Spiking neural networks (SNNs) are investigated as biologically inspired models of neural computation, distinguished by their computational capability and energy efficiency due to precise spiking times and sparse spikes with event-driven computation. A significant question is how SNNs can emulate human-like graph-based reasoning of concepts and relations, especially leveraging the temporal domain optimally. This paper reveals that SNNs, when amalgamated with synaptic delay and temporal coding, are proficient in executing (knowledge) graph reasoning. It is elucidated that spiking time can function as an additional dimension to encode relation properties via a neural-generalized path formulation. Empirical results highlight the efficacy of temporal delay in relation processing and showcase exemplary performance in diverse graph reasoning tasks. The spiking model is theoretically estimated to achieve $20\times$ energy savings compared to non-spiking counterparts, deepening insights into the capabilities and potential of biologically inspired SNNs for efficient reasoning. The code is available at https://github.com/pkuxmq/GRSNN.

翻译：脉冲神经网络（SNNs）作为受生物学启发的神经计算模型被广泛研究，其特点在于精确的脉冲发放时间、稀疏的脉冲事件驱动计算所带来的计算能力和能效优势。一个重要的问题是SNNs如何模拟人类基于图的、对概念和关系的推理过程，特别是如何最优地利用时序域。本文揭示了SNNs在与突触延迟和时序编码结合后，能够高效地执行（知识）图推理。研究阐明，脉冲发放时间可以作为通过神经广义路径公式来编码关系属性的额外维度。实证结果突显了时序延迟在处理关系上的有效性，并在多种图推理任务中展示了卓越的性能。理论估计表明，该脉冲模型相较于非脉冲模型可实现$20\times$的节能，这加深了我们对受生物学启发的SNNs在高效推理方面的能力与潜力的理解。代码发布于 https://github.com/pkuxmq/GRSNN。