2017/05/29 by Luis A. Aguirre, Leonardo L. Portes, Christophe Letellier
Biochemistry, Genetics and Molecular Biology · Neuroscience · Physics and Astronomy · #Context (archaeology) #Control theory (sociology) #Functional Brain Connectivity Studies #Measure (data warehouse) #Neural dynamics and brain function #Node (physics) #Observability #State (computer science) #Synchronization (alternating current) #Topology (electrical circuits) #nlin.CD #q-bio.NC #stochastic dynamics and bifurcation
paper · pdf · doi:10.1063/1.4985291
published as Chaos, 65:83--99, 2017
arxiv created 2017/05/29 · openalex created_date 2017/06/05 · openalex publication_date 2017/10/01 · arxiv updated 2019/05/06 · openalex updated_date 2026/08/05
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.