04537nam 22008055 450 991025460980332120200706021231.03-319-25115-510.1007/978-3-319-25115-8(CKB)3710000000501091(EBL)4084473(SSID)ssj0001584990(PQKBManifestationID)16265763(PQKBTitleCode)TC0001584990(PQKBWorkID)14864254(PQKB)10983712(DE-He213)978-3-319-25115-8(MiAaPQ)EBC4084473(PPN)190537132(EXLCZ)99371000000050109120151106d2016 u| 0engur|n|---|||||txtccrControlling Synchronization Patterns in Complex Networks /by Judith Lehnert1st ed. 2016.Cham :Springer International Publishing :Imprint: Springer,2016.1 online resource (213 p.)Springer Theses, Recognizing Outstanding Ph.D. Research,2190-5053Description based upon print version of record.3-319-25113-9 Includes bibliographical references at the end of each chapters and index.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.Springer Theses, Recognizing Outstanding Ph.D. Research,2190-5053PhysicsNeural networks (Computer science)Chemistry, Physical and theoreticalVibrationDynamicsDynamicsSystem theoryApplications of Graph Theory and Complex Networkshttps://scigraph.springernature.com/ontologies/product-market-codes/P33010Mathematical Models of Cognitive Processes and Neural Networkshttps://scigraph.springernature.com/ontologies/product-market-codes/M13100Physical Chemistryhttps://scigraph.springernature.com/ontologies/product-market-codes/C21001Vibration, Dynamical Systems, Controlhttps://scigraph.springernature.com/ontologies/product-market-codes/T15036Systems Theory, Controlhttps://scigraph.springernature.com/ontologies/product-market-codes/M13070Physics.Neural networks (Computer science)Chemistry, Physical and theoretical.Vibration.Dynamics.Dynamics.System theory.Applications of Graph Theory and Complex Networks.Mathematical Models of Cognitive Processes and Neural Networks.Physical Chemistry.Vibration, Dynamical Systems, Control.Systems Theory, Control.003.75Lehnert Judithauthttp://id.loc.gov/vocabulary/relators/aut803791MiAaPQMiAaPQMiAaPQBOOK9910254609803321Controlling Synchronization Patterns in Complex Networks1805288UNINA