Titlebook: Emergent Complexity from Nonlinearity, in Physics, Engineering and the Life Sciences; Proceedings of the X Giorgio Mantica,Ruedi Stoop,Seba

经典

A Multiplex Network Model to Characterize Brain Atrophy in Structural MRIheimer disease (AD). Our techniques can supply a convenient mathematical framework to model structural inter- and intra-subject brain similarities in magnetic resonance images (MRI) within Alzheimer disease studies. We used a set of 100 structural T1 brain scans, from subjects of the Alzheimer’s Dis

ostracize

FOLLY

Robert J. Gale,David G. Loveringstructure is generated when a conventional Arnol’d tongue transits to a higher-dimensional Arnol’d tongue. We discovered that, at least, two periodic attractors coexist in the conventional Arnol’d tongue which can bifurcate to two one-tori via doubly-folded Neimark–Sacker bifurcation.

抗体

BADGE

Complex Bifurcation of Arnol’d Tongues Generated in Three-Coupled Delayed Logistic Mapsstructure is generated when a conventional Arnol’d tongue transits to a higher-dimensional Arnol’d tongue. We discovered that, at least, two periodic attractors coexist in the conventional Arnol’d tongue which can bifurcate to two one-tori via doubly-folded Neimark–Sacker bifurcation.

形上升才刺激

Mean-Field Transport of a Bose-Einstein Condensateroach. The effect of the interactions is to enhance or suppress the transport depending on the sign and strength of the interactions. These effects are discusses in detail in view of recent experiments probing non-equilibrium transport of ultracold quantum gases.

florid

Conference proceedings 2017electronic circuits to synchronisation effects in complex networks to biological systems, neural dynamics and the complex organisation of the brain. Resting on a solid mathematical basis, these investigations address highly interdisciplinary problems in physics, engineering, biology and biochemistry..

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短程旅游

BLUSH

https://doi.org/10.1007/978-1-4757-1724-2 by Rulkov’s phenomenological, low-dimensional, map-based neuron models. Here, using phase response curves, we show that Rulkov map neurons also respond to transient pulse stimulation in a way that is compatible with the biological examples. This is important because Rulkov maps are computationally