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

arXiv 2608.17394 ↗First posted 18 Aug 2026cs.CVcited by 0

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Abstract (arXiv)

Spiking neural networks (SNNs), characterized by bio-inspired neuronal dynamics and event-driven communication, have attained significant progress in recent years. Nevertheless, training deep SNNs remains challenging due to spatiotemporal information loss and gradient mismatching. To simultaneously address these issues, we propose a noisy group neuron (NGN) model, which incorporates population-level synchronous resetting and neural stochasticity as fundamental computational mechanisms. We then develop the NGN method as a framework that combines the NGN model with backpropagation learning based on mean-field dynamics. We demonstrate the advantages of the NGN method through theoretical analysis and experimental validation on CIFAR-10, CIFAR-100, Tiny-ImageNet, DVS-Gesture, N-Caltech101, and CIFAR10-DVS. The proposed approach achieves an accuracy of 87.35% on CIFAR10-DVS within 10 inference time steps. These results support NGN as a practical approach to high-performance neuromorphic computing.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 20 of its 56 references that are in the map. First posted on arXiv 18 Aug 2026.

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References · 20

  1. 2017Spatio-Temporal Backpropagation for Training High-performance Spiking Neural Networkscited by 406
  2. 2020Incorporating Learnable Membrane Time Constant to Enhance Learning of Spiking Neural Networkscited by 345
  3. 2015Converting Static Image Datasets to Spiking Neuromorphic Datasets Using Saccadescited by 314
  4. 2020Going Deeper With Directly-Trained Larger Spiking Neural Networkscited by 278
  5. 2018Mean Field Multi-Agent Reinforcement Learningcited by 207
  6. 2022Temporal Efficient Training of Spiking Neural Network via Gradient Re-weightingcited by 198
  7. 2020Temporal Spike Sequence Learning via Backpropagation for Deep Spiking Neural Networkscited by 100
  8. 2021Efficient Neuromorphic Signal Processing with Loihi 2cited by 96
  9. 2022Training High-Performance Low-Latency Spiking Neural Networks by Differentiation on Spike Representationcited by 89
  10. 2022Neuromorphic Data Augmentation for Training Spiking Neural Networkscited by 52
  11. 2023Parallel Spiking Neurons with High Efficiency and Ability to Learn Long-term Dependenciescited by 41
  12. 2023Ternary Spike: Learning Ternary Spikes for Spiking Neural Networkscited by 38
8 more
  1. 2022Multi-Level Firing with Spiking DS-ResNet: Enabling Better and Deeper Directly-Trained Spiking Neural Networkscited by 21
  2. 2024CLIF: Complementary Leaky Integrate-and-Fire Neuron for Spiking Neural Networkscited by 21
  3. 2024Shrinking Your TimeStep: Towards Low-Latency Neuromorphic Object Recognition with Spiking Neural Networkcited by 19
  4. 2023Exploiting Noise as a Resource for Computation and Learning in Spiking Neural Networkscited by 18
  5. 2023Temporal Knowledge Sharing enable Spiking Neural Network Learning from Past and Futurecited by 10
  6. 2023Multi-compartment Neuron and Population Encoding Powered Spiking Neural Network for Deep Distributional Reinforcement Learningcited by 3
  7. 2024Optimal ANN-SNN Conversion with Group Neuronscited by 2
  8. 2025Multiplication-Free Parallelizable Spiking Neurons with Efficient Spatio-Temporal Dynamicscited by 1

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