通过融合多种机制，提升脉冲神经网络的视觉识别性能与能效。
Memory-Augmented Spiking Networks: Synergistic Integration of Complementary Mechanisms for Neuromorphic Vision
- 整合漏电积分-放电神经元、对比学习等五种机制构建增强型脉冲网络。
- 全模型在N-MNIST上达到97.49%准确率，能耗仅1.85μJ，稀疏性达97%。
- 强调架构平衡优于单一优化，适合神经形态计算研究者参考。
脉冲神经网络（SNNs）具备生物合理性与能效优势，但关于记忆增强策略的系统研究仍有限。本文在N-MNIST数据集上对五种模型进行消融实验，集成漏电积分-放电神经元、有监督对比学习（SCL）、霍普菲尔德网络与分层门控循环网络（HGRN）。基线SNN表现出结构化神经元簇，轮廓分数为0.687±0.012。单独增强带来权衡：SCL使准确率提升0.28%，但降低聚类性（轮廓分数0.637±0.015）；而HGRN在准确率上提升1.01%，计算效率提高170.6倍。完整集成实现多指标平衡提升，轮廓分数达0.715±0.008，分类准确率97.49±0.10%，能耗1.85±0.06μJ，稀疏性97.0%。结果表明，最优性能源于架构协同而非单一优化，为记忆增强型神经形态系统提供设计原则。
原文摘要 · Abstract (English)
Spiking Neural Networks (SNNs) provide biological plausibility and energy efficiency, yet systematic investigations of memory augmentation strategies remain limited. We conduct a five-model ablation study integrating Leaky Integrate-and-Fire neurons, Supervised Contrastive Learning (SCL), Hopfield networks, and Hierarchical Gated Recurrent Networks (HGRN) on the N-MNIST dataset. Baseline SNNs exhibit organized neuronal groupings, or structured assemblies, characterized by a silhouette score of $0.687 \pm 0.012$. Individual augmentations introduce trade-offs: SCL improves accuracy by $0.28\%$ but reduces clustering (silhouette score $0.637 \pm 0.015$), while HGRN yields consistent gains in both accuracy ($+1.01\%$) and computational efficiency ($170.6\times$). Full integration achieves a balanced improvement across metrics, reaching a silhouette score of $0.715 \pm 0.008$, classification accuracy of $97.49 \pm 0.10\%$, energy consumption of $1.85 \pm 0.06\,μ\mathrm{J}$, and sparsity of $97.0\%$. These results indicate that optimal performance emerges from architectural balance rather than isolated optimization, establishing design principles for memory-augmented neuromorphic systems.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。