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Stability Switching and Oscillation Regulation Strategies for Large-Scale Fractional-Order Neural Networks With
IEEE Transactions on Cybernetics
|May 27, 2026
Summary
A novel hub-targeted control strategy effectively suppresses abnormal brain oscillations in complex neural networks (NNs) by targeting a single hub. This approach simplifies control and enhances network stability against disruptions.
Area of Science:
- Neuroscience
- Control Theory
- Complex Systems
Background:
- Abnormal neuronal oscillations are implicated in brain disorders.
- Regulating these oscillations is complex due to neural network (NN) dynamics.
- Existing control methods often require extensive data and are complex to implement.
Purpose of the Study:
- To propose a low-dimensional, hub-targeted state-feedback control strategy.
- To suppress Hopf bifurcation in fractional-order dual-hub coupled NNs with time delays.
- To reduce implementation complexity and sensing overhead compared to full-dimensional controllers.
Main Methods:
- Derivation of the characteristic equation using the Coates flow graph method.
- Establishment of delay-dependent bifurcation criteria using fractional stability theory and Hopf bifurcation theorem.
- Numerical simulations to validate theoretical predictions.
Main Results:
- A single-hub targeted controller effectively postpones oscillation onset and improves robustness.
- Bifurcation threshold sensitivity to fractional-order variations and network scale effects on oscillation frequency.
- Interhub connectivity identified as a key trigger for oscillations; hub failure enhances stability.
Conclusions:
- The proposed hub-targeted control is effective and practical for regulating NN dynamics.
- Fractional-order and network topology significantly influence oscillation onset and stability.
- Understanding these factors is crucial for developing therapeutic strategies for brain disorders.
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