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.

翻译：脉冲时序依赖可塑性（STDP）为脉冲神经网络（SNNs）提供了基于生物机制的学习规则，但其对精确脉冲时序的依赖以及成对更新的特性限制了权重的快速学习。本文提出监督型脉冲一致性依赖可塑性（SADP）的扩展方法，该方法使用群体一致性度量（如Cohen's kappa系数）替代成对脉冲时序比较。所提出的学习规则保持了严格的突触局部性，具有线性时间复杂度，且无需反向传播、替代梯度或教师强制即可实现高效的监督学习。我们将监督型SADP集成于混合CNN-SNN架构中：卷积编码器提供紧凑的特征表示，这些特征被转换为泊松脉冲序列，用于驱动SNN中基于一致性的学习。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，该方法具有竞争性的性能和快速的收敛速度。进一步分析显示，该算法在宽泛的超参数范围内保持稳定性能，并与器件启发的突触更新动力学兼容。这些结果共同表明，监督型SADP为脉冲神经网络提供了一种可扩展、基于生物机制且与硬件对齐的学习范式。