Topology identification of uncertain nonlinearly coupled complex networks with delays based on anticipatory synchronization

Che, Yanqiu; Li, Ruixue; Cui, Shigang; Wang, Jiang; Wei, Xile; Deng, Bin

doi:10.1063/1.4793541

Cited by 17 publications

(12 citation statements)

References 44 publications

(77 reference statements)

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“…With rare exceptions (Pyragiené and Pyragas, 2013), previous observations of AS in theoretical, physical, and biological systems were based on the original framework, which included a negative delayed selffeedback (Voss, 2000;Voss, 2001a;Voss, 2001b;Pyragas and Pyragiené, 2008;Masoller and Zanette, 2001;Kostur et al, 2005;Ciszak et al, 2003;Sivaprakasam et al, 2001;Tang and Liu, 2003;Ciszak et al, 2009;Che et al, 2013). Despite efforts to join concepts of anticipatory behavior and AS dynamics (Stepp and Turvey, 2010;Stephen and Dixon, 2011), direct evidence for it in the brain have not been reported.…”

Section: Neuronal Populations Can Exhibit Asmentioning

confidence: 99%

“…This counterintuitive synchronization regime can be a stable solution of two dynamical systems coupled in a master-slave (sender-receiver) configuration, provided that the slave also receives a negative delayed self-feedback (Masoller and Zanette, 2001;Ciszak et al, 2003;Ciszak et al, 2004;Kostur et al, 2005;Che et al, 2013). In AS, the receiver's trajectory is able to precede that of the sender by predicting the sender's future behavior.…”

Section: Introductionmentioning

confidence: 99%

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Modeling positive Granger causality and negative phase lag between cortical areas

et al. 2014

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Different measures of directional influence have been employed to infer effective connectivity in the brain. When the connectivity between two regions is such that one them (the sender) strongly influences the other (the receiver), a positive phase lag is often expected. The assumption is that the time difference implicit in the relative phase reflects the transmission time of neuronal activity. However, Brovelli et al. (2004) observed that, in monkeys engaged in processing a cognitive task, a dominant directional influence from one area of sensorimotor cortex to another may be accompanied by either a negative or a positive time delay. Here we present a model of two brain regions, coupled with a well-defined directional influence, that displays similar features to those observed in the experimental data. This model is inspired by the theoretical framework of Anticipated Synchronization developed in the field of dynamical systems. Anticipated Synchronization is a form of synchronization that occurs when a unidirectional influence is transmitted from a sender to a receiver, but the receiver leads the sender in time. This counterintuitive synchronization regime can be a stable solution of two dynamical systems coupled in a masterslave (sender-receiver) configuration when the slave receives a negative delayed self-feedback. Despite efforts to understand the dynamics of Anticipated Synchronization, experimental evidence for it in the brain has been lacking. By reproducing experimental delay times and coherence spectra, our results provide a theoretical basis for the underlying mechanisms of the observed dynamics, and suggest that the primate cortex could operate in a regime of Anticipated Synchronization as part of normal neurocognitive function.

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Section: Neuronal Populations Can Exhibit Asmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Modeling positive Granger causality and negative phase lag between cortical areas

et al. 2014

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“…Almost all well-known dynamical chaotic and hyper chaotic systems have the form of Eq. (3), which meets the condition of assumption 1 [31].…”

Section: Adaptive Cluster Synchronizationmentioning

confidence: 66%

Adaptive Cluster Synchronization of Directed Complex Networks with Time Delays

2014

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This paper studied the cluster synchronization of directed complex networks with time delays. It is different from undirected networks, the coupling configuration matrix of directed networks cannot be assumed as symmetric or irreducible. In order to achieve cluster synchronization, this paper uses an adaptive controller on each node and an adaptive feedback strategy on the nodes which in-degree is zero. Numerical example is provided to show the effectiveness of main theory. This method is also effective when the number of clusters is unknown. Thus, it can be used in the community recognizing of directed complex networks.

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“…On the other hand, environmental uncertainty, always modeled by random noise, is also an inherent ingredient in complex networks. Now, identifying parameters and topology in time-delayed networks under noise circumstance has gradually attracted much research interest [22][23][24][25][26][27][28][29][30].…”

Section: Introductionmentioning

confidence: 99%

Revealing network topology and dynamical parameters in delay-coupled complex network subjected to random noise

Yang

Wei

2015

Nonlinear Dyn

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It is well known that random noise and time delay are two inherent ingredients in complex networks, whose dynamical parameters and topological structures are often unknown or uncertain. This paper will employ the techniques of impulsive control and adaptive control to infer dynamical parameters and network topology in delay-coupled complex network under circumstance noise. By constructing an appropriate adaptive-impulsive control strategy in the response network, the unknown dynamical parameters and topology structure contained in the drive network are to be accurately identified; moreover, these two networks will achieve the global exponential synchronization in mean square. Based on the comparison theorem of impulsive differential equations, the accuracy of the proposed identification strategy is rigorously proved. Finally, two examples with networks of chaotic oscillators are presented to illustrate the application of the suggested strategy. Meanwhile, numerical results indicate that our proposed scheme is robust against the impulsive gain, the update gain and the network topology.

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Topology identification of uncertain nonlinearly coupled complex networks with delays based on anticipatory synchronization

Cited by 17 publications

References 44 publications

Modeling positive Granger causality and negative phase lag between cortical areas

Modeling positive Granger causality and negative phase lag between cortical areas

Adaptive Cluster Synchronization of Directed Complex Networks with Time Delays

Revealing network topology and dynamical parameters in delay-coupled complex network subjected to random noise

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