Controlling Synchronization Patterns in Complex Networks

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...

Πλήρης περιγραφή

Λεπτομέρειες βιβλιογραφικής εγγραφής
Κύριος συγγραφέας: Lehnert, Judith (Συγγραφέας)
Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
Γλώσσα:English
Έκδοση: Cham : Springer International Publishing : Imprint: Springer, 2016.
Σειρά:Springer Theses, Recognizing Outstanding Ph.D. Research,
Θέματα:
Διαθέσιμο Online:Full Text via HEAL-Link
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100 1 |a Lehnert, Judith.  |e author. 
245 1 0 |a Controlling Synchronization Patterns in Complex Networks  |h [electronic resource] /  |c by Judith Lehnert. 
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300 |a XV, 203 p.  |b online resource. 
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490 1 |a Springer Theses, Recognizing Outstanding Ph.D. Research,  |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. 
650 0 |a Physics. 
650 0 |a Physical chemistry. 
650 0 |a System theory. 
650 0 |a Neural networks (Computer science). 
650 0 |a Vibration. 
650 0 |a Dynamical systems. 
650 0 |a Dynamics. 
650 1 4 |a Physics. 
650 2 4 |a Applications of Graph Theory and Complex Networks. 
650 2 4 |a Mathematical Models of Cognitive Processes and Neural Networks. 
650 2 4 |a Physical Chemistry. 
650 2 4 |a Vibration, Dynamical Systems, Control. 
650 2 4 |a Systems Theory, Control. 
710 2 |a SpringerLink (Online service) 
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776 0 8 |i Printed edition:  |z 9783319251134 
830 0 |a Springer Theses, Recognizing Outstanding Ph.D. Research,  |x 2190-5053 
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950 |a Physics and Astronomy (Springer-11651)