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：大脑是寻找灵感以开发更高效神经网络的绝佳场所。我们的突触和神经元的内部运作机制为深度学习的未来提供了可能的图景。本文既是一篇教程，也是一种视角，展示了如何将深度学习、梯度下降、反向传播和神经科学领域数十年研究中汲取的经验应用于生物可解释的脉冲神经网络。我们还探讨了将数据编码为脉冲与学习过程之间微妙的相互作用；将基于梯度的学习应用于脉冲神经网络（SNNs）所面临的挑战及解决方案；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迈进。其中一些观点在神经形态工程领域已被广泛接受和常用，而另一些则是首次在此提出或论证。深度学习和脉冲神经网络领域发展极为迅速。我们力求将此文档视为一份“动态”手稿，随着训练SNNs的通用实践变化而持续更新。我们还提供了与本文配套的一系列交互式教程，使用我们的Python包snnTorch。参见https://snntorch.readthedocs.io/en/latest/tutorials/index.html。