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On the Nature of Neural Causality in Large-Scale Brain Networks: Foundations, Modeling and Nonlinear Neurodynamics

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Date Issued:
2018
Abstract/Description:
We examine the nature of causality as it exists within large-scale brain networks by first providing a rigorous conceptual analysis of probabilistic causality as distinct from deterministic causality. We then use information-theoretic methods, including the linear autoregressive modeling technique of Wiener-Granger causality (WGC), and Shannonian transfer entropy (TE), to explore and recover causal relations between two neural masses. Time series data were generated by Stefanescu-Jirsa 3D model of two coupled network nodes in The Virtual Brain (TVB), a novel neuroinformatics platform used to model resting state large-scale networks with neural mass models. We then extended this analysis to three nodes to investigate the equivalence of a concept in probabilistic causality known as ‘screening off’ with a method of statistical ablation known as conditional Granger causality. Finally, we review some of the empirical and theoretical work of nonlinear neurodynamics of Walter Freeman, as well as metastable coordination dynamics and investigate what impact they have had on consciousness research.
Title: On the Nature of Neural Causality in Large-Scale Brain Networks: Foundations, Modeling and Nonlinear Neurodynamics.
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Name(s): Mannino, Michael, author
Bressler, Steven L., Thesis advisor
Florida Atlantic University, Degree grantor
Charles E. Schmidt College of Science
Center for Complex Systems and Brain Sciences
Type of Resource: text
Genre: Electronic Thesis Or Dissertation
Date Created: 2018
Date Issued: 2018
Publisher: Florida Atlantic University
Place of Publication: Boca Raton, Fla.
Physical Form: application/pdf
Extent: 255 p.
Language(s): English
Abstract/Description: We examine the nature of causality as it exists within large-scale brain networks by first providing a rigorous conceptual analysis of probabilistic causality as distinct from deterministic causality. We then use information-theoretic methods, including the linear autoregressive modeling technique of Wiener-Granger causality (WGC), and Shannonian transfer entropy (TE), to explore and recover causal relations between two neural masses. Time series data were generated by Stefanescu-Jirsa 3D model of two coupled network nodes in The Virtual Brain (TVB), a novel neuroinformatics platform used to model resting state large-scale networks with neural mass models. We then extended this analysis to three nodes to investigate the equivalence of a concept in probabilistic causality known as ‘screening off’ with a method of statistical ablation known as conditional Granger causality. Finally, we review some of the empirical and theoretical work of nonlinear neurodynamics of Walter Freeman, as well as metastable coordination dynamics and investigate what impact they have had on consciousness research.
Identifier: FA00013164 (IID)
Degree granted: Dissertation (Ph.D.)--Florida Atlantic University, 2018.
Collection: FAU Electronic Theses and Dissertations Collection
Note(s): Includes bibliography.
Subject(s): Neuroinformatics
Consciousness--Research
Computational neuroscience
Held by: Florida Atlantic University Libraries
Sublocation: Digital Library
Persistent Link to This Record: http://purl.flvc.org/fau/fd/FA00013164
Use and Reproduction: Copyright © is held by the author with permission granted to Florida Atlantic University to digitize, archive and distribute this item for non-profit research and educational purposes. Any reuse of this item in excess of fair use or other copyright exemptions requires permission of the copyright holder.
Use and Reproduction: http://rightsstatements.org/vocab/InC/1.0/
Host Institution: FAU
Is Part of Series: Florida Atlantic University Digital Library Collections.