arXiv AI By Yuli Zha, Wang Fan, Yixiao Dai, Lingjie Wang, Bang Hu, Wei Cao, Fengzhe Zhang, Fan Zhang

Robust Representation Learning for Spiking Neural Networks based on Conditional Information Bottleneck

Read the original on arXiv AI →

The Flow has not summarised this story yet — read it at arXiv AI.

arXiv AI
Jul 15

Burst Spiking Neural Networks

arXiv:2607. 11914v1 Announce Type: cross Abstract: A central goal of current Spiking Neural Network (SNN) research is to improve their accuracy toward becoming low-power alternatives to Artificial Neural Networks (ANNs).

By Jiahong Zhang, Sijun Shen, Man Yao, Han Xu, Mingqiang Huang, Yonghong Tian, Bo Xu, Guoqi Li
arXiv AI
Aug 17

SAGE: Surrogate-gradient Adaptation via Attention-Guided Entropy for Spiking Transformers

arXiv:2608. 13702v1 Announce Type: cross Abstract: Spiking neural networks (SNNs) offer an energy-efficient alternative to conventional deep neural networks by exploiting sparse event-driven computation, but their training remains challenging because the non-differentiable spike function requires surrogate gradients whose fixed shape may be suboptimal across layers and training stages.

By Kiran Nair, Rodrigue Rizk, KC Santosh