Controlling Synchronization Patterns in Complex Networks
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Beschreibung
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.
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.
Nominated as an outstanding Ph.D. thesis by the Technische Universität Berlin, Germany Gives a broad and comprehensive treatment of synchronization and its control in complex time-delayed networks Introduces, studies, and compares several adaptive control methods to stabilize cluster synchronization Studies the control of synchronization in neural networks on several detailed examples Includes supplementary material: sn.pub/extras
Autor*in
Judith Lehnert
Themen in »Controlling Synchronization Patterns in Complex Networks«
Adaptive control Adaptive networks Adaptive time-delayed feedback control Adaptive topologies Cluster synchronization Coupled chemical oscillators Delay differential equation Master stability function Neural networks Nonlinear dynamical systems Synchronization of complex networks