Quantifying Temporal Entropy in Neuromorphic Memory Forgetting: Exploring Advanced Forgetting Models for Robust Long-Term Information Storage

This paper presents a progression of a popular neuromorphic memory structure by exploring advanced forgetting models for robust long-term information storage. Inspired by biological neuronal systems, neuromorphic sensors efficiently capture and transmit sensory information using event-based communication. Managing the decay of information over time is a critical aspect, and forgetting models play a vital role in this process. Building upon the foundation of an existing popular neuromorphic memory structure, this study introduces and evaluates four advanced forgetting models: ROT, adaptive, emotional memory enhancement, and context-dependent memory forgetting models. Each model incorporates different factors to modulate the rate of decay or forgetting. Through rigorous experimentation and analysis, these models are compared with the original ROT forgetting model to assess their effectiveness in retaining relevant information while discarding irrelevant or outdated data. The results provide insights into the strengths, limitations, and potential applications of these advanced forgetting models in the context of neuromorphic memory systems, thereby contributing to the progression of this popular neuromorphic memory structure.