Controlling Synchronization Patterns in Complex Networks

✍ Scribed by Judith Lehnert (auth.)

Publisher: Springer International Publishing
Year: 2016
Tongue: English
Leaves: 213
Series: Springer Theses
Edition: 1
Category: Library

No coin nor oath required. For personal study only.

✦ Synopsis

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.

✦ Table of Contents

Front Matter....Pages i-xv
Introduction....Pages 1-14
Complex Dynamical Networks....Pages 15-29
Front Matter....Pages 31-31
Synchronization in Complex Networks....Pages 33-41
Control of Synchronization Transitions by Balancing Excitatory and Inhibitory Coupling....Pages 43-60
Cluster and Group Synchrony: The Theory....Pages 61-94
Zero-Lag and Cluster Synchrony: Towards Applications....Pages 95-125
Front Matter....Pages 127-127
Adaptive Control....Pages 129-132
Adaptive Time-Delayed Feedback Control....Pages 133-146
Adaptive Control of Cluster States in Network Motifs....Pages 147-164
Adaptive Topologies....Pages 165-188
Conclusion....Pages 189-194
Back Matter....Pages 195-203

✦ Subjects

Complex Networks; Mathematical Models of Cognitive Processes and Neural Networks; Physical Chemistry; Vibration, Dynamical Systems, Control; Systems Theory, Control

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