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.
Key-value memory in the brain
Key-value memory systems optimize storage and retrieval by separating representations, offering a more nuanced approach than classical similarity-based models.
- Year
- 2025
- Venue
- arXiv 2025
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- 3
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- Abstract onlyARXIV-DEFAULT
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- arxiv.org/abs/2501.02950v2ARXIV-DEFAULT
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