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Robust Multiobjective Controllability of Complex Neuronal Networks.

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    This study introduces a robust method for identifying key brain network nodes, considering uncertainties. Driver nodes are more critical than control gains for network robustness.

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    Area of Science:

    • Neuroscience
    • Complex Systems Analysis
    • Control Theory

    Background:

    • Neuronal networks are complex systems where identifying critical driver nodes is essential for understanding and control.
    • Uncertainties in node identification and control gain design pose significant challenges in network analysis.
    • Robustness in controlling complex networks is crucial for applications beyond neuroscience.

    Purpose of the Study:

    • To propose a framework for robust multiobjective identification of driver nodes in neuronal networks.
    • To develop and evaluate a novel optimization algorithm for addressing uncertainties in network controllability.
    • To investigate the relative impact of driver nodes versus control gains on network robustness.

    Main Methods:

    • Development of a robust multiobjective controllability framework incorporating interval uncertainties.
    • Introduction of a robust nondominated sorting adaptive differential evolution (NSJaDE) algorithm.
    • Comparative analysis of NSJaDE against statistical methods and other multiobjective evolutionary algorithms (MOEAs) like NSGA-II.

    Main Results:

    • The proposed NSJaDE algorithm demonstrates satisfactory performance in robust multiobjective controllability.
    • Simulation results confirm that uncertainties in driver nodes and control gains significantly impact neuronal network controllability.
    • Driver nodes were found to have a more substantial effect on robust controllability than control gains.

    Conclusions:

    • The NSJaDE algorithm provides an effective approach for robustly identifying driver nodes in complex networks.
    • Understanding the impact of uncertainties is vital for designing effective control strategies in neuronal and other complex systems.
    • The findings offer insights into controlling realistic complex networks, including transportation, power grid, and biological networks.