Controlling Synchronization Patterns in Complex Networks [electronic resource] / by Judith Lehnert.
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TextLanguage: İngilizce Series: Springer Theses, Recognizing Outstanding Ph.D. ResearchPublisher: Cham : Springer International Publishing : Imprint: Springer, 2016Edition: 1st ed. 2016Description: XV, 203 p. 67 illus., 50 illus. in color : online resourceContent type: - text
- computer
- online resource
- 9783319251158
- Springer ebooks.
- Q172.5 .S96 2016
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Merkez Kütüphane Genel Koleksiyon / Main Collection | Merkez Kütüphane | Genel Koleksiyon | Q172.5.S96 2016 (Browse shelf(Opens below)) | 1 | Available | 0053295 |
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| Q172.5.P77 W966 2005 Yanlış yönde kuantum sıçramalar / | Q172.5.S47 A76 2003 Bilimsel gaflar : | Q172.5.S95 G65 2002 The symmetry perspective : from equilibrium to chaos in phase space and physical space / | Q172.5.S96 2016 Controlling Synchronization Patterns in Complex Networks | Q173 .H3919 2019 Büyük sorulara kısa yanıtlar / | Q173 .H73 2011 Tozun gizli hayatı : evrenden mutfak tezgahına küçük şeylerin büyük sonuçları / | Q173 .K37 2007 Bilime yabancı sanat / |
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.
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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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