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 shows 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. This paper explores 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; 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.

中文翻译：

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使用深度学习的经验教训训练尖峰神经网络

大脑是寻找灵感以开发更高效神经网络的理想场所。我们突触和神经元的内部运作让我们得以一窥深度学习的未来可能是什么样子。本文展示了如何将在深度学习、梯度下降、反向传播和神经科学方面几十年的研究中汲取的经验教训应用于生物学上合理的尖峰神经网络。本文探讨了将数据编码为尖峰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尖峰神经网络的挑战和解决方案；时间反向传播和尖峰时间依赖性可塑性之间的微妙联系，以及深度学习如何朝着生物学上合理的在线学习发展。