Observability and synchronization of neuron models
| dc.creator | Luis Antonio Aguirre | |
| dc.creator | Leonardo Luiz Portes dos Santos | |
| dc.creator | Christophe Letellier | |
| dc.date.accessioned | 2025-04-07T14:55:15Z | |
| dc.date.accessioned | 2025-09-08T23:06:44Z | |
| dc.date.available | 2025-04-07T14:55:15Z | |
| dc.date.issued | 2017 | |
| dc.identifier.doi | https://doi.org/10.1063/1.4985291 | |
| dc.identifier.issn | 1054-1500 | |
| dc.identifier.uri | https://hdl.handle.net/1843/81340 | |
| dc.language | eng | |
| dc.publisher | Universidade Federal de Minas Gerais | |
| dc.relation.ispartof | Chaos | |
| dc.rights | Acesso Restrito | |
| dc.subject | Fisiologia | |
| dc.subject | Neurologia DeCS | |
| dc.subject | Modelos matemáticos | |
| dc.subject.other | In most studies involving neuron models, it is common to use the first state variable—the membrane potential—for monitoring or controlling purposes. This choice of variable results from the fact that, in experimental neuron networks, only this variable can be actually measured. However, it is importante to know if such a choice is the most adequate in terms of the dynamical behavior. In practice, the analysis of a variable that conveys poor dynamical information could imply unreliable and wrong results. | |
| dc.subject.other | the variables that convey greater observability were: the membrane potential in the Hodgkin–Huxley and Izhikevich's models (especially during chattering), FitzHugh–Nagumo the observability provided by the potential and recovery variables is comparable. | |
| dc.subject.other | The Hindmarsh–Rose model has some peculiarities in what concerns observability. The membrane potential and fast recovery variable reveal the fast time scales such as the spikes in the chattering regime, whereas the z variable (slow recovery) is the only one to clearly reveal the chaotic nature of the dynamics when it occurs. | |
| dc.title | Observability and synchronization of neuron models | |
| dc.type | Artigo de periódico | |
| local.citation.issue | 10 | |
| local.citation.spage | 103103 | |
| local.citation.volume | 27 | |
| local.description.resumo | 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. | |
| local.publisher.country | Brasil | |
| local.publisher.department | ENG - DEPARTAMENTO DE ENGENHARIA ELETRÔNICA | |
| local.publisher.initials | UFMG | |
| local.url.externa | https://pubs.aip.org/aip/cha/article/27/10/103103/151533 |
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