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Antiinterference Function of Scale-Free Spiking Neural Network Under AC Magnetic Field Stimulation

机译:AC磁场刺激下无规模尖钉神经网络的抗议功能

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摘要

The complexity and changeability of electromagnetic environment make the deficiency of traditional methods of electromagnetic protection increasingly prominent. The organisms with the regulation of nervous system have advantages of self-adaptive, self-organizing, and self-repairing. Therefore, it is necessary to explore a new thought on electromagnetic protection by drawing from the biological self-adaptive advantage. In this study, two kinds of scale-free spiking neural networks (sfSNNs) with different clustering coefficients are constructed. Then, the antiinterference function of the sfSNNs under the ac magnetic field stimulation is evaluated and compared. Finally, the antiinterference mechanism is analyzed. The experimental results show that both sfSNNs have a certain antiinterference function, and the performance of the sfSNN with high clustering coefficient is better than that with low clustering coefficient in the antiinterference function; the dynamic evolution of neural information processing in the sfSNN is clarified; and the dynamic regulation of synaptic plasticity is the intrinsic factor of the antiinterference function of the sfSNNs. This study lays a theoretical foundation for the electromagnetic protection of electronic system based on adaptive bionic mechanism.
机译:电磁环境的复杂性和可变性使得传统电磁保护方法的缺陷日益突出。具有神经系统调节的生物具有自适应,自适应和自我修复的优点。因此,需要通过从生物自适应优势中绘制来探索关于电磁保护的新思路。在本研究中,构造了具有不同聚类系数的两种无尺寸的尖峰神经网络(SFSNNS)。然后,评估SFSNNS在交流磁场刺激下的抗干扰功能进行评估并进行比较。最后,分析了抗干扰机制。实验结果表明,两个SFSNNS都具有一定的抗干扰功能,并且具有高聚类系数的SFSNN的性能优于抗干扰功能中具有低聚类系数的效果;澄清了SFSNN中神经信息处理的动态演变;突触塑性的动态调节是SFSNNS的抗干扰功能的内在因子。本研究为基于自适应仿生机制的电子系统电磁保护的理论基础。

著录项

  • 来源
    《IEEE Transactions on Magnetics》 |2021年第2期|1-5|共5页
  • 作者单位

    State Key Laboratory of Reliability and Intelligence of Electrical Equipment Hebei University of Technology Tianjin China;

    State Key Laboratory of Reliability and Intelligence of Electrical Equipment Hebei University of Technology Tianjin China;

    School of Artificial Intelligence Hebei University of Technology Tianjin China;

    State Key Laboratory of Reliability and Intelligence of Electrical Equipment Hebei University of Technology Tianjin China;

    State Key Laboratory of Reliability and Intelligence of Electrical Equipment Hebei University of Technology Tianjin China;

  • 收录信息
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Firing; Magnetic fields; Electromagnetics; Synapses; Neural networks;

    机译:射击;磁场;电磁学;突触;神经网络;
  • 入库时间 2022-08-18 23:00:34

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