The Hierarchy Of Time-Surfaces (HOTS) algorithm, a neuromorphic approach for feature extraction from event data, presents promising capabilities but faces challenges in accuracy and compatibility with neuromorphic hardware. In this paper, we introduce Sup3r, a Semi-Supervised algorithm aimed at addressing these challenges. Sup3r enhances sparsity, stability, and separability in the HOTS networks. It enables end-to-end online training of HOTS networks replacing external classifiers, by leveraging semi-supervised learning. Sup3r learns class-informative patterns, mitigates confounding features, and reduces the number of processed events. Moreover, Sup3r facilitates continual and incremental learning, allowing adaptation to data distribution shifts and learning new tasks without forgetting. Preliminary results on N-MNIST demonstrate that Sup3r achieves comparable accuracy to similarly sized Artificial Neural Networks trained with back-propagation. This work showcases the potential of Sup3r to advance the capabilities of HOTS networks, offering a promising avenue for neuromorphic algorithms in real-world applications.

翻译：时间曲面层次结构（HOTS）算法是一种从事件数据中提取特征的神经形态方法，展现出良好的潜力，但在准确性和与神经形态硬件的兼容性方面面临挑战。本文提出了一种半监督算法Sup3r，旨在解决这些挑战。Sup3r增强了HOTS网络中的稀疏性、稳定性和可分离性。它通过利用半监督学习，实现了HOTS网络的端到端在线训练，从而取代外部分类器。Sup3r学习类别信息模式，抑制混淆特征，并减少处理的事件数量。此外，Sup3r支持持续和增量学习，能够适应数据分布变化并学习新任务而不遗忘。在N-MNIST上的初步结果表明，Sup3r的准确率与采用反向传播训练的同等规模人工神经网络相当。该项工作展示了Sup3r提升HOTS网络能力的潜力，为神经形态算法在实际应用中提供了一条有前景的途径。