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A Tutorial for Information Theory in Neuroscience

Understanding how neural systems integrate, encode, and compute information is central to understanding brain function. Frequently, data from neuroscience experiments are multivariate, the interactions between the variables are nonlinear, and the landscape of hypothesized or possible interactions be...

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Detalles Bibliográficos
Autores principales: Timme, Nicholas M., Lapish, Christopher
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Society for Neuroscience 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6131830/
https://www.ncbi.nlm.nih.gov/pubmed/30211307
http://dx.doi.org/10.1523/ENEURO.0052-18.2018
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author Timme, Nicholas M.
Lapish, Christopher
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Lapish, Christopher
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description Understanding how neural systems integrate, encode, and compute information is central to understanding brain function. Frequently, data from neuroscience experiments are multivariate, the interactions between the variables are nonlinear, and the landscape of hypothesized or possible interactions between variables is extremely broad. Information theory is well suited to address these types of data, as it possesses multivariate analysis tools, it can be applied to many different types of data, it can capture nonlinear interactions, and it does not require assumptions about the structure of the underlying data (i.e., it is model independent). In this article, we walk through the mathematics of information theory along with common logistical problems associated with data type, data binning, data quantity requirements, bias, and significance testing. Next, we analyze models inspired by canonical neuroscience experiments to improve understanding and demonstrate the strengths of information theory analyses. To facilitate the use of information theory analyses, and an understanding of how these analyses are implemented, we also provide a free MATLAB software package that can be applied to a wide range of data from neuroscience experiments, as well as from other fields of study.
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spelling pubmed-61318302018-09-12 A Tutorial for Information Theory in Neuroscience Timme, Nicholas M. Lapish, Christopher eNeuro Reviews Understanding how neural systems integrate, encode, and compute information is central to understanding brain function. Frequently, data from neuroscience experiments are multivariate, the interactions between the variables are nonlinear, and the landscape of hypothesized or possible interactions between variables is extremely broad. Information theory is well suited to address these types of data, as it possesses multivariate analysis tools, it can be applied to many different types of data, it can capture nonlinear interactions, and it does not require assumptions about the structure of the underlying data (i.e., it is model independent). In this article, we walk through the mathematics of information theory along with common logistical problems associated with data type, data binning, data quantity requirements, bias, and significance testing. Next, we analyze models inspired by canonical neuroscience experiments to improve understanding and demonstrate the strengths of information theory analyses. To facilitate the use of information theory analyses, and an understanding of how these analyses are implemented, we also provide a free MATLAB software package that can be applied to a wide range of data from neuroscience experiments, as well as from other fields of study. Society for Neuroscience 2018-09-11 /pmc/articles/PMC6131830/ /pubmed/30211307 http://dx.doi.org/10.1523/ENEURO.0052-18.2018 Text en Copyright © 2018 Timme and Lapish http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International license (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution and reproduction in any medium provided that the original work is properly attributed.
spellingShingle Reviews
Timme, Nicholas M.
Lapish, Christopher
A Tutorial for Information Theory in Neuroscience
title A Tutorial for Information Theory in Neuroscience
title_full A Tutorial for Information Theory in Neuroscience
title_fullStr A Tutorial for Information Theory in Neuroscience
title_full_unstemmed A Tutorial for Information Theory in Neuroscience
title_short A Tutorial for Information Theory in Neuroscience
title_sort tutorial for information theory in neuroscience
topic Reviews
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6131830/
https://www.ncbi.nlm.nih.gov/pubmed/30211307
http://dx.doi.org/10.1523/ENEURO.0052-18.2018
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