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A quantum-inspired online spiking neural network for time-series predictions

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Abstract

Spiking neural networks (SNNs) are considered the most promising new generation of artificial neural networks, due to their superior dynamic structures and low energy consumption, resembling that of the biological brain. Recent studies have suggested that SNNs could benefit from online learning in dynamic scenarios involving temporal sequences. However, the network performance of traditional spiking encoding methods is significantly affected by noise. As such, this study proposes a quantum-inspired online spiking neural network (QiSNN), which combines a quantum particle swarm optimization algorithm and a Kalman filtering technique to smooth and denoise the original time-series data. Additionally, a novel adaptive threshold selection method is developed to determine the similarity between neurons in a repository. The resulting model is applied to a dataset from the Department of Environment, Food, and Rural Affairs (DEFRA) in the UK, and used to predict ozone and PM10 concentrations that characterize air quality. Experimental results demonstrate that the proposed QiSNN outperforms baseline models across multiple evaluation metrics.

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Funding

This work was supported by the Jilin Provincial Department of Science and Technology, China, under Grant 20210201075GX.

Author information

Authors and Affiliations

  1. School of Computer Science and Technology, Changchun University of Science and Technology, Changchun, 130022, China

    Fei Yan

  2. Department of Computer Science and Technology, Tsinghua University, Beijing, 100084, China

    Fei Yan

  3. School of Computer Science and Technology, Changchun University of Science and Technology, Changchun, 130022, China

    Wenjing Liu

  4. Faculty of Mechanical Engineering & Mechanics, Ningbo University, Ningbo, 315211, China

    Fangyan Dong

  5. School of Computing, Tokyo Institute of Technology, Yokohama, 226-8502, Japan

    Kaoru Hirota

Authors
  1. Fei Yan
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  2. Wenjing Liu
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  3. Fangyan Dong
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  4. Kaoru Hirota
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Contributions

FY Investigation, Methodology, Writing-original draft, Writing-review & editing, Funding acquisition. WL Methodology, Data curation, Software, Visualization, Writing-original draft, Writing-review & editing. FD Formal analysis, Validation, Writing-review & editing. KH Supervision, Writing-review & editing.

Corresponding author

Correspondence to Fangyan Dong.

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Yan, F., Liu, W., Dong, F. et al. A quantum-inspired online spiking neural network for time-series predictions. Nonlinear Dyn 111, 15201–15213 (2023). https://doi.org/10.1007/s11071-023-08655-9

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  • DOI: https://doi.org/10.1007/s11071-023-08655-9

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