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Updated: Jun 22, 2026
07:33
Optogenetic Entrainment of Hippocampal Theta Oscillations in Behaving Mice
Published on: June 29, 2018
Synchronization and spatiotemporal patterns in coupled phase oscillators on a weighted planar network.
Yuki Kagawa1, Atsuko Takamatsu
1Department of Electrical Engineering and Bioscience, Waseda University, Tokyo 169-8555, Japan. ykagawa@aoni.waseda.jp
Summary
Network structure and edge weight distribution critically influence synchronization and oscillation patterns in biological systems. This research explores how weighted networks generate traveling and concentric waves, offering insights for plasmodial and neural networks.
Area of Science:
- Complex Systems
- Network Science
- Mathematical Biology
Background:
- Biological systems exhibit complex network structures and emergent spatiotemporal dynamics.
- Understanding the relationship between network topology and oscillatory patterns is crucial for fields like neuroscience and developmental biology.
Purpose of the Study:
- To investigate how network structure and edge weight distribution influence spatiotemporal oscillation patterns in two-dimensional biological systems.
- To determine the effect of weighted planar networks on global synchronization and pattern formation.
Main Methods:
- Constructed coupled phase oscillators on weighted planar networks.
- Investigated network dynamics under varying edge weight distributions.
- Analyzed synchronization propensity and emergent oscillation patterns (traveling and concentric waves).
Main Results:
- Edge weight distribution significantly impacts global synchronization and the emergence of oscillation patterns, even with fixed total network weight.
- Uniformly weighted networks favored in-phase locking.
- Center-weighted treelike networks promoted traveling waves.
- Periphery-weighted ring-shaped networks led to concentric waves.
Conclusions:
- Local coupling rules and weight distribution can control global spatiotemporal patterns in biological networks.
- Findings have potential applications in understanding and manipulating plasmodial networks and neural networks in the brain.
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