提出大脑记忆的键值分离机制，提升存储精度与检索区分度。
Key-value memory in the brain
- 用键值分离架构区分存储与检索表征
- 实现高保真存储与强区分性检索的双重优化
- 适用于解释记忆现象并启发神经网络设计
经典记忆模型依赖存储模式与检索线索之间的相似性进行召回，但无法区分存储与检索的独立需求。键值记忆系统则将存储内容（值）与检索线索（键）分开表征，使系统能同时优化存储保真度与检索区分度。本文综述了键值记忆的计算基础、在现代机器学习中的应用、心理学与神经科学中的相关思想、对若干经验难题的解释，以及可能的生物学实现方式。
原文摘要 · Abstract (English)
Classical models of memory in psychology and neuroscience rely on similarity-based retrieval of stored patterns, where similarity is a function of retrieval cues and the stored patterns. While parsimonious, these models do not allow distinct representations for storage and retrieval, despite their distinct computational demands. Key-value memory systems, in contrast, distinguish representations used for storage (values) and those used for retrieval (keys). This allows key-value memory systems to optimize simultaneously for fidelity in storage and discriminability in retrieval. We review the computational foundations of key-value memory, its role in modern machine learning systems, related ideas from psychology and neuroscience, applications to a number of empirical puzzles, and possible biological implementations.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。