Complementary learning systems
Open accessAbstract
This paper reviews the fate of the central ideas behind the complementary learning systems (CLS) framework as originally articulated in McClelland, McNaughton, and O’Reilly (1995) . This framework explains why the brain requires two differentially specialized learning and memory systems, and it nicely specifies their central properties (i.e., the hippocampus as a sparse, pattern‐separated system for rapidly learning episodic memories, and the neocortex as a distributed, overlapping system for gradually integrating across episodes to extract latent semantic structure). We review the application of the CLS framework to a range of important topics, including the following: the basic neural processes of hippocampal memory encoding and recall, conjunctive encoding, human recognition memory, consolidation of initial hippocampal learning in cortex, dynamic modulation of encoding versus recall, and the synergistic interactions between hippocampus and neocortex. Overall, the CLS framework remains a vital theoretical force in the field, with the empirical data over the past 15 years generally confirming its key principles.
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Human memory leaves most experiences unconsolidated unless repeatedly activated.
“RecMem's design mirrors human memory: most experiences remain unconsolidated unless repeatedly activated (Atkinson and Shiffrin, 1968; O'Reilly et al., 2014; McClelland et al., 1995).”
From RecMem: Recurrence-based Memory Consolidation for Efficient and Effective Long-Running LLM Agents (2026) · cites this paper as O'Reilly et al., 2014
Section 4 describes hippocampal replay as a way to train the slower-learning cortex, and §4.1 discusses replay’s role in consolidation. But the paper does not say that most human experiences remain unconsolidated or that repeated activation is required for an experience to consolidate. The indexed passages provide no page number. (LLM, conf=0.86)
Experimental — accuracy not yet measuredThe hippocampus plays a vital role in learning episodic memory by encoding them through a process that separates similar personal experiences and stores them distinctly, thereby minimizing interference between different episodic memory.
“The hippocampus in the brain plays a vital role in learning episodic memory by encoding them through a process that separates similar personal experiences and stores them distinctly, thereby minimizing interference between different episodic memory [77, 78, 79].”
From Human-inspired Perspectives: A Survey on AI Long-term Memory (2024) · cites this paper as [79]
The hippocampus is involved in the complex process of retrieving specific memory based on cues.
“The hippocampus of brain is involved in the complex process of retrieving specific memory based on cues [77, 79].”
From Human-inspired Perspectives: A Survey on AI Long-term Memory (2024) · cites this paper as [79]
The hippocampus and cortex of the brain are involved in the encoding of memory, with the hippocampus encoding unique episodic information through its pattern separation properties and the cortex encoding more generalized information through its distributed representations.
“The hippocampus and cortex of the brain are involved in the encoding of memory, with the hippocampus encoding unique episodic information through its pattern separation properties and the cortex encoding more generalized information through its distributed representations [77, 78, 96, 79].”
From Human-inspired Perspectives: A Survey on AI Long-term Memory (2024) · cites this paper as [79]
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InfluentialRecMem: Recurrence-based Memory Consolidation for Efficient and Effective Long-Running LLM Agents
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“The multi-store theory (Atkinson and Shiffrin, 1968) and the Complementary Learning Systems framework (Kumaran et al., 2016; O’Reilly et al., 2014; McClelland et al., 1995) Motivated by these insights, we propose RecMem, an efficient memory system for long-running agents that conducts fewer…”
- Authors
- O’Reilly, R. C., Bhattacharyya, R., Howard, M. D., & Ketz, N.
- Affiliations
- University of Colorado Boulder (US)
- HRL Laboratories (United States) (US)
- Corresponding author
- O’Reilly, Randall C.
- Venue
- Cognitive Science
- Publisher
- Wiley
- Published
- 2011-12-05
- Volume
- 38
- Issue
- 6
- Pages
- 1229-1248
- ISSN
- 0364-0213 (ISSN-L)
- 1551-6709
- OpenAlex
- W2159345153
- Cited by
- 259
- References
- 83
- Citations by year
- 2026: 10 · 2025: 26 · 2024: 22 · 2023: 26 · 2022: 26 · 2021: 24 · 2020: 16 · 2019: 22 · 2018: 14 · 2017: 17 · 2016: 17 · 2015: 13 · 2014: 9 · 2013: 12 · 2012: 4
- FWCI
- 0.71
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Cognitive Science (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Defense Advanced Research Projects Agency (HR0011)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Memory and Neural Mechanisms
- Life Sciences › Neuroscience › Cognitive Neuroscience › Memory Processes and Influences
- Life Sciences › Neuroscience › Cognitive Neuroscience › Sleep and Wakefulness Research
- Keywords
- complementary learning systems, hippocampus, neocortex, episodic memory, pattern separation, sparse coding, memory consolidation, recognition memory, memory encoding, memory retrieval, hippocampal-neocortical interaction
- MeSH terms
- Hippocampus, Humans, Learning, Models, Neurological, Neural Pathways, Neocortex, Recognition, Psychology, Memory, Episodic
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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