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

Tirabassi, Giulio; Sevilla-Escoboza, Ricardo; Martín Buldú, Javier y 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.

Descripción

Título: Inferring the connectivity of coupled oscillators from time-series statistical similarity analysis
Autor/es:
  • Tirabassi, Giulio
  • Sevilla-Escoboza, Ricardo
  • Martín Buldú, Javier
  • Masoller, Cristina
Tipo de Documento: Artículo
Título de Revista/Publicación: Scientific Reports
Fecha: 4 Junio 2015
Volumen: 5
Materias:
Escuela: Centro de Tecnología Biomédica (CTB) (UPM)
Departamento: Otro
Licencias Creative Commons: Reconocimiento - Sin obra derivada - No comercial

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Resumen

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.

Proyectos asociados

TipoCódigoAcrónimoResponsableTítulo
FP7FP7-PEOPLE-2011-ITNLINC projectSin especificarSin especificar
Gobierno de EspañaFIS2012-37655-C02-01Sin especificarSin especificarSin especificar
Gobierno de Españaproject FIS2013-41057MINECOSin especificarSin especificar

Más información

ID de Registro: 41079
Identificador DC: http://oa.upm.es/41079/
Identificador OAI: oai:oa.upm.es:41079
Identificador DOI: 10.1038/srep10829
URL Oficial: http://www.nature.com/articles/srep10829
Depositado por: Memoria Investigacion
Depositado el: 22 Abr 2017 10:22
Ultima Modificación: 22 Abr 2017 10:22
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