A2SG:Adaptive and Asymmetric Surrogate Gradients for Training Deep Spiking Neural Networks
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 A2SG. The adaptive gradients adjust an effective window for spatio-temporal adaptation, reducing spatial gradient variation and maintaining directional consistency of gradients over time. The asymmetric gradients reflect neuronal dynamics by assigning larger gradients to neurons with higher membrane potentials, and we prove that they yield lower variation than symmetric surrogates. Our analysis further establishes a direct connection be...
| Authors | Yechan Kang, Yongjin Kweon, Mingyeong Seo et al. |
|---|---|
| Primary category | cs.NE |
| Published | 29 May 2026 |
| Last updated at source | 29 May 2026 |
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