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Controllability Analysis of the Neural Mass Model with Dynamic Parameters
Xian Liu1, Jing Gao2, Guan Wang3
1Key Lab of Industrial Computer Control Engineering of Hebei Province, Institute of Electrical Engineering, Yanshan University, Qinhuangdao 066004, P.R.C. liuxian@ysu.edu.cn.
Neural Computation
|December 29, 2016
Summary
Understanding brain control is key for treating neuropsychiatric disorders. This study reveals specific dynamical parameter ranges that optimize brain controllability for potential therapeutic applications.
Area of Science:
- Neuroscience
- Control Theory
- Computational Psychiatry
Background:
- Brain control technology is crucial for addressing neuropsychiatric disorders and enhancing mental health.
- Feasibility of brain control necessitates a thorough controllability analysis.
Purpose of the Study:
- To investigate how dynamical parameters influence controllability within a neural mass model.
- To identify optimal parameter ranges for improved brain controllability.
Main Methods:
- Utilized controllability indices as quantitative measures.
- Employed computation of Lie brackets and condition numbers for analysis.
- Examined the impact of key dynamical parameters on model controllability.
Main Results:
- Demonstrated that controllability varies significantly with specific dynamical parameters.
- Identified distinct ranges of parameters associated with enhanced controllability.
- Provided quantitative insights into parameter-dependent brain controllability.
Conclusions:
- Dynamical parameters play a critical role in determining brain controllability.
- Specific parameter ranges offer improved controllability, suggesting potential therapeutic targets.
- Findings may inform the development of novel therapies for brain nervous disorders.
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