Observability and synchronization of neuron models - Normandie Université Access content directly
Journal Articles Chaos: An Interdisciplinary Journal of Nonlinear Science Year : 2017

Observability and synchronization of neuron models

Luis A Aguirre
  • Function : Author
Leonardo L. Portes
  • Function : Author


Observability is the property that enables recovering the state of a dynamical system from a reduced number of measured variables. In high-dimensional systems, it is therefore important to make sure that the variable recorded to perform the analysis conveys good observability of the system dynamics. The observability of a network of neuron models depends nontrivially on the observability of the node dynamics and on the topology of the network. The aim of this paper is twofold. First, to perform a study of observability using four well-known neuron models by computing three different observability coefficients. This not only clarifies observability properties of the models but also shows the limitations of applicability of each type of coefficients in the context of such models. Second, to study the emergence of phase synchronization in networks composed of neuron models. This is done performing multivariate singular spectrum analysis which, to the best of the authors' knowledge, has not been used in the context of networks of neuron models. It is shown that it is possible to detect phase synchronization: (i) without having to measure all the state variables, but only one (that provides greatest observability) from each node and (ii) without having to estimate the phase. Published by AIP Publishing. https://doi.
Fichier principal
Vignette du fichier
AguirreChaos2017.pdf (2.33 Mo) Télécharger le fichier
Origin : Publisher files allowed on an open archive

Dates and versions

hal-01672077 , version 1 (23-12-2017)



Luis A Aguirre, Leonardo L. Portes, Christophe Letellier. Observability and synchronization of neuron models. Chaos: An Interdisciplinary Journal of Nonlinear Science, 2017, 27 (10), ⟨10.1063/1.4985291⟩. ⟨hal-01672077⟩
61 View
198 Download



Gmail Facebook X LinkedIn More