Cooperative phenomena in systems far from thermal equilibrium and in nonphysical systems

Haken, Hermann

doi:10.1103/revmodphys.47.67

Cited by 958 publications

(360 citation statements)

References 124 publications

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“…Basic mean field models capture changes of the mean firing rate (Brunel and Wang, 2003), whereas more sophisticated mean field models account for parameter dispersion in the neurons and the subsequent richer behavioral repertoire of the mean field dynamics (Assisi et al, 2005;Jirsa and Stefanescu, 2011;Jirsa, 2008, 2011). These approaches demonstrate a relatively new concept from statistical physics that macroscopic physical systems obey laws that are independent of the details of the microscopic constituents they are built of (Haken, 1975). These and related ideas have been exploited in neurosciences (Buzsaki, 2006;Kelso, 1995).…”

Section: A Choice Of Local Mesoscopic Dynamicsmentioning

confidence: 99%

The Virtual Brain Integrates Computational Modeling and Multimodal Neuroimaging

et al. 2013

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Brain function is thought to emerge from the interactions among neuronal populations. Apart from traditional efforts to reproduce brain dynamics from the micro-to macroscopic scales, complementary approaches develop phenomenological models of lower complexity. Such macroscopic models typically generate only a few selected-ideally functionally relevant-aspects of the brain dynamics. Importantly, they often allow an understanding of the underlying mechanisms beyond computational reproduction. Adding detail to these models will widen their ability to reproduce a broader range of dynamic features of the brain. For instance, such models allow for the exploration of consequences of focal and distributed pathological changes in the system, enabling us to identify and develop approaches to counteract those unfavorable processes. Toward this end, The Virtual Brain (TVB) (www.thevirtualbrain.org), a neuroinformatics platform with a brain simulator that incorporates a range of neuronal models and dynamics at its core, has been developed. This integrated framework allows the modelbased simulation, analysis, and inference of neurophysiological mechanisms over several brain scales that underlie the generation of macroscopic neuroimaging signals. In this article, we describe how TVB works, and we present the first proof of concept.

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Section: A Choice Of Local Mesoscopic Dynamicsmentioning

confidence: 99%

The Virtual Brain Integrates Computational Modeling and Multimodal Neuroimaging

et al. 2013

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“…inhomogeneous broadening) and coupling strengths. It is one of the central models of quantum optics, describing for example lasing [30], equilibrium superradiance [31], and dynamical superradiance [32]. It can be viewed as a generalization of the BCS Hamiltonian to the strong-coupling regime, as is apparent on rewriting the spin operators in terms of two species of fermions.…”

Section: Background and Basic Modelmentioning

confidence: 99%

The new physics of non-equilibrium condensates: insights from classical dynamics

Eastham¹

2007

J. Phys.: Condens. Matter

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Abstract. We discuss the dynamics of classical Dicke-type models, aiming to clarify the mechanisms by which coherent states could develop in potentially non-equilibrium systems such as semiconductor microcavities.We present simulations of an undamped model which show spontaneous coherent states with persistent oscillations in the magnitude of the order parameter. These states are generalisations of superradiant ringing to the case of inhomogeneous broadening. They correspond to the persistent gap oscillations proposed in fermionic atomic condensates, and arise from a variety of initial conditions. We show that introducing randomness into the couplings can suppress the oscillations, leading to a limiting dynamics with a time-independent order parameter. This demonstrates that non-equilibrium generalisations of polariton condensates can be created even without dissipation. We explain the dynamical origins of the coherence in terms of instabilities of the normal state, and consider how it can additionally develop through scattering and dissipation.

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“…(66) into Eq. (67) and using the expansions (7), (22) and (25), we find the following coupled differential equationṡ…”

mentioning

confidence: 99%

Relativistic and Non-Relativistic Quantum Brownian Motion in an Anisotropic Dissipative Medium

Amooghorban

Kheirandish²

2014

Int J Theor Phys

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Using a minimal-coupling-scheme we investigate the quantum Brownian motion of a particle in an anisotropic-dissipative-medium under the influence of an arbitrary potential in both relativistic and non-relativistic regimes. A general quantum Langevin equation is derived and explicit expressions for quantum-noise and dynamical variables of the system are obtained. The equations of motion for the canonical variables are solved explicitly and an expression for the radiation-reaction of a charged particle in the presence of a dissipative-medium is obtained. Some examples are given to elucidate the applicability of this approach.

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Cooperative phenomena in systems far from thermal equilibrium and in nonphysical systems

Cited by 958 publications

References 124 publications

The Virtual Brain Integrates Computational Modeling and Multimodal Neuroimaging

The Virtual Brain Integrates Computational Modeling and Multimodal Neuroimaging

The new physics of non-equilibrium condensates: insights from classical dynamics

Relativistic and Non-Relativistic Quantum Brownian Motion in an Anisotropic Dissipative Medium

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