Observability and synchronization of neuron models

Chaos
Luis A AguirreChristophe Letellier

Abstract

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...Continue Reading

References

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Citations

Mar 2, 2018·Scientific Reports·Christophe LetellierMurilo S Baptista
Dec 25, 2019·Physical Review. E·Kun TianCelso Grebogi
Oct 10, 2020·Computers in Biology and Medicine·Anthony GuzmanLaura Muñoz
Nov 4, 2020·Chaos·Christopher E GonzalezChristophe Letellier
Jan 1, 2022·Proceedings of the National Academy of Sciences of the United States of America·Arthur N MontanariAdilson E Motter

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