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PyPhi: A toolbox for integrated information theory

Integrated information theory provides a mathematical framework to fully characterize the cause-effect structure of a physical system. Here, we introduce PyPhi, a Python software package that implements this framework for causal analysis and unfolds the full cause-effect structure of discrete dynami...

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Autores principales: Mayner, William G. P., Marshall, William, Albantakis, Larissa, Findlay, Graham, Marchman, Robert, Tononi, Giulio
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6080800/
https://www.ncbi.nlm.nih.gov/pubmed/30048445
http://dx.doi.org/10.1371/journal.pcbi.1006343
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author Mayner, William G. P.
Marshall, William
Albantakis, Larissa
Findlay, Graham
Marchman, Robert
Tononi, Giulio
author_facet Mayner, William G. P.
Marshall, William
Albantakis, Larissa
Findlay, Graham
Marchman, Robert
Tononi, Giulio
author_sort Mayner, William G. P.
collection PubMed
description Integrated information theory provides a mathematical framework to fully characterize the cause-effect structure of a physical system. Here, we introduce PyPhi, a Python software package that implements this framework for causal analysis and unfolds the full cause-effect structure of discrete dynamical systems of binary elements. The software allows users to easily study these structures, serves as an up-to-date reference implementation of the formalisms of integrated information theory, and has been applied in research on complexity, emergence, and certain biological questions. We first provide an overview of the main algorithm and demonstrate PyPhi’s functionality in the course of analyzing an example system, and then describe details of the algorithm’s design and implementation. PyPhi can be installed with Python’s package manager via the command ‘pip install pyphi’ on Linux and macOS systems equipped with Python 3.4 or higher. PyPhi is open-source and licensed under the GPLv3; the source code is hosted on GitHub at https://github.com/wmayner/pyphi. Comprehensive and continually-updated documentation is available at https://pyphi.readthedocs.io. The pyphi-users mailing list can be joined at https://groups.google.com/forum/#!forum/pyphi-users. A web-based graphical interface to the software is available at http://integratedinformationtheory.org/calculate.html.
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spelling pubmed-60808002018-08-16 PyPhi: A toolbox for integrated information theory Mayner, William G. P. Marshall, William Albantakis, Larissa Findlay, Graham Marchman, Robert Tononi, Giulio PLoS Comput Biol Research Article Integrated information theory provides a mathematical framework to fully characterize the cause-effect structure of a physical system. Here, we introduce PyPhi, a Python software package that implements this framework for causal analysis and unfolds the full cause-effect structure of discrete dynamical systems of binary elements. The software allows users to easily study these structures, serves as an up-to-date reference implementation of the formalisms of integrated information theory, and has been applied in research on complexity, emergence, and certain biological questions. We first provide an overview of the main algorithm and demonstrate PyPhi’s functionality in the course of analyzing an example system, and then describe details of the algorithm’s design and implementation. PyPhi can be installed with Python’s package manager via the command ‘pip install pyphi’ on Linux and macOS systems equipped with Python 3.4 or higher. PyPhi is open-source and licensed under the GPLv3; the source code is hosted on GitHub at https://github.com/wmayner/pyphi. Comprehensive and continually-updated documentation is available at https://pyphi.readthedocs.io. The pyphi-users mailing list can be joined at https://groups.google.com/forum/#!forum/pyphi-users. A web-based graphical interface to the software is available at http://integratedinformationtheory.org/calculate.html. Public Library of Science 2018-07-26 /pmc/articles/PMC6080800/ /pubmed/30048445 http://dx.doi.org/10.1371/journal.pcbi.1006343 Text en © 2018 Mayner et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Mayner, William G. P.
Marshall, William
Albantakis, Larissa
Findlay, Graham
Marchman, Robert
Tononi, Giulio
PyPhi: A toolbox for integrated information theory
title PyPhi: A toolbox for integrated information theory
title_full PyPhi: A toolbox for integrated information theory
title_fullStr PyPhi: A toolbox for integrated information theory
title_full_unstemmed PyPhi: A toolbox for integrated information theory
title_short PyPhi: A toolbox for integrated information theory
title_sort pyphi: a toolbox for integrated information theory
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6080800/
https://www.ncbi.nlm.nih.gov/pubmed/30048445
http://dx.doi.org/10.1371/journal.pcbi.1006343
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