Exploration of synergistic and redundant information sharing in static and dynamical Gaussian systems

Barrett, Adam B (2015) Exploration of synergistic and redundant information sharing in static and dynamical Gaussian systems. Physical Review E, 91 (5). a052802. ISSN 1539-3755

[img] PDF - Accepted Version
Download (334kB)
[img] PDF (For REF only) - Published Version
Restricted to SRO admin only

Download (298kB)


To fully characterize the information that two source variables carry about a third target variable, one must decompose the total information into redundant, unique, and synergistic components, i.e., obtain a partial information decomposition (PID). However, Shannon's theory of information does not provide formulas to fully determine these quantities. Several recent studies have begun addressing this. Some possible definitions for PID quantities have been proposed and some analyses have been carried out on systems composed of discrete variables. Here we present an in-depth analysis of PIDs on Gaussian systems, both static and dynamical. We show that, for a broad class of Gaussian systems, previously proposed PID formulas imply that (i) redundancy reduces to the minimum information provided by either source variable and hence is independent of correlation between sources, and (ii) synergy is the extra information contributed by the weaker source when the stronger source is known and can either increase or decrease with correlation between sources. We find that Gaussian systems frequently exhibit net synergy, i.e., the information carried jointly by both sources is greater than the sum of information carried by each source individually. Drawing from several explicit examples, we discuss the implications of these findings for measures of information transfer and information-based measures of complexity, both generally and within a neuroscience setting. Importantly, by providing independent formulas for synergy and redundancy applicable to continuous time-series data, we provide an approach to characterizing and quantifying information sharing amongst complex system variables.

Item Type: Article
Schools and Departments: School of Engineering and Informatics > Informatics
Depositing User: Adam Barrett
Date Deposited: 08 Mar 2016 15:19
Last Modified: 08 Mar 2021 16:00
URI: http://sro.sussex.ac.uk/id/eprint/59952

View download statistics for this item

📧 Request an update
Project NameSussex Project NumberFunderFunder Ref
Explaining Consciousness as Neural Dynamical ComplexityG1201EPSRC-ENGINEERING & PHYSICAL SCIENCES RESEARCH COUNCILEP/L005131/1