Spiking neural networks are a type of artificial neural networks in which
communication between neurons is only made of events, also called spikes. This
property allows neural networks to make asynchronous and sparse computations
and therefore to drastically decrease energy consumption when run on
specialized hardware. However, training such networks is known to be difficult,
mainly due to the non-differentiability of the spike activation, which prevents
the use of classical backpropagation. This is because state-of-the-art spiking
neural networks are usually derived from biologically-inspired neuron models,
to which are applied machine learning methods for training. Nowadays, research
about spiking neural networks focuses on the design of training algorithms
whose goal is to obtain networks that compete with their non-spiking version on
specific tasks. In this paper, we attempt the symmetrical approach: we modify
the dynamics of a well-known, easily trainable type of recurrent neural network
to make it event-based. This new RNN cell, called the Spiking Recurrent Cell,
therefore communicates using events, i.e. spikes, while being completely
differentiable. Vanilla backpropagation can thus be used to train any network
made of such RNN cell. We show that this new network can achieve performance
comparable to other types of spiking networks in the MNIST benchmark and its
variants, the Fashion-MNIST and the Neuromorphic-MNIST. Moreover, we show that
this new cell makes the training of deep spiking networks achievable.Comment: 12 pages, 3 figure