Inferring the connectivity of coupled oscillators from time-series statistical similarity analysis

Tirabassi, Giulio and Sevilla-Escoboza, Ricardo and Martín Buldú, Javier and Masoller, Cristina (2015). Inferring the connectivity of coupled oscillators from time-series statistical similarity analysis. "Scientific Reports", v. 5 (n. 10829); pp. 1-14. ISSN 2045-2322. https://doi.org/10.1038/srep10829.

Description

Title: Inferring the connectivity of coupled oscillators from time-series statistical similarity analysis
Author/s:
  • Tirabassi, Giulio
  • Sevilla-Escoboza, Ricardo
  • Martín Buldú, Javier
  • Masoller, Cristina
Item Type: Article
Título de Revista/Publicación: Scientific Reports
Date: 4 June 2015
ISSN: 2045-2322
Volume: 5
Subjects:
Faculty: Centro de Tecnología Biomédica (CTB) (UPM)
Department: Otro
Creative Commons Licenses: Recognition - No derivative works - Non commercial

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Abstract

A system composed by interacting dynamical elements can be represented by a network, where the nodes represent the elements that constitute the system, and the links account for their interactions, which arise due to a variety of mechanisms, and which are often unknown. A popular method for inferring the system connectivity (i.e., the set of links among pairs of nodes) is by performing a statistical similarity analysis of the time-series collected from the dynamics of the nodes. Here, by considering two systems of coupled oscillators (Kuramoto phase oscillators and Rössler chaotic electronic oscillators) with known and controllable coupling conditions, we aim at testing the performance of this inference method, by using linear and non linear statistical similarity measures. We find that, under adequate conditions, the network links can be perfectly inferred, i.e., no mistakes are made regarding the presence or absence of links. These conditions for perfect inference require: i) an appropriated choice of the observed variable to be analysed, ii) an appropriated interaction strength, and iii) an adequate thresholding of the similarity matrix. For the dynamical units considered here we find that the linear statistical similarity measure performs, in general, better than the non-linear ones.

Funding Projects

TypeCodeAcronymLeaderTitle
FP7289447LINCUnspecifiedLearning about Interacting Networks in Climate
Government of SpainFIS2012-37655-C02-01UnspecifiedUnspecifiedUnspecified
Government of SpainFIS2013-41057UnspecifiedUnspecifiedUnspecified

More information

Item ID: 41079
DC Identifier: http://oa.upm.es/41079/
OAI Identifier: oai:oa.upm.es:41079
DOI: 10.1038/srep10829
Official URL: http://www.nature.com/articles/srep10829
Deposited by: Memoria Investigacion
Deposited on: 22 Apr 2017 10:22
Last Modified: 06 Jun 2019 14:13
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