On Controllability of Neuronal Networks With Constraints on the Average of Control Gains

Control gains play an important role in the control of a natural or a technical system since they reflect how much resource is required to optimize a certain control objective. This paper is concerned with the controllability of neuronal networks with constraints on the average value of the control...

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Bibliographic Details
Published in:IEEE transactions on cybernetics Vol. 44; no. 12; pp. 2670 - 2681
Main Authors: Yang Tang, Zidong Wang, Huijun Gao, Hong Qiao, Kurths, Jurgen
Format: Journal Article
Language:English
Published: United States IEEE 01.12.2014
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Biological neural networks
Collision avoidance systems
Complex networks
Constraints
Control systems
Controllability
Dominance
Educational institutions
Evolution
evolutionary algorithms
Gain
multiagent systems
Networks
neural networks
Optimization
Real time
Statistical methods
synchronization/consensus
synchronization/consensus
controllability
Complex networks
evolutionary algorithms
neural networks
multiagent systems
ISSN:2168-2267, 2168-2275, 2168-2275
Online Access:Get full text
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Summary:Control gains play an important role in the control of a natural or a technical system since they reflect how much resource is required to optimize a certain control objective. This paper is concerned with the controllability of neuronal networks with constraints on the average value of the control gains injected in driver nodes, which are in accordance with engineering and biological backgrounds. In order to deal with the constraints on control gains, the controllability problem is transformed into a constrained optimization problem (COP). The introduction of the constraints on the control gains unavoidably leads to substantial difficulty in finding feasible as well as refining solutions. As such, a modified dynamic hybrid framework (MDyHF) is developed to solve this COP, based on an adaptive differential evolution and the concept of Pareto dominance. By comparing with statistical methods and several recently reported constrained optimization evolutionary algorithms (COEAs), we show that our proposed MDyHF is competitive and promising in studying the controllability of neuronal networks. Based on the MDyHF, we proceed to show the controlling regions under different levels of constraints. It is revealed that we should allocate the control gains economically when strong constraints are considered. In addition, it is found that as the constraints become more restrictive, the driver nodes are more likely to be selected from the nodes with a large degree. The results and methods presented in this paper will provide useful insights into developing new techniques to control a realistic complex network efficiently.
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ISSN:2168-2267
2168-2275
2168-2275
DOI:10.1109/TCYB.2014.2313154