In recent years, Recurrent Spiking Neural Networks (RSNNs) have shown promising potential in long-term temporal modeling. Many studies focus on improving neuron models and also integrate recurrent structures, leveraging their synergistic effects to improve the long-term temporal modeling capabilities of Spiking Neural Networks (SNNs). However, these studies often place an excessive emphasis on the role of neurons, overlooking the importance of analyzing neurons and recurrent structures as an integrated framework. In this work, we consider neurons and recurrent structures as an integrated system and conduct a systematic analysis of gradient propagation along the temporal dimension, revealing a challenging gradient vanishing problem. To address this issue, we propose the Skip Recurrent Connection (SRC) as a replacement for the vanilla recurrent structure, effectively mitigating the gradient vanishing problem and enhancing long-term temporal modeling performance. Additionally, we propose the Adaptive Skip Recurrent Connection (ASRC), a method that can learn the skip span of skip recurrent connection in each layer of the network. Experiments show that replacing the vanilla recurrent structure in RSNN with SRC significantly improves the model's performance on temporal benchmark datasets. Moreover, ASRC-SNN outperforms SRC-SNN in terms of temporal modeling capabilities and robustness.

翻译：近年来，循环脉冲神经网络（RSNN）在长期时序建模方面展现出巨大潜力。许多研究致力于改进神经元模型，并同时整合循环结构，利用其协同效应来提升脉冲神经网络（SNN）的长期时序建模能力。然而，这些研究往往过度强调神经元的作用，忽视了将神经元与循环结构作为一个整体框架进行分析的重要性。在本工作中，我们将神经元与循环结构视为一个集成系统，并沿时间维度对梯度传播进行了系统性分析，揭示了一个具有挑战性的梯度消失问题。为解决此问题，我们提出了跳跃循环连接（SRC）以替代基础的循环结构，有效缓解了梯度消失问题，并增强了长期时序建模性能。此外，我们提出了自适应跳跃循环连接（ASRC），该方法能够学习网络中每一层跳跃循环连接的跳跃跨度。实验表明，在RSNN中用SRC替代基础循环结构，能显著提升模型在时序基准数据集上的性能。此外，ASRC-SNN在时序建模能力和鲁棒性方面均优于SRC-SNN。