  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.
