Implementing Backpropagation for Learning on Neuromorphic Spiking Hardware

Many contemporary advances in the theory and practice of neural networks are inspired by our understanding of how information is processed by natural neural systems. However, the basis of modern deep neural networks remains the error backpropagation algorithm [1], which though founded in rigorous mathematical optimization theory, has not been successfully demonstrated in a neurophysiologically realistic circuit. In a recent study, we proposed a neuromorphic architecture for learning that tunes the propagation of information forward and backwards through network layers using an endogenous timing mechanism controlled by thresholding of intensities [2]. This mechanism was demonstrated in simulation of analog currents, which represent the mean fields of spiking neuron populations. In this follow-on study, we present a modified architecture that includes several new mechanisms that enable implementation of the backpropagation algorithm using neuromorphic spiking units.We demonstrate the function of this architecture in learning mapping examples, both in event-based simulation as well as a true hardware implementation.