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Updated: Apr 21, 2026
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Fabrication and Testing of Microfluidic Optomechanical Oscillators
Published on: May 29, 2014
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Dynamical robustness of coupled heterogeneous oscillators.
Gouhei Tanaka1, Kai Morino2, Hiroaki Daido3
1Graduate School of Engineering, The University of Tokyo, Tokyo 113-8656, Japan and Graduate School of Information Science and Technology, The University of Tokyo, Tokyo 113-8656, Japan and Institute of Industrial Science, The University of Tokyo, Tokyo 153-8505, Japan.
Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics
|October 30, 2014
Summary
Heterogeneity in coupled oscillator networks enhances robustness against component deterioration. Increased component diversity improves the network
Area of Science:
- Complex systems
- Nonlinear dynamics
- Network science
Background:
- Coupled oscillator networks exhibit complex dynamics.
- Component deterioration can disrupt network function.
- Heterogeneity is a common feature in biological systems.
Purpose of the Study:
- To investigate the impact of component deterioration on coupled heterogeneous oscillator networks.
- To develop analytical methods for quantifying network robustness.
- To explore the role of heterogeneity in network resilience.
Main Methods:
- Analytical derivation of critical points for network collapse.
- Approximation of order parameters near critical points.
- Modeling with Stuart-Landau oscillators and Morris-Lecar neuron models.
- Analysis of networks with electrical synapses.
Main Results:
- A general formula for the critical point of oscillation loss was derived.
- An approximate formula for the order parameter near criticality was obtained.
- Greater oscillator heterogeneity leads to increased network robustness against deterioration.
- This robustness was confirmed in both Stuart-Landau and Morris-Lecar models.
Conclusions:
- Network heterogeneity is a key factor in dynamical robustness.
- The findings provide a theoretical framework for understanding biological network resilience.
- This approach can inform the design of robust artificial networks.
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