4 papers
MPD-SGR: Robust Spiking Neural Networks with Membrane Potential Distribution-Driven Surrogate Gradient Regularization
Runhao Jiang, Chengzhi Jiang, Rui Yan +1
The surrogate gradient (SG) method has shown significant promise in enhancing the performance of deep spiking neural networks (SNNs), but it also introduces vulnerabilities to adve…
DS-ATGO: Dual-Stage Synergistic Learning via Forward Adaptive Threshold and Backward Gradient Optimization for Spiking Neural Networks
Jiaqiang Jiang, Wenfeng Xu, Jing Fan +1
Brain-inspired spiking neural networks (SNNs) are recognized as a promising avenue for achieving efficient, low-energy neuromorphic computing. Direct training of SNNs typically rel…
Adaptive Gradient Learning for Spiking Neural Networks by Exploiting Membrane Potential Dynamics
Jiaqiang Jiang, Lei Wang, Runhao Jiang +2
Brain-inspired spiking neural networks (SNNs) are recognized as a promising avenue for achieving efficient, low-energy neuromorphic computing. Recent advancements have focused on d…
ASRC-SNN: Adaptive Skip Recurrent Connection Spiking Neural Network
Shang Xu, Jiayu Zhang, Ziming Wang +3
In recent years, Recurrent Spiking Neural Networks (RSNNs) have shown promising potential in long-term temporal modeling. Many studies focus on improving neuron models and also int…