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Controlling synchronization patterns...

Lehnert, Judith.

Controlling synchronization patterns in complex networks

Record Type:	Electronic resources : Monograph/item
Title/Author:	Controlling synchronization patterns in complex networksby Judith Lehnert.
Author:	Lehnert, Judith.
Published:	Cham :Springer International Publishing :2016.
Description:	xv, 203 p. :ill., digital ;24 cm.
Contained By:	Springer eBooks
Subject:	Synchronization.
Online resource:	http://dx.doi.org/10.1007/978-3-319-25115-8
ISBN:	9783319251158$q(electronic bk.)

Controlling synchronization patterns in complex networks
Lehnert, Judith.

Controlling synchronization patterns in complex networks[electronic resource] /by Judith Lehnert. - Cham :Springer International Publishing :2016. - xv, 203 p. :ill., digital ;24 cm. - Springer theses,2190-5053. - Springer theses..

Introduction -- Complex Dynamical Networks -- Synchronization In Complex Networks -- Control of Synchronization Transitions by Balancing Excitatory and Inhibitory Coupling -- Cluster and Group Synchrony: The Theory -- Zero-Lag and Cluster Synchrony: Towards Applications -- Adaptive Control -- Adaptive Time-Delayed Feedback Control -- Adaptive Control of Cluster States in Network Motifs -- Adaptive Topologies -- Conclusion.

This research aims to achieve a fundamental understanding of synchronization and its interplay with the topology of complex networks. Synchronization is a ubiquitous phenomenon observed in different contexts in physics, chemistry, biology, medicine and engineering. Most prominently, synchronization takes place in the brain, where it is associated with several cognitive capacities but is - in abundance - a characteristic of neurological diseases. Besides zero-lag synchrony, group and cluster states are considered, enabling a description and study of complex synchronization patterns within the presented theory. Adaptive control methods are developed, which allow the control of synchronization in scenarios where parameters drift or are unknown. These methods are, therefore, of particular interest for experimental setups or technological applications. The theoretical framework is demonstrated on generic models, coupled chemical oscillators and several detailed examples of neural networks.

ISBN: 9783319251158$q(electronic bk.)

Standard No.: 10.1007/978-3-319-25115-8doiSubjects--Topical Terms:

226631
Synchronization.

LC Class. No.: Q172.5.S96

Dewey Class. No.: 003.75

Controlling synchronization patterns in complex networks
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100 1 $a Lehnert, Judith. $3 738411
245 1 0 $a Controlling synchronization patterns in complex networks $h [electronic resource] / $c by Judith Lehnert.
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2016.
300 $a xv, 203 p. : $b ill., digital ; $c 24 cm.
490 1 $a Springer theses, $x 2190-5053
505 0 $a Introduction -- Complex Dynamical Networks -- Synchronization In Complex Networks -- Control of Synchronization Transitions by Balancing Excitatory and Inhibitory Coupling -- Cluster and Group Synchrony: The Theory -- Zero-Lag and Cluster Synchrony: Towards Applications -- Adaptive Control -- Adaptive Time-Delayed Feedback Control -- Adaptive Control of Cluster States in Network Motifs -- Adaptive Topologies -- Conclusion.
520 $a This research aims to achieve a fundamental understanding of synchronization and its interplay with the topology of complex networks. Synchronization is a ubiquitous phenomenon observed in different contexts in physics, chemistry, biology, medicine and engineering. Most prominently, synchronization takes place in the brain, where it is associated with several cognitive capacities but is - in abundance - a characteristic of neurological diseases. Besides zero-lag synchrony, group and cluster states are considered, enabling a description and study of complex synchronization patterns within the presented theory. Adaptive control methods are developed, which allow the control of synchronization in scenarios where parameters drift or are unknown. These methods are, therefore, of particular interest for experimental setups or technological applications. The theoretical framework is demonstrated on generic models, coupled chemical oscillators and several detailed examples of neural networks.
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650 2 4 $a Mathematical Models of Cognitive Processes and Neural Networks. $3 567118
650 2 4 $a Physical Chemistry. $3 273698
650 2 4 $a Vibration, Dynamical Systems, Control. $3 274667
650 2 4 $a Systems Theory, Control. $3 274654
710 2 $a SpringerLink (Online service) $3 273601
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856 4 0 $u http://dx.doi.org/10.1007/978-3-319-25115-8
950 $a Physics and Astronomy (Springer-11651)