Spiking Neural Networks (SNNs) are considered as the third generation of artificial neural networks, which are more closely with information processing in biological brains. However, it is still a challenge for how to train the non-differential SNN efficiently and robustly with the form of spikes. Here we give an alternative method to train SNNs by biologically-plausible structural and functional inspirations from the brain. Firstly, inspired by the significant top-down structural connections, a global random feedback alignment is designed to help the SNN propagate the error target from the output layer directly to the previous few layers. Then inspired by the local plasticity of the biological system in which the synapses are more tuned by the neighborhood neurons, a differential STDP is used to optimize local plasticity. Extensive experimental results on the benchmark MNIST (98.62%) and Fashion MNIST (89.05%) have shown that the proposed algorithm performs favorably against several state-of-the-art SNNs trained with backpropagation.

中文翻译：

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GLSNN：基于全局反馈对齐和局部 STDP 可塑性的多层尖峰神经网络

尖峰神经网络（SNN）被认为是第三代人工神经网络，它与生物大脑中的信息处理更加密切。然而，如何以尖峰的形式高效、鲁棒地训练非差分 SNN 仍然是一个挑战。在这里，我们提供了另一种方法，通过来自大脑的生物学上合理的结构和功能灵感来训练 SNN。首先，受重要的自上而下结构连接的启发，设计了全局随机反馈对齐来帮助 SNN 将误差目标从输出层直接传播到前面的几层。然后，受到生物系统局部可塑性的启发，其中突触更多地受到邻近神经元的调节，使用差分 STDP 来优化局部可塑性。在基准 MNIST (98.62%) 和 Fashion MNIST (89.05%) 上的大量实验结果表明，所提出的算法比通过反向传播训练的几种最先进的 SNN 表现得更好。