SADP让脉冲神经网络更高效，靠神经元放电同步性学习。
Spike Agreement Dependent Plasticity: A scalable Bio-Inspired learning paradigm for Spiking Neural Networks
- 用神经元放电一致性替代精确时间，实现可扩展学习
- 在手写数字数据集上准确率超经典STDP，运行更快
- 适合想实现生物合理又高效计算的类脑系统研究者
我们提出脉冲一致依赖可塑性（SADP），一种面向脉冲神经网络（SNNs）的生物启发式突触学习规则，其依据前、后突触脉冲序列的一致性，而非精确的脉冲对时间。SADP通过使用科恩κ系数等群体级相关性度量，推广了经典脉冲时间依赖可塑性（STDP），将成对时间更新替换为整体相关性判断。该更新规则具有线性时间复杂度，可通过位运算逻辑实现高效硬件部署。在MNIST和Fashion-MNIST上的实证结果显示，尤其是结合基于实验离子有机忆阻器器件数据提取的样条核时，SADP在准确率与运行效率上均优于传统STDP。本框架弥合了生物合理性与计算可扩展性之间的差距，为类脑系统提供了可行的学习机制。
原文摘要 · Abstract (English)
We introduce Spike Agreement Dependent Plasticity (SADP), a biologically inspired synaptic learning rule for Spiking Neural Networks (SNNs) that relies on the agreement between pre- and post-synaptic spike trains rather than precise spike-pair timing. SADP generalizes classical Spike-Timing-Dependent Plasticity (STDP) by replacing pairwise temporal updates with population-level correlation metrics such as Cohen's kappa. The SADP update rule admits linear-time complexity and supports efficient hardware implementation via bitwise logic. Empirical results on MNIST and Fashion-MNIST show that SADP, especially when equipped with spline-based kernels derived from our experimental iontronic organic memtransistor device data, outperforms classical STDP in both accuracy and runtime. Our framework bridges the gap between biological plausibility and computational scalability, offering a viable learning mechanism for neuromorphic systems.
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