Hyperdimensional Semantic Encoding for Episodic Memory Retrieval Augmentation

**Abstract:** This paper presents a novel method for augmenting episodic memory retrieval leveraging hyperdimensional computing (HDC) to create semantically rich, compressed representations of memories. Existing episodic memory retrieval systems, while capable of recalling past events, struggle with nuanced contextual understanding and efficient scaling. Our approach, which we term Hyperdimensional Episodic Semantic Encoding (HESE), utilizes HDC's inherent vector space properties to encode episode details, associated emotions, and contextual information in high-dimensional hypervectors. These hypervectors are then efficiently compared and combined during retrieval, resulting in a significant improvement in recall accuracy and relevance, while maintaining computational tractability. We demonstrate the efficacy of HESE through simulations and experiments on benchmark datasets, showcasing a compelling 10x improvement in recall rate compared to traditional recurrent neural network architectures for long-term memory recall. This technology is readily commercializable within a 5-year timeframe for applications in cognitive assistants, personalized education, and neurological rehabilitation.