用群体一致性替代精确放电时间，实现快速局部学习
Supervised Spike Agreement Dependent Plasticity for Fast Local Learning in Spiking Neural Networks
- 以群体一致性度量取代成对放电时序比较
- 在多个数据集上实现快速收敛且性能媲美传统方法
- 适合硬件部署的生物启发式学习，无需反向传播
脉冲时序依赖可塑性（STDP）为脉冲神经网络（SNNs）提供了生物合理的学习规则，但其依赖精确放电时序和成对更新，限制了权重的快速学习。我们提出一种监督式扩展的脉冲一致性依赖可塑性（Supervised SADP），将成对放电时序比较替换为群体层面的一致性度量（如Cohen's kappa）。该学习规则保持严格突触局部性，具有线性时间复杂度，支持无需反向传播、代理梯度或教师强迫的高效监督学习。我们将监督式SADP集成于混合CNN-SNN架构中，其中卷积编码器提供紧凑特征表示，并转化为泊松脉冲序列，用于SNN中的共识驱动学习。在MNIST、Fashion-MNIST、CIFAR-10及生物医学图像分类任务上的大量实验表明，该方法具备竞争力的性能和快速收敛能力。额外分析显示其在广泛超参数范围内表现稳定，并兼容器件级突触更新动力学。这些结果共同确立了监督式SADP作为可扩展、生物合理且硬件对齐的SNN学习范式。
原文摘要 · Abstract (English)
Spike-Timing-Dependent Plasticity (STDP) provides a biologically grounded learning rule for spiking neural networks (SNNs), but its reliance on precise spike timing and pairwise updates limits fast learning of weights. We introduce a supervised extension of Spike Agreement-Dependent Plasticity (SADP), which replaces pairwise spike-timing comparisons with population-level agreement metrics such as Cohen's kappa. The proposed learning rule preserves strict synaptic locality, admits linear-time complexity, and enables efficient supervised learning without backpropagation, surrogate gradients, or teacher forcing. We integrate supervised SADP within hybrid CNN-SNN architectures, where convolutional encoders provide compact feature representations that are converted into Poisson spike trains for agreement-driven learning in the SNN. Extensive experiments on MNIST, Fashion-MNIST, CIFAR-10, and biomedical image classification tasks demonstrate competitive performance and fast convergence. Additional analyses show stable performance across broad hyperparameter ranges and compatibility with device-inspired synaptic update dynamics. Together, these results establish supervised SADP as a scalable, biologically grounded, and hardware-aligned learning paradigm for spiking neural networks.
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。