Biological spiking neurons with intrinsic dynamics underlie the powerful representation and learning capabilities of the brain for processing multimodal information in complex environments. Despite recent tremendous progress in spiking neural networks (SNNs) for handling Euclidean-space tasks, it still remains challenging to exploit SNNs in processing non-Euclidean-space data represented by graph data, mainly due to the lack of effective modeling framework and useful training techniques. Here we present a general spike-based modeling framework that enables the direct training of SNNs for graph learning. Through spatial-temporal unfolding for spiking data flows of node features, we incorporate graph convolution filters into spiking dynamics and formalize a synergistic learning paradigm. Considering the unique features of spike representation and spiking dynamics, we propose a spatial-temporal feature normalization (STFN) technique suitable for SNN to accelerate convergence. We instantiate our methods into two spiking graph models, including graph convolution SNNs and graph attention SNNs, and validate their performance on three node-classification benchmarks, including Cora, Citeseer, and Pubmed. Our model can achieve comparable performance with the state-of-the-art graph neural network (GNN) models with much lower computation costs, demonstrating great benefits for the execution on neuromorphic hardware and prompting neuromorphic applications in graphical scenarios.

本研究提出了一个基于神经脉冲的建模框架，使得SNN网络可以直接应用于处理图形数据。通过空时展开的神经脉冲数据流，我们将图形卷积滤波器与神经脉冲动力学相结合，并提出了一种适用于SNN的空时特征归一化技术(STFN)，并将其实例化为两个脉冲图形模型。在三个节点分类基准上验证了我们的模型的性能，包括Cora，Citeseer和Pubmed。实验结果表明，我们的方法与现有的图神经网络(GNN)模型相比具有可比性，但计算成本明显更低，可以在神经形态硬件上得到很好的应用。

在图形学习中利用脉冲动态与空时特征规范化