This journal article introduces a significantly expanded framework for integrating dynamic neuromorphic event-based processing with tropical hyperdimensional computing in cognitive ontology networks. Enhancements include the exploration of practical applications, ethical implications, and scalability to real-world datasets. This approach employs advanced dynamic adaptive learning rates, hierarchical relationship encoding, and novel binding mechanisms to achieve substantial improvements in clustering and classification tasks. A comparative study with state-of-the-art models highlights the framework's robustness, scalability, and biological plausibility. Additionally, the paper discusses new real-time hardware implementation potentials and multimodal integration, paving the way for future advancements.