Advancing Direct Training for Spiking Neural Networks with Circulate-Firing Neurons and Learnable Gradients
Spiking Neural Networks (SNNs) have emerged with promising energy-efficient property, yet a substantial performance gap persists compared to Artificial Neural Networks (ANNs). This gap stems from at least two key limitations: first, conventional spiking neurons offer limited information representation capacity, underutilizing the rich dynamics of membrane potentials; second, fixed surrogate gradient (SG) functions across time steps leads to imprecise gradient propagation, impeding effective direct training. To address these two challenges, we propose a new direct training algorithm with three core innovations: first, a circulate-firing spiking neuron model that enhances information repres...
| Authors | Feifan Zhou, Xiang Wei, Yang Liu et al. |
|---|---|
| Primary category | cs.NE |
| Published | 14 May 2026 |
| Last updated at source | 14 May 2026 |
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