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024 7 $a10.1007/978-3-319-25115-8
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100   $a20151106d2016      u|         0
101 0 $aeng
135   $aur|n|---|||||
181   $ctxt
182   $cc
183   $acr
200 10$aControlling Synchronization Patterns in Complex Networks /$fby Judith Lehnert
205   $a1st ed. 2016.
210  1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2016.
215   $a1 online resource (213 p.)
225 1 $aSpringer Theses, Recognizing Outstanding Ph.D. Research,$x2190-5053
300   $aDescription based upon print version of record.
311   $a3-319-25113-9 
320   $aIncludes bibliographical references at the end of each chapters and index.
327   $aIntroduction -- 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.
330   $aThis research aims to achieve a fundamental understanding of synchronization and its interplay with the topology of complex networks. Synchronization is a ubiquitous phenomenon observed in different contexts in physics, chemistry, biology, medicine and engineering. Most prominently, synchronization takes place in the brain, where it is associated with several cognitive capacities but is - in abundance - a characteristic of neurological diseases. Besides zero-lag synchrony, group and cluster states are considered, enabling a description and study of complex synchronization patterns within the presented theory. Adaptive control methods are developed, which allow the control of synchronization in scenarios where parameters drift or are unknown. These methods are, therefore, of particular interest for experimental setups or technological applications. The theoretical framework is demonstrated on generic models, coupled chemical oscillators and several detailed examples of neural networks.
410  0$aSpringer Theses, Recognizing Outstanding Ph.D. Research,$x2190-5053
606   $aPhysics
606   $aNeural networks (Computer science)
606   $aChemistry, Physical and theoretical
606   $aVibration
606   $aDynamics
606   $aDynamics
606   $aSystem theory
606   $aApplications of Graph Theory and Complex Networks$3https://scigraph.springernature.com/ontologies/product-market-codes/P33010
606   $aMathematical Models of Cognitive Processes and Neural Networks$3https://scigraph.springernature.com/ontologies/product-market-codes/M13100
606   $aPhysical Chemistry$3https://scigraph.springernature.com/ontologies/product-market-codes/C21001
606   $aVibration, Dynamical Systems, Control$3https://scigraph.springernature.com/ontologies/product-market-codes/T15036
606   $aSystems Theory, Control$3https://scigraph.springernature.com/ontologies/product-market-codes/M13070
615  0$aPhysics.
615  0$aNeural networks (Computer science)
615  0$aChemistry, Physical and theoretical.
615  0$aVibration.
615  0$aDynamics.
615  0$aDynamics.
615  0$aSystem theory.
615 14$aApplications of Graph Theory and Complex Networks.
615 24$aMathematical Models of Cognitive Processes and Neural Networks.
615 24$aPhysical Chemistry.
615 24$aVibration, Dynamical Systems, Control.
615 24$aSystems Theory, Control.
676   $a003.75
700   $aLehnert$b Judith$4aut$4http://id.loc.gov/vocabulary/relators/aut$0803791
801  0$bMiAaPQ
801  1$bMiAaPQ
801  2$bMiAaPQ
906   $aBOOK
912   $a9910254609803321
996   $aControlling Synchronization Patterns in Complex Networks$91805288
997   $aUNINA