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Spike-based neuromorphic hardware promises to reduce the energy consumptionof image classification and other deep learning applications, particularly onmobile phones or other edge devices. However, direct training of deep spikingneural networks is difficult, and previous methods for converting trainedartificial neural networks to spiking neurons were inefficient because theneurons had to emit too many spikes. We show that a substantially moreefficient conversion arises when one optimizes the spiking neuron model forthat purpose, so that it not only matters for information transmission how manyspikes a neuron emits, but also when it emits those spikes. This advances theaccuracy that can be achieved for image classification with spiking neurons,and the resulting networks need on average just two spikes per neuron forclassifying an image. In addition, our new conversion method improves latencyand throughput of the resulting spiking networks.

Optimized spiking neurons classify images with high accuracy through temporal coding with two spikes