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Reconstructing Network Dynamics of Coupled Discrete Chaotic Units from Data.

Irem Topal1, Deniz Eroglu1

  • 1Faculty of Engineering and Natural Sciences, Kadir Has University, 34083 Istanbul, Turkey.

Physical Review Letters
|March 31, 2023
PubMed
Summary
This summary is machine-generated.

We developed a new method to reconstruct network dynamics from data, overcoming limitations of previous approaches. This technique aids in predicting complex system changes, like in neuron networks, with fewer constraints.

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Area of Science:

  • Computational Neuroscience
  • Network Science
  • Dynamical Systems Theory

Background:

  • Reconstructing network dynamics is vital for understanding complex systems like neuron networks.
  • Previous methods required extensive data or small system sizes, limiting real-world applicability.

Purpose of the Study:

  • To present a novel recovery scheme for identifying governing equations and interactions in complex networks.
  • To overcome the stringent constraints of previous network reconstruction techniques.
  • To enable the prediction of critical transitions in network dynamics.

Main Methods:

  • A hybrid approach combining theoretical model reduction and sparse recovery.
  • Identification of governing equations and network interactions in weakly coupled chaotic maps.
  • Application to neuronal systems on real mouse neocortex and artificial networks, with and without noise.

Main Results:

  • Successfully identified governing equations and network interactions.
  • Demonstrated the ability to ease unrealistic constraints on data length and system size.
  • Enabled the detection of critical transitions in network dynamics due to parameter changes.

Conclusions:

  • The developed recovery scheme offers a more practical approach to reconstructing network dynamics.
  • This method facilitates the prediction of complex system behavior and critical transitions.
  • Applicable to realistic neuronal networks, advancing neuroscience and network science research.