A network of networks model to study phase synchronization using structural connection matrix of human brain

Ferrari, Fernanda; Viana, Ricardo L.; Reis, Adriane S.; Iarosz, Kelly C.; Caldas, Iberê L.; Batista, Antônio M.

doi:10.1016/j.physa.2017.12.129

Cited by 22 publications

(4 citation statements)

References 42 publications

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“…In the internal connection scheme, each network node can be understood as a neuron and their connections as the edges [8], which are able to simulate a single network, as used in many works [9][10][11][12][13]. On the other hand, considering the inter-networks connection scheme, it is possible to consider a neural system composed of different sub-areas, so each sub-network can be understood as a node and their connections as the edges, building a network of networks [14][15][16][17][18].…”

Section: Introductionmentioning

confidence: 99%

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: Introductionmentioning

confidence: 99%

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 limitation of this model is given by its purpose, which does not consider the role of coupling topologies that correspond directly to structural connectivity data. From the perspective of complex network synchronization control, Ferrari et al (35) presented a model in which cortical areas are represented by networks composed of coupled Rulkov neurons. They improved the stable partial synchronization of the network by adjusting the coupling strength while the intensity of phase synchronization between the cortical areas varies depending on coupling strength.…”

Section: Discussionmentioning

confidence: 99%

Synchronization Stability Model of Complex Brain Networks: An EEG Study

Yin

Tan

et al. 2020

Front. Psychiatry

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In this paper, from the perspective of complex network dynamics we investigated the formation of the synchronization state of the brain networks. Based on the Lyapunov stability theory of complex networks, a synchronous steady-state model suitable for application to complex dynamic brain networks was proposed. The synchronization stability problem of brain network state equation was transformed into a convex optimization problem with Block Coordinate Descent (BCD) method. By using Random Apollo Network (RAN) method as a node selection rule, the brain network constructs its subnet work dynamically. We also analyzes the change of the synchronous stable state of the subnet work constructed by this method with the increase of the size of the network. Simulation EEG data from alcohol addicts patients and Real experiment EEG data from schizophrenia patients were used to verify the robustness and validity of the proposed model. Differences in the synchronization characteristics of the brain networks between normal and alcoholic patients were analyzed, so as differences between normal and schizophrenia patients. The experimental results indicated that the establishment of a synchronous steady state model in this paper could be used to verify the synchronization of complex dynamic brain networks and potentially be of great value in the further study of the pathogenic mechanisms of mental illness.

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“…Each time the neuron begins to burst, the neuronal recovery variable y reaches its maximum value. For a better analysis of phase synchronization, the phase of the recovery variable y is discussed here, giving the definition of phase θ [24] θ…”

Section: Phase Synchronization and Synchronization Transitionmentioning

confidence: 99%

“…On this foundation, Cao et al [19][20][21][22][23] disclosed two identical Rulkov neurons coupled with chemical and electrical synapses, respectively, and reported the influence of the coupling strength on synchronization in detail. By constructing a small-world network made up of Rulkov neurons, Ferrari et al [24] proposed the idea of using delayed feedback to suppress synchronization. Rakshit S et al [25] revealed several types of firing patterns and synchronous behavior of Rulkov neurons under the joint effect of internal connections and a chemical synapse.…”

Section: Introductionmentioning

confidence: 99%

Multistability and Phase Synchronization of Rulkov Neurons Coupled with a Locally Active Discrete Memristor

et al. 2023

Fractal Fract

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In order to enrich the dynamic behaviors of discrete neuron models and more effectively mimic biological neural networks, this paper proposes a bistable locally active discrete memristor (LADM) model to mimic synapses. We explored the dynamic behaviors of neural networks by introducing the LADM into two identical Rulkov neurons. Based on numerical simulation, the neural network manifested multistability and new firing behaviors under different system parameters and initial values. In addition, the phase synchronization between the neurons was explored. Additionally, it is worth mentioning that the Rulkov neurons showed synchronization transition behavior; that is, anti-phase synchronization changed to in-phase synchronization with the change in the coupling strength. In particular, the anti-phase synchronization of different firing patterns in the neural network was investigated. This can characterize the different firing behaviors of coupled homogeneous neurons in the different functional areas of the brain, which is helpful to understand the formation of functional areas. This paper has a potential research value and lays the foundation for biological neuron experiments and neuron-based engineering applications.

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A network of networks model to study phase synchronization using structural connection matrix of human brain

Cited by 22 publications

References 42 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

Synchronization Stability Model of Complex Brain Networks: An EEG Study

Multistability and Phase Synchronization of Rulkov Neurons Coupled with a Locally Active Discrete Memristor

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