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FPGA-based small-world spiking neural network with anti-interference ability under external noise

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Abstract

Neuromorphic hardware has become hotspot in the field of brain-like computing due to its advantages. However, the presence of external noise imposes challenges with respect to maintaining normal function of neuromorphic hardware. Biological brains have self-adaptability to external noise, meaning that a brain-like hardware with bio-plausibility can be expected to improve robustness. The purpose of this paper is to implement a highly fitted brain-like hardware with anti-interference ability (AIA) while preserving bio-plausibility. We propose a method of implementing a small-world spiking neural network (SWSNN) with bio-plausibility based on a field-programmable gate array (FPGA), in which the nodes are Izhikevich neuron modules, the edges are synaptic plasticity modules, and the topology is a small-world network. Then, the AIAs of the FPGA-based SNNs with different external noises are evaluated by two anti-interference indices. Further, taking a speech recognition task as the case study, the AIAs of these FPGA-based SNNs are verified in application. Finally, the AIA mechanism of the FPGA-based SNNs is discussed. Our results demonstrate that: (i) In the FPGA-based SWSNN, the FPGA-based Izhikevich neuron modules and the synaptic plasticity modules highly fit to the corresponding simulation results, and the topology conforms to the small-world property of human functional brain networks. (ii) Based on two anti-interference indices, the FPGA-based SWSNN outperforms the FPGA-based SNNs with other topologies, which is further verified by the speech recognition accuracy. (iii) Our discussions hint that the synaptic plasticity is intrinsic factor of the AIA, and the topology is a factor affecting the AIA.

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Data availability

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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Funding

This work was supported by the National Natural Science Foundation of China (Grant Nos. 52077056 and 61976240) and the National Key Research and Development Program of China (Grant No. 2022YFC2402203).

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Authors and Affiliations

  1. Hebei Key Laboratory of Bioelectromagnetics and Neuroengineering, School of Health Sciences and Biomedical Engineering, Hebei University of Technology, Tianjin, 300131, China

    Lei Guo, Yongkang Liu & Guizhi Xu

  2. State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology, Tianjin, 300131, China

    Lei Guo, Yongkang Liu & Guizhi Xu

  3. School of Artificial Intelligence, Hebei University of Technology, Tianjin, 300401, China

    Youxi Wu

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  1. Lei Guo
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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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Guo, L., Liu, Y., Wu, Y. et al. FPGA-based small-world spiking neural network with anti-interference ability under external noise. Neural Comput & Applic 36, 12505–12527 (2024). https://doi.org/10.1007/s00521-024-09667-1

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