rdf-syntax-grammar
mods:
  schema location: http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
  title info:
    title: Controlling Synchronization Patterns in Complex Networks
  name:
    name part: Lehnert, Judith.
    role:
      role term: creator
    role:
      role term: author.
  name:
    name part: SpringerLink (Online service)
  type of resource: text
  origin info:
    place:
      place term: gw
    date issued: 2016
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: electronic
    form: electronic resource
    reformatting quality: access
    form: remote
    extent: XV, 203 p. online resource.
  abstract: 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.
  table of contents: 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.
  note: by Judith Lehnert.
  subject:
    topic: Physics
  subject:
    topic: Physical chemistry
  subject:
    topic: System theory
  subject:
    topic: Neural networks (Computer science)
  subject:
    topic: Vibration
  subject:
    topic: Dynamical systems
  subject:
    topic: Dynamics
  subject:
    topic: Physics
  subject:
    topic: Applications of Graph Theory and Complex Networks
  subject:
    topic: Mathematical Models of Cognitive Processes and Neural Networks
  subject:
    topic: Physical Chemistry
  subject:
    topic: Vibration, Dynamical Systems, Control
  subject:
    topic: Systems Theory, Control
  classification: QC1-QC999
  classification: 621
  related item:
    title info:
      title: Springer eBooks
  related item:
    title info:
      title: Springer Theses, Recognizing Outstanding Ph.D. Research
  identifier: 9783319251158
  identifier: http://dx.doi.org/10.1007/978-3-319-25115-8
  record info:
    record creation date: 151106
    record change date: 20170520022944.0
    record identifier: 978-3-319-25115-8