Scholarlink

<p>Spiking Neural Networks, known for their event-driven and energy-efficient characteristics, offer promising potential in temporal modeling. However, existing SNN-based models often struggle to capture long-range dependencies and inter-channel interactions, limiting their performance in complex time series prediction. To address these challenges, we propose the Spiking Depthwise Separable Temporal Convolutional Network (SDSTCN), which replaces conventional convolutions with depthwise separable convolutions. This decoupling of temporal and channel-wise computations enables fine-grained feature extraction while significantly reducing parameter overhead and redundant updates in spike propagation. To further enhance representational capacity, we design the Spiking Global–Local Attention (SGLA) module—an attention framework that models global inter-channel dependencies and local spatio-temporal patterns in parallel. Experimental results demonstrate that the proposed method improves prediction accuracy while maintaining low computational complexity across multiple public benchmark datasets.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Spike-TCN with Depthwise-Separable Convolution and Attention for Efficient Time Series Prediction

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年11月27日

查看全文
  • Yongping Dan,
  • Mengzhao Zhang,
  • Zhaoyuan Wang,
  • Changhao Sun

摘要

Spiking Neural Networks, known for their event-driven and energy-efficient characteristics, offer promising potential in temporal modeling. However, existing SNN-based models often struggle to capture long-range dependencies and inter-channel interactions, limiting their performance in complex time series prediction. To address these challenges, we propose the Spiking Depthwise Separable Temporal Convolutional Network (SDSTCN), which replaces conventional convolutions with depthwise separable convolutions. This decoupling of temporal and channel-wise computations enables fine-grained feature extraction while significantly reducing parameter overhead and redundant updates in spike propagation. To further enhance representational capacity, we design the Spiking Global–Local Attention (SGLA) module—an attention framework that models global inter-channel dependencies and local spatio-temporal patterns in parallel. Experimental results demonstrate that the proposed method improves prediction accuracy while maintaining low computational complexity across multiple public benchmark datasets.