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

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Abstract

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ROT forgetting model to assess their effectiveness in retaining relevant information while discarding irrelevant or outdated data.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.
Original languageEnglish
Pages294-299
Number of pages6
DOIs
Publication statusPublished online - 1 Jan 2024
Event2023 IEEE Symposium Series on Computational Intelligence: SSCI 2023 - heraton Mexico City Maria Isabel Hotel, Mexico City, Mexico
Duration: 5 Dec 20238 Dec 2023
https://attend.ieee.org/ssci-2023/

Conference

Conference2023 IEEE Symposium Series on Computational Intelligence
Abbreviated titleSSCI 2023
Country/TerritoryMexico
CityMexico City
Period5/12/238/12/23
Internet address

Bibliographical note

Publisher Copyright:
© 2023 IEEE.

Keywords

  • Neuromorphic
  • Forgetting Model
  • Data Structure
  • Imaging
  • Machine Vision
  • Pattern Recognition
  • Bio-inspired
  • Model-based

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