The brain is the perfect place to look for inspiration to develop more efficient neural networks. The inner workings of our synapses and neurons provide a glimpse at what the future of deep learning might look like. This paper serves as a tutorial and perspective showing how to apply the lessons learnt from several decades of research in deep learning, gradient descent, backpropagation and neuroscience to biologically plausible spiking neural neural networks. We also explore the delicate interplay between encoding data as spikes and the learning process; the challenges and solutions of applying gradient-based learning to spiking neural networks (SNNs); the subtle link between temporal backpropagation and spike timing dependent plasticity, and how deep learning might move towards biologically plausible online learning. Some ideas are well accepted and commonly used amongst the neuromorphic engineering community, while others are presented or justified for the first time here. The fields of deep learning and spiking neural networks evolve very rapidly. We endeavour to treat this document as a 'dynamic' manuscript that will continue to be updated as the common practices in training SNNs also change. A series of companion interactive tutorials complementary to this paper using our Python package, snnTorch, are also made available. See https://snntorch.readthedocs.io/en/latest/tutorials/index.html .

翻译：大脑是寻求灵感以开发更高效神经网络的理想场所。突触和神经元的内在运作机制为我们揭示了深度学习未来可能的发展方向。本文兼具教程与展望性质，展示了如何将深度学习、梯度下降、反向传播及神经科学领域数十年的研究成果应用于生物可解释的脉冲神经网络。我们探讨了将数据编码为脉冲与学习过程之间的精妙交互；在脉冲神经网络（SNN）中应用基于梯度学习所面临的挑战与解决方案；时间反向传播与脉冲时序依赖可塑性之间的微妙联系，以及深度学习如何向生物可解释的在线学习演进。部分观点已被神经形态工程学界广泛接受并普遍采用，另一些则在此首次提出或论证。深度学习与脉冲神经网络领域发展极为迅速，我们致力于将本文视为一份“动态”手稿，将随SNN训练常规实践的演变持续更新。本文还配套提供一系列基于Python包snnTorch的交互式教程，详见https://snntorch.readthedocs.io/en/latest/tutorials/index.html。