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.

翻译：大脑是寻找灵感以开发更高效神经网络的完美场所。 我们神经突触和神经元的内部作用让我们可以一窥深层学习的未来可能是什么样子。 本文展示了如何将数十年来深层学习、梯度下行、回压和神经科学研究中的经验教训应用到生物上可信的神经神经网络中。 本文探讨了编码数据作为钉钉钉和学习过程之间的微妙互动关系; 将梯度学习应用到突触神经网络的挑战和解决方案; 时间回流和超峰定时依赖的塑料性之间的微妙联系, 以及深层学习如何走向生物上可信的在线学习。 某些想法在神经形态工程界被广泛接受和普遍使用, 而另一些想法则首次在这里被提出或证明合理。