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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.
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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