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Spiking neural networks (SNNs) are brain-inspired mathematical models withthe ability to process information in the form of spikes. SNNs are expected toprovide not only new machine-learning algorithms, but also energy-efficientcomputational models when implemented in VLSI circuits. In this paper, wepropose a novel supervised learning algorithm for SNNs based on temporalcoding. A spiking neuron in this algorithm is designed to facilitate analogVLSI implementations with analog resistive memory, by which ultra-high energyefficiency can be achieved. We also propose several techniques to improve theperformance on a recognition task, and show that the classification accuracy ofthe proposed algorithm is as high as that of the state-of-the-art temporalcoding SNN algorithms on the MNIST dataset. Finally, we discuss the robustnessof the proposed SNNs against variations that arise from the devicemanufacturing process and are unavoidable in analog VLSI implementation. Wealso propose a technique to suppress the effects of variations in themanufacturing process on the recognition performance.

A Supervised Learning Algorithm for Multilayer Spiking Neural Networks Based on Temporal Coding Toward Energy-Efficient VLSI Processor Design