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The primate visual system has inspired the development of deep artificialneural networks, which have revolutionized the computer vision domain. Yetthese networks are much less energy-efficient than their biologicalcounterparts, and they are typically trained with backpropagation, which isextremely data-hungry. To address these limitations, we used a deepconvolutional spiking neural network (DCSNN) and a latency-coding scheme. Wetrained it using a combination of spike-timing-dependent plasticity (STDP) forthe lower layers and reward-modulated STDP (R-STDP) for the higher ones. Inshort, with R-STDP a correct (resp. incorrect) decision leads to STDP (resp.anti-STDP). This approach led to an accuracy of $97.2\%$ on MNIST, withoutrequiring an external classifier. In addition, we demonstrated that R-STDPextracts features that are diagnostic for the task at hand, and discards theother ones, whereas STDP extracts any feature that repeats. Finally, ourapproach is biologically plausible, hardware friendly, and energy-efficient.

Bio-inspired digit recognition using reward-modulated spike-timing-dependent plasticity in deep convolutional networks