Synchronization domains in two coupled neural networks

Budzinski, Roberto C.; Boaretto, B. R. R.; Prado, Thiago de Lima; Lopes, S. R.

doi:10.1016/j.cnsns.2019.03.028

Cited by 22 publications

(13 citation statements)

References 55 publications

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“…It is important to note that the mean field consists of more easily experimentally accessible data than the individual signal. As observed in [12,30,67], the amplitude of the mean field of the network is related to the phase synchronization. Here, the scale-free (panels (a), (b), and (c)) and small-world networks (panels (d), (e), and (f)) are considered.…”

Section: Resultsmentioning

confidence: 70%

Investigation of Details in the Transition to Synchronization in Complex Networks by Using Recurrence Analysis

Budzinski

Boaretto

Prado

et al. 2019

MCA

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The study of synchronization in complex networks is useful for understanding a variety of systems, including neural systems. However, the properties of the transition to synchronization are still not well known. In this work, we analyze the details of the transition to synchronization in complex networks composed of bursting oscillators under small-world and scale-free topologies using recurrence quantification analysis, specifically the determinism. We demonstrate the existence of non-stationarity states in the transition region. In the small-world network, the transition region denounces the existence of two-state intermittency.

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Section: Resultsmentioning

confidence: 70%

Investigation of Details in the Transition to Synchronization in Complex Networks by Using Recurrence Analysis

Budzinski

Boaretto

Prado

et al. 2019

MCA

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“…It was shown that the scale-free network displays a non-monotonic evolution of the phase synchronization as the coupling between neurons increases. A similar scenario has been observed in small-world networks, which is called "anomalous phase synchronization" [30,31,55], since the traditional behavior should monotonically transition to PS [33]. Especially, Parkinson's disease and some episodes of seizure behavior generated by epilepsy may be associated to anomalous synchronization.…”

Section: Discussionmentioning

confidence: 53%

“…In this paper, we model a neural network composed of N = 5000 Hodgkin-Huxley-type neurons to study the synchronization phenomena. A similar approach have been used to analyze small-world neural networks [31,33,55]. However, the influence of topology plays an important role in the synchronization characteristics [22,35,38].…”

Section: Discussionmentioning

confidence: 99%

Suppression of Phase Synchronization in Scale-Free Neural Networks Using External Pulsed Current Protocols

Boaretto

Budzinski

Prado

et al. 2019

MCA

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The synchronization of neurons is fundamental for the functioning of the brain since its lack or excess may be related to neurological disorders, such as autism, Parkinson’s and neuropathies such as epilepsy. In this way, the study of synchronization, as well as its suppression in coupled neurons systems, consists of an important multidisciplinary research field where there are still questions to be answered. Here, through mathematical modeling and numerical approach, we simulated a neural network composed of 5000 bursting neurons in a scale-free connection scheme where non-trivial synchronization phenomenon is observed. We proposed two different protocols to the suppression of phase synchronization, which is related to deep brain stimulation and delayed feedback control. Through an optimization process, it is possible to suppression the abnormal synchronization in the neural network.

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Section: Resultsmentioning

confidence: 70%

“…The "periodic" behavior of the mean field time series makes possible the use of RQA to evaluate the synchronization characteristics of the networks [9,26,67]. As depicted in Figure 3, the mean value of the determinism ( Δ ) is able to distinguish the level of phase synchronization.…”

Section: Resultsmentioning

confidence: 99%

Dynamics Days Latin America and the Caribbean 2018

Martı́¹,

Rubido²

2019

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show how important it is to adequately choose the sampling rates in population dynamics observations. This is particularly relevant in order to avoid undermining the reliable estimation of ecological interaction. In particular, they show that by choosing slow acquisition rates, the collected data can produce deceptive patterns, such as the prey becoming the predator. An application of how stochastic processes, agent-based modeling, and symmetries can be used to the analysis of epidemic spreading is presented by Gilberto M. Nakamura et al. (https://www. mdpi.com/2297-8747/24/2/44) in the ninth article of this book. The authors present an algorithm that explores permutation symmetries to enhance the computational performance of agent-based epidemic models and show the statistical properties of propagation. The book finishes with the work by María S. Torre et al. (https://www.mdpi.com/2297-8747/ 24/3/68), who propose an ecological model describing the temporal evolution of the infection of mice due to hantavirus and, therefore, with clear implications to public health. We would like to acknowledge the help of Luisa Parodi and Sociedad Uruguaya de Física, who provide us with the photograph of the cover.

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Synchronization domains in two coupled neural networks

Cited by 22 publications

References 55 publications

Investigation of Details in the Transition to Synchronization in Complex Networks by Using Recurrence Analysis

Investigation of Details in the Transition to Synchronization in Complex Networks by Using Recurrence Analysis

Suppression of Phase Synchronization in Scale-Free Neural Networks Using External Pulsed Current Protocols

Dynamics Days Latin America and the Caribbean 2018

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