用脉冲时序可塑性实现无需反向传播的图像识别，一次训练就达40%准确率。
Supervised Learning without Backpropagation using Spike-Timing-Dependent Plasticity for Image Recognition
- 以脉冲时序可塑性替代反向传播，在监督框架下训练脉冲神经网络。
- 仅用10个样本训练即达40%准确率，样本增多后最高提升至87%。
- 隐藏层仅10个神经元就实现89%准确率，适合低功耗实时识别场景。
本文提出一种新型的脉冲神经网络监督学习方法，不依赖传统反向传播。该方法在监督框架中利用脉冲时序可塑性（STDP）进行图像识别任务。在MNIST数据集上的实验表明，模型仅需10次训练刺激（每类仅暴露一次，即一次学习）即可达到约40%的学习准确率；随着训练样本增加，准确率最高可达87%，且歧义极低。值得注意的是，当仅使用10个隐层神经元时，模型在约10%的歧义水平下仍能获得89%的准确率。该方法为传统反向传播监督学习提供了高效且鲁棒的替代方案。
原文摘要 · Abstract (English)
This study introduces a novel supervised learning approach for spiking neural networks that does not rely on traditional backpropagation. Instead, it employs spike-timing-dependent plasticity (STDP) within a supervised framework for image recognition tasks. The effectiveness of this method is demonstrated using the MNIST dataset. The model achieves approximately 40\% learning accuracy with just 10 training stimuli, where each category is exposed to the model only once during training (one-shot learning). With larger training samples, the accuracy increases up to 87\%, maintaining negligible ambiguity. Notably, with only 10 hidden neurons, the model reaches 89\% accuracy with around 10\% ambiguity. This proposed method offers a robust and efficient alternative to traditional backpropagation-based supervised learning techniques.
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