We propose a new supervised learning rule for multilayer spiking neural networks (SNN) that use a form of temporal coding known as rank-order-coding. With this coding scheme, all neurons fire exactly one spike per stimulus, but the firing order carries information. In particular, in the readout layer, the first neuron to fire determines the class of the stimulus. We derive a new learning rule for this sort of network, termed S4NN, akin to traditional error backpropagation, yet based on latencies. We show how approximate error gradients can be computed backward in a feedforward network with an arbitrary number of layers. This approach reaches state-of-the-art performance with SNNs: test accuracy of 97.4% for the MNIST dataset, and of 99.2% for the Caltech Face/Motorbike dataset. Yet the neuron model we use, non-leaky integrate-and-fire, are simpler and more hardware friendly than the one used in all previous similar proposals.

提出了基于时间编码的多层脉冲神经网络（SNNs）的新的有监督学习规则，并演示了在多层全连接SNNs的MNIST数据集上实现了97.4%的测试准确性，使用的神经元模型比以前的工作要简单得多，源代码公开。

S4NN：每个神经元一个脉冲的脉冲神经网络的时间反向传播