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Anti-interference of a small-world spiking neural network against pulse noise

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Abstract

Inspired by the nervous system working mechanism of a biological brain, brain-like intelligence has been a research frontier in the field of artificial intelligence. Under external stimulation, biological brains have self-adaptive advantages. Drawing from the advantage of biological brains, it is meaningful to investigate the anti-interference ability of brain-like models. In this study, we proposed a spiking neural network with small-world topology (SWSNN), where Izhikevich neuron models and synaptic plasticity models with excitatory and inhibitory synapses are introduced to represent nodes and edges of the network, respectively. The anti-interference ability of the SWSNN against pulse noise is investigated, and the anti-interference ability of SNNs with different topologies are compared. The simulation results indicate that: (i) our SWSNN has anti-interference ability against pulse noise, which is supported from different perspectives based on two indices. Furthermore, the chain reaction of firing rates, synaptic weights and topological characteristics forms neural information processing in the SWSNN under pulse noise. In addition, the synaptic weights are significantly relevant to the anti-interference ability, which implies that an intrinsic factor of the anti-interference ability is the dynamic regulation of synaptic plasticity. (ii) Our SWSNN outperforms the random and regular SNNs that are not complex networks in terms of anti-interference performance. For complex network, the anti-interference performance of SWSNN is superior to that of scale-free SNN, and the anti-interference superiority of the SWSNN is more obvious with the increase of amplitude of pulse noise. The topological characteristics of the network are further discussed, and the results imply that the topology is a factor that affects the anti-interference performance of the SWSNN.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China under Grants 52077056, 61976240 and 51977060 and the Natural Science Foundation of Hebei Province under Grant E2020202033.

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Authors and Affiliations

  1. State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology, 300130, Tianjin, China

    Lei Guo, Yihua Song & Guizhi Xu

  2. Hebei Key Laboratory of Bioelectromagnetics and Neural Engineering, School of Health Sciences and Biomedical Engineering, Hebei University of Technology, 300130, Tianjin, China

    Lei Guo, Yihua Song & Guizhi Xu

  3. School of Artificial Intelligence, Hebei University of Technology, 300130, Tianjin, China

    Youxi Wu

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  1. Lei Guo
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Contributions

Conceptualization: Lei Guo; Methodology: Lei Guo; Formal analysis and investigation: Lei Guo, Yihua Song; Writing-original draft preparation: Yihua Song; Writing-review and editing: Youxi Wu; Funding acquisition: Lei Guo, Youxi Wu, Guizhi Xu; Supervision: Guizhi Xu.

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Correspondence to Lei Guo.

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The authors declared that we have independently written programs to construct our network and performed the research and analysis of the anti-interference ability of our network.

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The authors declared that we have independently written programs to construct our network and performed the research and analysis of the anti-interference ability of our network. There are no additional data sources used in this paper.

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Guo, L., Song, Y., Wu, Y. et al. Anti-interference of a small-world spiking neural network against pulse noise. Appl Intell 53, 7074–7092 (2023). https://doi.org/10.1007/s10489-022-03804-w

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