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This eBook is a collection of articles from a Frontiers Research Topic. Frontiers Research Topics are very popular trademarks of the Frontiers Journals Series: they are collections of at least ten articles, all centered on a particular subject. With their unique mix of varied contributions from Original Research to Review Articles, Frontiers Research Topics unify the most influential researchers, the latest key findings and historical advances in a hot research area! Find out more on how to host your own Frontiers Research Topic or contribute to one as an author by contacting the Frontiers Editorial Office: frontiersin.org/about/contact

self-organization --- cyber-physical system --- swarm robotics --- resilience --- scalability

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Nanotechnology --- Molecular technology --- Nanoscale technology --- self-organization --- thin films --- nanostructuring --- device fabrication --- nanoscale patterning --- lithography --- High technology --- Technology - General --- Nanotechnology.

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Biophysical modelling of brain activity has had a long and illustrious history and has during the last few years profited from technological advances that allow obtaining neuroimaging data at an unprecedented spatiotemporal resolution. It is a very active area of research with applications ranging from the characterization of neurobiological and cognitive processes to constructing artificial brains in silico and building brain-machine interface and neuroprosthetic devices. The relevant community has always benefited from interdisciplinary interactions between different and seemingly distant fields ranging from mathematics and engineering to linguistics and psychology. This Research Topic aims to promote such interactions and we welcome all works related or that can contribute to an understanding of and construction of models for neural activity. The focus will be on biophysical models describing brain activity usually measured by fMRI or electrophysiology. Such models can be divided into two large classes: neural mass and neural field models. The main difference between these two classes is that field models prescribe how a quantity characterizing neural activity (such as average depolarization of a neural population) evolves over both space and time as opposed to mass models which characterize the evolution of this quantity over time only and assume that all neurons of a population are located at (approximately) the same point. This Research Topic will focus on both classes of such models and discuss several of their aspects and relative merits focusing on the main ideas of neural field and mass theories that span from synapses to the whole brain, comparisons of their predictions with EEG and MEG spectra of spontaneous brain activity, evoked responses, seizures, and fitting to data to infer brain states and map physiological parameters. We welcome submissions shedding light on the underlying dynamics within the neural tissue that can yield explanations of disorders such as epilepsy and migraine as well as normal functions such as attention, working memory and decision making and encourage papers reporting new theoretical and/or modelling work as well as advances in experimental methods that can benefit modelling endeavours. The aim of this Research Topic is to provide a forum for state-of-the-art research in the field and foster new theoretical advances.

Calculus --- Mathematics --- Physical Sciences & Mathematics --- neural disorders --- self-organization --- Electroencephalogram --- neural networks --- Electrophysiology --- Integro-differential equations --- neural field theory --- neural masses --- oscillations --- anaesthesia

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Nature is not only stable and static but also unstable and dynamical ? this has been shown by physics during the last 40 years. Today, instabilities are regarded as productive and creative sources of change, pattern formation, and growth. They constitute the nomological nucleus of self-organization, chaos and complexity, time's arrow and chance. This change in understanding nature has also modified sciences. Physics has traditionally limited its scope to stability. Currently, it is expanding and renewing itself: a late-modern physics is emerging, and discloses rich interdisciplinary perspectiv

Nonlinear theories. --- Physics --- Stability. --- Dynamics --- Mechanics --- Motion --- Vibration --- Benjamin-Feir instability --- Equilibrium --- Nonlinear problems --- Nonlinearity (Mathematics) --- Calculus --- Mathematical analysis --- Mathematical physics --- Philosophy. --- Physics - Philosophy. --- Physics - History. --- Instability. --- complexity. --- philosophy. --- physics. --- self-organization. --- Instabilität. --- Naturphilosophie. --- Naturverständnis. --- Philosophie. --- Physik. --- Wissenschaftsentwicklung. --- History.

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foreword by Hermann Haken For the past twenty years Scott Kelso's research has focused on extending the physical concepts of self- organization and the mathematical tools of nonlinear dynamics to understand how human beings (and human brains) perceive, intend, learn, control, and coordinate complex behaviors. In this book Kelso proposes a new, general framework within which to connect brain, mind, and behavior.Kelso's prescription for mental life breaks dramatically with the classical computational approach that is still the operative framework for many newer psychological and neurophysiological studies. His core thesis is that the creation and evolution of patterned behavior at all levels—from neurons to mind—is governed by the generic processes of self-organization. Both human brain and behavior are shown to exhibit features of pattern-forming dynamical systems, including multistability, abrupt phase transitions, crises, and intermittency.Dynamic Patterns brings together different aspects of this approach to the study of human behavior, using simple experimental examples and illustrations to convey essential concepts, strategies, and methods, with a minimum of mathematics.Kelso begins with a general account of dynamic pattern formation. He then takes up behavior, focusing initially on identifying pattern-forming instabilities in human sensorimotor coordination. Moving back and forth between theory and experiment, he establishes the notion that the same pattern-forming mechanisms apply regardless of the component parts involved (parts of the body, parts of the nervous system, parts of society) and the medium through which the parts are coupled. Finally, employing the latest techniques to observe spatiotemporal patterns of brain activity, Kelso shows that the human brain is fundamentally a pattern forming dynamical system, poised on the brink of instability. Self-organization thus underlies the cooperative action of neurons that produces human behavior in all its forms.

577.22 --- 577.22 Structural organization of life. Assembly and self-organization of biological structures --- Structural organization of life. Assembly and self-organization of biological structures --- Systèmes auto-organisés. --- Systèmes auto-organisés --- #WSCH:AAS2 --- Neuropsychology --- Self-organizing systems --- Learning systems (Automatic control) --- Self-optimizing systems --- Cybernetics --- Intellect --- Learning ability --- Synergetics --- Neurophysiology --- Psychophysiology --- Psychiatry --- Physiology of nerves and sense organs --- Social psychology --- Neuropsychology. --- Self-organizing systems. --- Neuropsychologie --- Neuropsychologie.