Spiking Neural Networks (SNNs) present a more energy-efficient alternative to Artificial Neural Networks (ANNs) by harnessing spatio-temporal dynamics and event-driven spikes. Effective utilization of temporal information is crucial for SNNs, leading to the exploration of attention mechanisms to enhance this capability. Conventional attention operations either apply identical operation or employ non-identical operations across target dimensions. We identify that these approaches provide distinct perspectives on temporal information. To leverage the strengths of both operations, we propose a novel Dual Temporal-channel-wise Attention (DTA) mechanism that integrates both identical/non-identical attention strategies. To the best of our knowledge, this is the first attempt to concentrate on both the correlation and dependency of temporal-channel using both identical and non-identical attention operations. Experimental results demonstrate that the DTA mechanism achieves state-of-the-art performance on both static datasets (CIFAR10, CIFAR100, ImageNet-1k) and dynamic dataset (CIFAR10-DVS), elevating spike representation and capturing complex temporal-channel relationship. We open-source our code: https://github.com/MnJnKIM/DTA-SNN.

本研究针对脉冲神经网络(SNNs)在利用时间信息时所面临的挑战，提出了一种新颖的双时域通道注意力机制(DTA)。该方法结合了同质和异质注意力策略，首次聚焦于时间通道的相关性和依赖性。实验结果表明，DTA机制在静态和动态数据集上均达到了最先进的性能，显著提升了脉冲表示和复杂时间通道关系的捕获能力。

双时域通道注意力机制用于脉冲神经网络