Exploiting Spiking Dynamics with Spatial-temporal Feature Normalization in Graph Learning

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.

Authors

• Mingkun Xu Guangdong Institute of Intelligence Science and Technology Center for Brain Inspired Computing Research (CBICR), Department of Precision Instrument, Tsinghua University
• YuJie Wu Central South University
• Lei Deng
• Faqiang Liu
• Guoqi Li Institute of Automation, Key Laboratory of Brain Cognition and Brain-inspired Intelligence Technology, Chinese Academy of Sciences School of Artificial Intelligence, University of Chinese Academy of Sciences
• Jing Pei

Keywords

• Humans and AI: Cognitive Modeling
• Humans and AI: Cognitive Systems
• Machine Learning: Learning Graphical Models