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：时间曲面层级结构（HOTS）算法是一种从事件数据中提取特征的神经形态方法，虽展现出潜力，但在准确率及与神经形态硬件的兼容性方面仍面临挑战。本文提出Sup3r算法——一种旨在解决上述问题的半监督方法。Sup3r增强了HOTS网络中的稀疏性、稳定性与可分离性。通过利用半监督学习，Sup3r实现了HOTS网络的端到端在线训练，从而替代外部分类器。该算法能够学习具有类别判别力的模式、削弱混杂特征的影响，并减少需处理的事件数量。此外，Sup3r支持持续学习与增量学习，既能适应数据分布偏移，又能在不遗忘先前知识的情况下学习新任务。在N-MNIST数据集上的初步实验表明，Sup3r的准确率可与使用反向传播训练的同等规模人工神经网络相媲美。本工作展示了Sup3r提升HOTS网络能力的潜力，为神经形态算法在实际应用中的发展提供了新途径。