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Hippocampal and neocortical contributions to memory: advances in the complementary learning systems framework
Abstract
The complementary learning systems framework provides a simple set of principles, derived from converging biological, psychological and computational constraints, for understanding the differential contributions of the neocortex and hippocampus to learning and memory. The central principles are that the neocortex has a low learning rate and uses overlapping distributed representations to extract the general statistical structure of the environment, whereas the hippocampus learns rapidly using separated representations to encode the details of specific events while minimizing interference. In recent years, we have instantiated these principles in working computational models, and have used these models to address human and animal learning and memory findings, across a wide range of domains and paradigms. Here, we review a few representative applications of our models, focusing on two domains: recognition memory and animal learning in the fear-conditioning paradigm. In both domains, the models have generated novel predictions that have been tested and confirmed.
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“The two complementary memory systems-the hippocampal system and the neocortical sensori-motor areas-allow the coexistence of instances and abstractions, thus solving the two basic knowledge functions of an organism that needs to be able to acquire both specifics ~”
From AT THE INTERFACE: DYNAMIC INTERACTIONS OF EXPLICIT AND IMPLICIT LANGUAGE KNOWLEDGE (2005) · cites this paper as O'Reilly & Norman, 2002
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Metadata
- Authors
- O'Reilly, R. C., & Norman, K. A.
- Affiliations
- University of Colorado Boulder (US)
- ORCID
- Norman, Kenneth A
- Venue
- Trends in Cognitive Sciences
- Publisher
- Elsevier BV
- Published
- 2002-12
- Volume
- 6
- Issue
- 12
- Pages
- 505-510
- ISSN
- 1364-6613 (ISSN-L)
- OpenAlex
- W2104939199
- Cited by
- 351
- References
- 47
- Citations by year
- 2026: 7 · 2025: 14 · 2024: 20 · 2023: 7 · 2022: 7 · 2021: 18 · 2020: 20 · 2019: 12 · 2018: 20 · 2017: 13 · 2016: 10 · 2015: 15 · 2014: 17 · 2013: 16 · 2012: 21
- FWCI
- 1.26
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Trends in Cognitive Sciences (publishedVersion)
- PubMed (publishedVersion)
- 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, hippocampus, neocortex, memory, recognition memory, fear conditioning, computational models of learning, distributed representations, learning rate, interference minimization
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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