A Robust Estimation of Information Flow in Coupled Nonlinear Systems

Shivkumar Sabesan (), Konstantinos Tsakalis (), Andreas Spanias () and Leon Iasemidis ()
Additional contact information
Shivkumar Sabesan: Arizona State University
Konstantinos Tsakalis: Arizona State University
Andreas Spanias: Arizona State University
Leon Iasemidis: Arizona State University

Chapter Chapter 15 in Computational Neuroscience, 2010, pp 271-283 from Springer

Abstract: Abstract Transfer entropy (TE) is a recently proposed measure of the information flow between coupled linear or nonlinear systems. In this study, we first suggest improvements in the selection of parameters for the estimation of TE that significantly enhance its accuracy and robustness in identifying the direction and the level of information flow between observed data series generated by coupled complex systems. Second, a new measure, the net transfer of entropy (NTE), is defined based on TE. Third, we employ surrogate analysis to show the statistical significance of the measures. Fourth, the effect of measurement noise on the measures’ performance is investigated up to $$S/N = 3$$ dB. We demonstrate the usefulness of the improved method by analyzing data series from coupled nonlinear chaotic oscillators. Our findings suggest that TE and NTE may play a critical role in elucidating the functional connectivity of complex networks of nonlinear systems.

Keywords: Autocorrelation Function; Information Flow; Surrogate Data; Transfer Entropy; Drive Oscillator (search for similar items in EconPapers)
Date: 2010
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:spochp:978-0-387-88630-5_15

Ordering information: This item can be ordered from
http://www.springer.com/9780387886305

DOI: 10.1007/978-0-387-88630-5_15

Access Statistics for this chapter

More chapters in Springer Optimization and Its Applications from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().