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; 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. A
series of companion interactive tutorials complementary to this paper using our
Python package, snnTorch, are also made available:
https://snntorch.readthedocs.io/en/latest/tutorials/index.htm