Synchronization and control in time-delayed complex networks and spatio-temporal patterns

Banerjee, Santo; Kurths, Jürgen; Schöll, Eckehard

doi:10.1140/epjst/e2016-02627-6

Cited by 5 publications

(2 citation statements)

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“…Complexity measures are of trending interest in both classical and quantum systems. It is very effective to quantify the 'uncertain' phenomenon in several branches of Science, Engineering and Management [34][35][36][37][38][39]. For example, the structure of an organization is complex, may not be chaotic.…”

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confidence: 99%

Complexity, Chaos and Fluctuations

Banerjee

Colangeli

2017

Eur. Phys. J. Spec. Top.

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This EPJ special topics issue is a collection of contributions about some recent developments in statistical mechanics and dynamical complexity. The various articles report results on statistical fluctuations, non equilibrium models, chaos and nonlinear interactions, complexity and its various associated measures.The long-standing endeavor of Statistical Mechanics is to establish a mathematical framework which allows to encompass different levels of description of a many-particle system: (i) the microscopic setting, stemming from the Boltzmann-Gibbs statistical ensemble theory [1, 2]; (ii) the mesoscopic scale, typically described in the framework of kinetic theory [3-5], interacting particle systems [6][7][8][9][10][11][12] or even more general stochastic processes [13,14]; (iii) the macroscopic level, which is the standard set-up in contexts such as the derivation of hydrodynamic equations, turbulence [15][16][17], Irreversible Thermodynamics [18], etc. Equilibrium phenomena have been investigated and understood much more thoroughly than non-equilibrium ones. At present, the equilibrium theory may be considered complete, for what concerns the microscopic foundations of equilibrium thermodynamics, including the theory of phase transitions and critical phenomena. On the other hand, in spite of the long-standing research activity, Nonequilibrium Statistical Mechanics is still an open field of research. One of the reasons is that nonequilibrium phenomena are so numerous, diverse and complex that attaining a unified description may still appear as an elusive ambition. In particular, away from equilibrium one cannot bypass the analysis of the microscopic dynamics even in the study of stationary states, that are the simplest beyond equilibrium.Nevertheless, relevant progresses were made in this field in the last decades, cf.[19]. We refer, for instance, to the discovery of the Fluctuation Relations for deterministic and stochastic dynamics [20][21][22]. Much effort was needed, in particular, to identify the minimal mathematical ingredients as well as the physical mechanisms lying beneath the validity of such relations [23][24][25]. Another result, whose mathematical theory was originally dug out in the sixties of the last century [26,27] and was then gradually a

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confidence: 99%

Complexity, Chaos and Fluctuations

Banerjee

Colangeli

2017

Eur. Phys. J. Spec. Top.

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“…It is well known that synchronization is crucial to the information transmission among coupled neurons, it is correlated with many physiological mechanisms of normal and pathological brain functions (Wang et al 2015a(Wang et al , b, 2016Banerjee et al 2016;Mehta et al 2002) as well as with several neurological diseases (Wang et al 2016;Levy et al 2000;Mormann et al 2003;Wu et al 2014), such as epilepsy and tremor in Parkinson's disease. In the past decade, synchronization phenomena in coupled neural system has been extensively studied and many synchronization phenomena have been found, such as noise-induced synchronization (Wu et al 2014;Wang et al 2015b;Zhou and Kurths 2003;Perc 2009), bursting synchronization (Ngouonkadi et al 2016), time delayinduced synchronization (Zhang et al 2014), the partial synchronization (Steur et al (2012), the phase synchronization (Yu et al 2011;Ngouonkadi et al 2015).…”

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confidence: 99%

Cooperative effect of random and time-periodic coupling strength on synchronization transitions in one-way coupled neural system: mean field approach

2017

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The cooperative effect of random coupling strength and time-periodic coupling strengh on synchronization transitions in one-way coupled neural system has been investigated by mean field approach. Results show that cooperative coupling strength (CCS) plays an active role for the enhancement of synchronization transitions. There exist an optimal frequency of CCS which makes the system display the best CCS-induced synchronization transitions, a critical frequency of CCS which can not further affect the CCS-induced synchronization transitions, and a critical amplitude of CCS which can not occur the CCS-induced synchronization transitions. Meanwhile, noise intensity plays a negative role for the CCS-induced synchronization transitions. Furthermore, it is found that the novel CCS amplitude-induced synchronization transitions and CCS frequency-induced synchronization transitions are found.

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Open-Ended Intelligence

Weinbaum

Veitas

2016

Artificial General Intelligence

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Synchronization and control in time-delayed complex networks and spatio-temporal patterns

Cited by 5 publications

References 42 publications

Complexity, Chaos and Fluctuations

Complexity, Chaos and Fluctuations

Cooperative effect of random and time-periodic coupling strength on synchronization transitions in one-way coupled neural system: mean field approach

Open-Ended Intelligence

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