1411.2832v2.pdf (327.13 kB)
Exploration of synergistic and redundant information sharing in static and dynamical Gaussian systems
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.
Funding
Explaining Consciousness as Neural Dynamical Complexity; G1201; EPSRC-ENGINEERING & PHYSICAL SCIENCES RESEARCH COUNCIL; EP/L005131/1
History
Publication status
- Published
File Version
- Accepted version
Journal
Physical Review EISSN
1539-3755Publisher
American Physical SocietyExternal DOI
Issue
5Volume
91Article number
a052802Department affiliated with
- Informatics Publications
Full text available
- Yes
Peer reviewed?
- Yes
Legacy Posted Date
2016-03-08First Open Access (FOA) Date
2016-03-08First Compliant Deposit (FCD) Date
2016-03-08Usage metrics
Categories
No categories selectedKeywords
Licence
Exports
RefWorks
BibTeX
Ref. manager
Endnote
DataCite
NLM
DC