  An ongoing challenge in neuromorphic computing is to devise general and
computationally efficient models of inference and learning which are compatible
with the spatial and temporal constraints of the brain. One increasingly
popular and successful approach is to take inspiration from inference and
learning algorithms used in deep neural networks. However, the workhorse of
deep learning, the gradient descent Back Propagation (BP) rule, often relies on
the immediate availability of network-wide information stored with
high-precision memory, and precise operations that are difficult to realize in
neuromorphic hardware. Remarkably, recent work showed that exact backpropagated
weights are not essential for learning deep representations. Random BP replaces
feedback weights with random ones and encourages the network to adjust its
feed-forward weights to learn pseudo-inverses of the (random) feedback weights.
Building on these results, we demonstrate an event-driven random BP (eRBP) rule
that uses an error-modulated synaptic plasticity for learning deep
representations in neuromorphic computing hardware. The rule requires only one
addition and two comparisons for each synaptic weight using a two-compartment
leaky Integrate & Fire (I&F) neuron, making it very suitable for implementation
in digital or mixed-signal neuromorphic hardware. Our results show that using
eRBP, deep representations are rapidly learned, achieving nearly identical
classification accuracies compared to artificial neural network simulations on
GPUs, while being robust to neural and synaptic state quantizations during
learning.
