  A growing body of work underlines striking similarities between biological
neural networks and recurrent, binary neural networks. A relatively smaller
body of work, however, discusses similarities between learning dynamics
employed in deep artificial neural networks and synaptic plasticity in spiking
neural networks. The challenge preventing this is largely caused by the
discrepancy between the dynamical properties of synaptic plasticity and the
requirements for gradient backpropagation. Learning algorithms that approximate
gradient backpropagation using locally synthesized gradients can overcome this
challenge. Here, we show that synthetic gradients enable the derivation of Deep
Continuous Local Learning (DECOLLE) in spiking neural networks. DECOLLE is
capable of learning deep spatio-temporal representations from spikes relying
solely on local information. Synaptic plasticity rules are derived
systematically from user-defined cost functions and neural dynamics by
leveraging existing autodifferentiation methods of machine learning frameworks.
We benchmark our approach on the MNIST and the event-based neuromorphic
DvsGesture dataset, on which DECOLLE performs comparably to the
state-of-the-art. DECOLLE networks provide continuously learning machines that
are relevant to biology and supportive of event-based, low-power computer
vision architectures matching the accuracies of conventional computers on tasks
where temporal precision and speed are essential.
