Spiking Neural Networks (SNNs) emulate the integrated-fire-leak mechanism found in biological neurons, offering a compelling combination of biological realism and energy efficiency. In recent years, they have gained considerable research interest. However, existing SNNs predominantly rely on the Leaky Integrate-and-Fire (LIF) model and are primarily suited for simple, static tasks. They lack the ability to effectively model long-term temporal dependencies and facilitate spatial information interaction, which is crucial for tackling complex, dynamic spatio-temporal prediction tasks. To tackle these challenges, this paper draws inspiration from the concept of autaptic synapses in biology and proposes a novel Spatio-Temporal Circuit (STC) model. The STC model integrates two learnable adaptive pathways, enhancing the spiking neurons' temporal memory and spatial coordination. We conduct a theoretical analysis of the dynamic parameters in the STC model, highlighting their contribution in establishing long-term memory and mitigating the issue of gradient vanishing. Through extensive experiments on multiple spatio-temporal prediction datasets, we demonstrate that our model outperforms other adaptive models. Furthermore, our model is compatible with existing spiking neuron models, thereby augmenting their dynamic representations. In essence, our work enriches the specificity and topological complexity of SNNs.

翻译：脉冲神经网络（SNNs）模拟了生物神经元中整合-发放-泄漏的机制，兼具生物真实性与高能效的优点，近年来引起了广泛的研究兴趣。然而，现有的SNN主要基于泄漏积分发放（LIF）模型，且多适用于简单的静态任务。它们缺乏有效建模长期时间依赖关系及促进空间信息交互的能力，而这对于处理复杂的动态时空预测任务至关重要。为应对这些挑战，本文从生物学中的自突触概念汲取灵感，提出了一种新颖的时空回路（STC）模型。STC模型整合了两个可学习的自适应通路，增强了脉冲神经元的时间记忆与空间协调能力。我们对STC模型中的动态参数进行了理论分析，阐明了其在建立长期记忆和缓解梯度消失问题方面的作用。通过在多个时空预测数据集上的大量实验，我们证明了所提模型优于其他自适应模型。此外，该模型与现有的脉冲神经元模型兼容，从而增强了它们的动态表征能力。本质上，我们的工作丰富了SNN的特异性与拓扑复杂性。