  Recently, brain-inspired spiking neural networks (SNNs) have attracted great
research attention owing to their inherent bio-interpretability,
event-triggered properties and powerful perception of spatiotemporal
information, which is beneficial to handling event-based neuromorphic datasets.
In contrast to conventional static image datasets, event-based neuromorphic
datasets present heightened complexity in feature extraction due to their
distinctive time series and sparsity characteristics, which influences their
classification accuracy. To overcome this challenge, a novel approach termed
Neuromorphic Momentum Contrast Learning (NeuroMoCo) for SNNs is introduced in
this paper by extending the benefits of self-supervised pre-training to SNNs to
effectively stimulate their potential. This is the first time that
self-supervised learning (SSL) based on momentum contrastive learning is
realized in SNNs. In addition, we devise a novel loss function named MixInfoNCE
tailored to their temporal characteristics to further increase the
classification accuracy of neuromorphic datasets, which is verified through
rigorous ablation experiments. Finally, experiments on DVS-CIFAR10,
DVS128Gesture and N-Caltech101 have shown that NeuroMoCo of this paper
establishes new state-of-the-art (SOTA) benchmarks: 83.6% (Spikformer-2-256),
98.62% (Spikformer-2-256), and 84.4% (SEW-ResNet-18), respectively.
