NeuroMoCo: A Neuromorphic Momentum Contrast Learning Method for Spiking Neural Networks

• URL: http://arxiv.org/abs/2406.06305v1
• Date: Mon, 10 Jun 2024 14:20:48 GMT
• Title: NeuroMoCo: A Neuromorphic Momentum Contrast Learning Method for Spiking Neural Networks
• Authors: Yuqi Ma, Huamin Wang, Hangchi Shen, Xuemei Chen, Shukai Duan, Shiping Wen,
• Abstract summary: This paper introduces Neuromorphic Momentum Contrast Learning (NeuroMoCo) for brain-inspired spiking neural networks (SNNs) This is the first time that self-supervised learning (SSL) based on momentum contrastive learning is realized in SNNs. experiments on DVS-CI10, DVS128Gesture and N-Caltech101 have shown that NeuroMoCo establishes new state-of-the-art (SOTA) benchmarks.
• Score: 18.038225756466844
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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