Spiking Neural Networks (SNNs) as one of the biology-inspired models have
received much attention recently. It can significantly reduce energy
consumption since they quantize the real-valued membrane potentials to 0/1
spikes to transmit information thus the multiplications of activations and
weights can be replaced by additions when implemented on hardware. However,
this quantization mechanism will inevitably introduce quantization error, thus
causing catastrophic information loss. To address the quantization error
problem, we propose a regularizing membrane potential loss (RMP-Loss) to adjust
the distribution which is directly related to quantization error to a range
close to the spikes. Our method is extremely simple to implement and
straightforward to train an SNN. Furthermore, it is shown to consistently
outperform previous state-of-the-art methods over different network
architectures and datasets.

我们提出了一种调节与量化误差密切相关的分布范围接近于脉冲的正则化膜电位损失 (RMP-Loss) 的方法，该方法非常简单易于实现和训练 SNN，并且在不同网络架构和数据集上始终表现优于以往的最先进方法。