3 papers
cs.AI2024
Unveiling the Potential of Spiking Dynamics in Graph Representation Learning through Spatial-Temporal Normalization and Coding Strategies
Mingkun Xu, Huifeng Yin, Yujie Wu +5
In recent years, spiking neural networks (SNNs) have attracted substantial interest due to their potential to replicate the energy-efficient and event-driven processing of biologic…
cs.AI2024
Enhancing Graph Representation Learning with Attention-Driven Spiking Neural Networks
Huifeng Yin, Mingkun Xu, Jing Pei +1
Graph representation learning has become a crucial task in machine learning and data mining due to its potential for modeling complex structures such as social networks, chemical c…
cs.NE2024
Understanding the Functional Roles of Modelling Components in Spiking Neural Networks
Huifeng Yin, Hanle Zheng, Jiayi Mao +6
Spiking neural networks (SNNs), inspired by the neural circuits of the brain, are promising in achieving high computational efficiency with biological fidelity. Nevertheless, it is…