  Deep spiking neural networks (SNNs) hold great potential for improving the
latency and energy efficiency of deep neural networks through event-based
computation. However, training such networks is difficult due to the
non-differentiable nature of asynchronous spike events. In this paper, we
introduce a novel technique, which treats the membrane potentials of spiking
neurons as differentiable signals, where discontinuities at spike times are
only considered as noise. This enables an error backpropagation mechanism for
deep SNNs, which works directly on spike signals and membrane potentials. Thus,
compared with previous methods relying on indirect training and conversion, our
technique has the potential to capture the statics of spikes more precisely.
Our novel framework outperforms all previously reported results for SNNs on the
permutation invariant MNIST benchmark, as well as the N-MNIST benchmark
recorded with event-based vision sensors.
