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Although widely used in machine learning, backpropagation cannot directly beapplied to SNN training and is not feasible on a neuromorphic processor thatemulates biological neuron and synapses. This work presents a spike-basedbackpropagation algorithm with biological plausible local update rules andadapts it to fit the constraint in a neuromorphic hardware. The algorithm isimplemented on Intel Loihi chip enabling low power in-hardware supervisedonline learning of multilayered SNNs for mobile applications. We test thisimplementation on MNIST, Fashion-MNIST, CIFAR-10 and MSTAR datasets withpromising performance and energy-efficiency, and demonstrate a possibility ofincremental online learning with the implementation.

In-Hardware Learning of Multilayer Spiking Neural Networks on a Neuromorphic Processor