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Adaptive dendritic plasticity in brain-inspired dynamic neural networks for enhanced multi-timescale feature extraction

文献类型：期刊论文

作者Mao, Jiayi5; Zheng, Hanle5; Yin, Huifeng5; Fan, Hanxiao5; Mei, Lingrui2; Guo, Hao1; Li, Yao1; Wu, Jibin3,4; Pei, Jing5; Deng, Lei5
刊名NEURAL NETWORKS
出版日期2026-02-01
卷号194页码:15
关键词Brain-inspired neural networks Dendritic heterogeneity LIM neurons Adaptive dendritic plasticity Multi-timescale feature extraction
ISSN号0893-6080
DOI10.1016/j.neunet.2025.108191
英文摘要Brain-inspired neural networks, drawing insights from biological neural systems, have emerged as a promising paradigm for temporal information processing due to their inherent neural dynamics. Spiking Neural Networks (SNNs) have gained extensive attention among existing brain-inspired neural models. However, they often struggle with capturing multi-timescale temporal features due to the static parameters across time steps and the low-precision spike activities. To this end, we propose a dynamic SNN with enhanced dendritic heterogeneity to enhance the multi-timescale feature extraction capability. We design a Leaky Integrate Modulation neuron model with Dendritic Heterogeneity (DH-LIM) that replaces traditional spike activities with a continuous modulation mechanism for preserving the nonlinear behaviors while enhancing the feature expression capability. We also introduce an Adaptive Dendritic Plasticity (ADP) mechanism that dynamically adjusts dendritic timing factors based on the frequency domain information of input signals, enabling the model to capture both rapid-and slow-changing temporal patterns. Extensive experiments on multiple datasets with rich temporal features demonstrate that our proposed method achieves excellent performance in processing complex temporal signals. These optimizations provide fresh solutions for optimizing the multi-timescale feature extraction capability of SNNs, showcasing its broad application potential.
资助项目National Natural Science Foundation of China[62276151] ; Tsinghua University Initiative Scientific Research Program, Chinese Institute for Brain Research, Beijing ; Neuracle Medical Technology Co., Ltd.
WOS研究方向Computer Science ; Neurosciences & Neurology
语种英语
WOS记录号WOS:001597588900002
出版者PERGAMON-ELSEVIER SCIENCE LTD
源URL[http://119.78.100.204/handle/2XEOYT63/41640]  
专题中国科学院计算技术研究所期刊论文_英文
通讯作者Li, Yao; Deng, Lei
作者单位1.Taiyuan Univ Technol, Coll Comp Sci & Technol, Jinzhong 030600, Peoples R China
2.Chinese Acad Sci, Inst Comp Technol, Key Lab Ai Safety, Beijing 100190, Peoples R China
3.Hong Kong Polytech Univ, Dept Comp, Hong Kong, Peoples R China
4.Hong Kong Polytech Univ, Dept Data Sci & Artificial Intelligence, Hong Kong, Peoples R China
5.Tsinghua Univ, Ctr Brain Inspired Comp Res, Dept Precis Instrument, Beijing 100084, Peoples R China
推荐引用方式
GB/T 7714		 Mao, Jiayi,Zheng, Hanle,Yin, Huifeng,et al. Adaptive dendritic plasticity in brain-inspired dynamic neural networks for enhanced multi-timescale feature extraction[J]. NEURAL NETWORKS,2026,194:15.
APA Mao, Jiayi.,Zheng, Hanle.,Yin, Huifeng.,Fan, Hanxiao.,Mei, Lingrui.,...&Deng, Lei.(2026).Adaptive dendritic plasticity in brain-inspired dynamic neural networks for enhanced multi-timescale feature extraction.NEURAL NETWORKS,194,15.
MLA Mao, Jiayi,et al."Adaptive dendritic plasticity in brain-inspired dynamic neural networks for enhanced multi-timescale feature extraction".NEURAL NETWORKS 194(2026):15.

入库方式： OAI收割

来源：计算技术研究所

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