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Noisy group neurons with synchronous resetting for high-performance spiking neural networks

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The paper introduces a noisy group neuron (NGN) model that uses synchronous resetting and neural stochasticity to improve spiking neural networks (SNNs). It combines this model with a mean‑field backpropagation framework and demonstrates superior performance on several benchmark datasets, achieving 87.35% accuracy on CIFAR10‑DVS in just 10 inference steps.

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arXiv Machine Learning
Jun 11

A2SG:Adaptive and Asymmetric Surrogate Gradients for Training Deep Spiking Neural Networks

arXiv:2606. 11236v1 Announce Type: cross Abstract: Training deep spiking neural networks (SNNs) remains challenging due to sharp loss landscapes and temporal inconsistency caused by surrogate gradients.

By Yechan Kang, Yongjin Kweon, Mingyeong Seo, Sohee Park, Yeonguk Jeon, Jongkil Park, Hyun Jae Jang, Jaewook Kim, YeonJoo Jeong, Suyoun Lee, Seongsik Park
arXiv Machine Learning
3d ago

Exploring napping paradigm for Recurrent Spiking Neural Networks

The paper proposes a biologically inspired micro‑sleep technique called napping for recurrent spiking neural networks, combining proportional weight scaling with continuous stochastic membrane activity. Experiments on an unsupervised SNN trained with trace‑based STDP on Gabor‑preprocessed MNIST show that well‑tuned napping can match the classification accuracy of conventional weight normalization while offering different clustering characteristics. The study suggests that napping may be preferable when representational structure is more important than raw classification speed, despite its higher simulation cost.

By Andreas Massey, Stefano Nichele, Aliaksandr Hubin