Hierarchical Temporal Memory

Hierarchical Temporal Memory (HTM) is a biologically-inspired computing framework designed to mimic the neocortex's ability to learn and process temporal sequences. Current research focuses on improving HTM's efficiency and applicability through variations like sparse Hopfield networks and Grid HTM architectures, tailored for specific tasks such as anomaly detection in video and document categorization. These advancements aim to address limitations of traditional deep learning approaches, particularly regarding noise tolerance, online learning, and explainability, thereby expanding HTM's potential for robust and interpretable solutions in various domains. The resulting models show promise in surpassing existing methods in specific applications.