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| High-Time-Step-Friendly Slice Parallel Spiking Neuron |
| LAI Jianxiang1, HUANG Fangwan1,2,3, WU Yuezhong1,2,3, YU Zhiyong1,2,3 |
1. College of Computer and Data Science, Fuzhou University, Fuzhou 350108;
2. Engineering Research Center of Big Data Intelligence of Mi-nistry of Education, Fuzhou University, Fuzhou 350108;
3. Fujian Key Laboratory of Network Computing and Intelligent Information Processing, Fuzhou University, Fuzhou 350108 |
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Abstract Spiking neural networks are faced with challenges in time series prediction, including the difficulty of balancing computational efficiency and information processing capability in temporal mapping, insufficient scalability of parallel spiking neurons at high time steps, and a tendency to fall into local optimum. To address these issues, a high-time-step-friendly slice parallel spiking neuron(HSPSN) is proposed in this paper. First, a direct temporal mapping method is employed to achieve one-to-one matching of time steps. Then, a slice parallel mechanism is designed to integrate local and global temporal patterns at the neuronal level through the synergy of local and global slices. Finally, a constriction matrix random dropout strategy is adopted to effectively guide the neurons toward superior convergence. Experiments on seven real-world time series prediction datasets demonstrate that HSPSN significantly outperforms existing spiking neural networks in terms of prediction accuracy, energy efficiency and convergence stability and it can effectively capture complex spatiotemporal dependencies in multivariate time series and covariate-based time series.
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Received: 02 December 2025
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| Fund:National Natural Science Foundation of China(No.U25A20436, 62332014), Fujian Provincial Special Fund for Promoting High-Quality Development of Marine and Fishery Industry(No.FJHYF-ZH-2023-02), Fuzhou-Xiamen-Quanzhou National Independent Innovation Demonstration Zone Collaborative Innovation Platform(No.2022FX5) |
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Corresponding Authors:
HUANG Fangwan, Ph.D., lecturer. Her research inte-rests include computational intelligence.
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About author:: LAI Jianxiang, Master student. His research interests include spiking neural networks.
WU Yuezhong, Ph.D., assistant profe-ssor. His research interests include sensor network systems.
YU Zhiyong, Ph.D., professor. His research interests include pervasive computing. |
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2026, Vol. 39 
