python
title_fullStr naplib-python: Neural Acoustic Data Processing and Analysis Tools in Python
title_full_unstemmed naplib-python: Neural Acoustic Data Processing and Analysis Tools in Python
title_short naplib-python: Neural Acoustic Data Processing and Analysis Tools in Python
title_sort naplib-python: neural acoustic data processing and analysis tools in python
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10104195/
https://www.ncbi.nlm.nih.gov/pubmed/37064534
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