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神经网络可控性与可观测性的分析及应用

Analysis and application of neuronal network controllability and observability.

作者信息

Su Fei, Wang Jiang, Li Huiyan, Deng Bin, Yu Haitao, Liu Chen

机构信息

School of Electrical Engineering and Automation, Tianjin University, Tianjin 300072, China.

School of Automation and Electrical Engineering, Tianjin University of Technology and Education, Tianjin 300222, China.

出版信息

Chaos. 2017 Feb;27(2):023103. doi: 10.1063/1.4975124.

Abstract

Controllability and observability analyses are important prerequisite for designing suitable neural control strategy, which can help lower the efforts required to control and observe the system dynamics. First, 3-neuron motifs including the excitatory motif, the inhibitory motif, and the mixed motif are constructed to investigate the effects of single neuron and synaptic dynamics on network controllability (observability). Simulation results demonstrate that for networks with the same topological structure, the controllability (observability) of the node always changes if the properties of neurons and synaptic coupling strengths vary. Besides, the inhibitory networks are more controllable (observable) than the excitatory networks when the coupling strengths are the same. Then, the numerically determined controllability results of 3-neuron excitatory motifs are generalized to the desynchronization control of the modular motif network. The control energy and neuronal synchrony measure indexes are used to quantify the controllability of each node in the modular network. The best driver node obtained in this way is the same as the deduced one from motif analysis.

摘要

可控性和可观测性分析是设计合适的神经控制策略的重要前提,这有助于降低控制系统动态所需的努力。首先,构建包括兴奋性基序、抑制性基序和混合基序在内的三神经元基序,以研究单个神经元和突触动态对网络可控性(可观测性)的影响。仿真结果表明,对于具有相同拓扑结构的网络,如果神经元特性和突触耦合强度发生变化,节点的可控性(可观测性)总是会改变。此外,当耦合强度相同时,抑制性网络比兴奋性网络更具可控性(可观测性)。然后,将三神经元兴奋性基序的数值确定的可控性结果推广到模块化基序网络的去同步控制。控制能量和神经元同步测量指标用于量化模块化网络中每个节点的可控性。通过这种方式获得的最佳驱动节点与从基序分析推导出来的节点相同。

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