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Cited by 104Memory and Neural MechanismsMemory Processes and InfluencesNeural dynamics and brain function

Organizing memories for generalization in complementary learning systems

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Sun, Weinan, Advani, Madhu, Spruston, Nelson, Saxe, Andrew, Fitzgerald, James E.·2023·Nature Neuroscience
DOI 10.1038/s41593-023-01382-9PMC PMC10400413

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Abstract

Memorization and generalization are complementary cognitive processes that jointly promote adaptive behavior. For example, animals should memorize safe routes to specific water sources and generalize from these memories to discover environmental features that predict new ones. These functions depend on systems consolidation mechanisms that construct neocortical memory traces from hippocampal precursors, but why systems consolidation only applies to a subset of hippocampal memories is unclear. Here we introduce a new neural network formalization of systems consolidation that reveals an overlooked tension—unregulated neocortical memory transfer can cause overfitting and harm generalization in an unpredictable world. We resolve this tension by postulating that memories only consolidate when it aids generalization. This framework accounts for partial hippocampal–cortical memory transfer and provides a normative principle for reconceptualizing numerous observations in the field. Generalization-optimized systems consolidation thus provides new insight into how adaptive behavior benefits from complementary learning systems specialized for memorization and generalization.

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  • Schema formation is beneficial when new evidence fits existing structure, but harmful when it is non-selective or poorly controlled: nonselective consolidation causes interference and loss of specificity.

    “Schema formation is beneficial when new evidence fits existing structure (Tse et al., 2007), but harmful when it is non-selective or poorly controlled: nonselective consolidation causes interference and loss of specificity (McClelland et al., 1995; Sun et al., 2023).”

    From Useful Memories Become Faulty When Continuously Updated by LLMs (2026) · cites this paper as Sun et al., 2023

    The Discussion (page unavailable) says unregulated consolidation can produce inaccurate predictions through overfitting, and the Results section says excessive consolidation can degrade generalization in less predictable environments. These passages support the warning about poorly regulated consolidation, but do not establish the cited clause’s specific claims of interference and loss of specificity. The sentence attributes its separate point about new evidence fitting existing structure to Tse et al., not Sun et al. (LLM, conf=0.87)

    Experimental — accuracy not yet measured

  • These failure modes weaken the boundary between what should be generalized, what should remain task-specific, and what should be preserved as raw episodic evidence.

    “Together, these failure modes weaken the boundary between what should be generalized, what should remain task-specific, and what should be preserved as raw episodic evidence (Sun et al., 2023; Bartlett, 1932; Brainerd and Reyna, 2002).”

    From Useful Memories Become Faulty When Continuously Updated by LLMs (2026) · cites this paper as Sun et al., 2023

    The Discussion (page unavailable) says that predictable parts of an experience should be consolidated for generalization, while unpredictable details may remain in hippocampus-dependent episodic memory. It also says unregulated consolidation can produce inaccurate predictions. This supports the generalization–episodic distinction and a related failure mode, but the paper does not establish a separate boundary for what should remain task-specific. (LLM, conf=0.84)

    Experimental — accuracy not yet measured

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Research works printed but not linked: 5

  1. [4]Rumelhart, D. E., Mcclelland, J. L. & PDP Research Group. Parallel Distributed Processing, Vol. 1: Foundations (Bradford Books, 1987).
  2. [7]Nadel L Samsonovich A Ryan L Moscovitch M Multiple trace theory of human memory: computational, neuroimaging, and neuropsychological results Hippocampus 2000 10 352 368 10985275
  3. [29]MacKay, D. J. C. Information Theory, Inference and Learning Algorithms (Cambridge University Press, 2003).
  4. [42]Pezeshki, M., Mitra, A., Bengio, Y. & Lajoie, G. Multi-scale feature learning dynamics: insights for double descent. In Proceedings of the 39th International Conference on Machine Learning (Eds. Chaudhuri, K. et al.) 17669–17690 (PMLR, 2022).
  5. [54]Aly, M. & Turk-Browne, N. B. The Hippocampus From Cells to Systems (eds. Hannula, D. E. & Duff, M. C.) 369–403 (Springer International Publishing, 2017).

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Authors
Sun, W., Advani, M., Spruston, N., Saxe, A., & Fitzgerald, J. E.
Affiliations
  • Howard Hughes Medical Institute (US)
  • Harvard University (US)
  • Canadian Institute for Advanced Research (CA)
Corresponding author
  • Saxe, Andrew
  • Fitzgerald, James E.
ORCID
Venue
Nature Neuroscience
Publisher
Springer Science and Business Media LLC
Published
2023-07-20
Volume
26
Issue
8
Pages
1438-1448
ISSN
  • 1097-6256 (ISSN-L)
  • 1546-1726
DOI
10.1038/s41593-023-01382-9
PMC
PMC10400413
OpenAlex
W4384922492
Cited by
103
References
124
Citations by year
2026: 21 · 2025: 30 · 2024: 30 · 2023: 5 · 2022: 16 · 2021: 1
FWCI
12.85
Type
journal article
Language
en
Open access
Licence
Locations
  • Nature Neuroscience (publishedVersion, cc-by)
  • bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • UCL Discovery (University College London) (submittedVersion, cc-by)
Funders
  • Howard Hughes Medical Institute
  • Swartz Foundation
  • Wellcome Trust (216386/Z/19/Z)
  • Royal Society
  • Gatsby Charitable Foundation
  • Swartz Foundation CIFAR Azrieli Global Scholars program
Topics
  • Life Sciences › Neuroscience › Cognitive Neuroscience › Memory and Neural Mechanisms
  • Life Sciences › Neuroscience › Cognitive Neuroscience › Memory Processes and Influences
  • Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
Keywords
complementary learning systems, systems consolidation, memory generalization, hippocampus, neocortex, memory transfer, overfitting, neural network model, memory consolidation, adaptive behavior
MeSH terms
Memory Consolidation, Animals, Generalization, Psychological, Hippocampus, Learning

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