  Although widely used in machine learning, backpropagation cannot directly be
applied to SNN training and is not feasible on a neuromorphic processor that
emulates biological neuron and synapses. This work presents a spike-based
backpropagation algorithm with biological plausible local update rules and
adapts it to fit the constraint in a neuromorphic hardware. The algorithm is
implemented on Intel Loihi chip enabling low power in-hardware supervised
online learning of multilayered SNNs for mobile applications. We test this
implementation on MNIST, Fashion-MNIST, CIFAR-10 and MSTAR datasets with
promising performance and energy-efficiency, and demonstrate a possibility of
incremental online learning with the implementation.
