Yongjin Kweon

A$^2$SG: Adaptive and Asymmetric Surrogate Gradients for Training Deep Spiking Neural Network

Training deep spiking neural networks (SNNs) remains challenging due to sharp loss landscapes and temporal inconsistency caused by surrogate gradients. To address these challenges, we propose a unified framework: adaptive and asymmetric surrogate gradients (A$^2$SG). The adaptive gradients adjust an…