The DOI matches its Crossref record; 45 of the 47 references it cites were found in a registry or an index.
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.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“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
Citations
Cites
(1978) · UA Campus Repository (The University of Arizona) · Cited by 8,010
LOSS OF RECENT MEMORY AFTER BILATERAL HIPPOCAMPAL LESIONS
Scoville, W. B., & Milner, B. (1957) · Journal of Neurology, Neurosurgery & Psychiatry · doi:10.1136/jnnp.20.1.11 · Cited by 7,322
Memory and the hippocampus: A synthesis from findings with rats, monkeys, and humans.
Squire, L. R. (1992) · Psychological Review · doi:10.1037/0033-295x.99.2.195 · Cited by 5,376
McClelland, J. L., McNaughton, B. L., & O'Reilly, R. C. (1995) · Psychological Review · doi:10.1037/0033-295x.102.3.419 · Cited by 5,256
Self-citationThe hippocampus as a cognitive map
Passingham, R. E. (1979) · Neuroscience · doi:10.1016/0306-4522(79)90015-0 · Cited by 5,128
The Nature of Recollection and Familiarity: A Review of 30 Years of Research
Yonelinas, A. P. (2002) · Journal of Memory and Language · doi:10.1006/jmla.2002.2864 · Cited by 3,840
Simple memory: a theory for archicortex
Marr, D. (1971) · Philosophical Transactions of the Royal Society of London. B, Biological Sciences · doi:10.1098/rstb.1971.0078 · Cited by 2,771
Grossberg, S. (1976) · Biological Cybernetics · doi:10.1007/bf00344744 · Cited by 1,910
Episodic memory, amnesia, and the hippocampal–anterior thalamic axis
Aggleton, J. P., & Brown, M. W. (1999) · Behavioral and Brain Sciences · doi:10.1017/s0140525x99002034 · Cited by 1,879
Norman, K. A., & O'Reilly, R. C. (2003) · Psychological Review · doi:10.1037/0033-295x.110.4.611 · Cited by 1,312
Self-citation
Research works printed but not linked: 1
- O'Reilly, 2000
Cited by
Sorted by citations
Continual lifelong learning with neural networks: A review
Parisi, G. I., Kemker, R., Part, J. L., Kanan, C., & Wermter, S. (2019) · Neural Networks · doi:10.1016/j.neunet.2019.01.012 · Cited by 3,117
The Quarterly Journal of Experimental Psychology
Treisman, A., & Geffen, G. M. (2012) · Oxford University Press eBooks · doi:10.1093/acprof:osobl/9780199734337.003.0007 · Cited by 1,654
Pattern separation in the hippocampus
Yassa, M. A., & Stark, C. E. L. (2011) · Trends in Neurosciences · doi:10.1016/j.tins.2011.06.006 · Cited by 1,544
Left ventrolateral prefrontal cortex and the cognitive control of memory
Badre, D., & Wagner, A. D. (2007) · Neuropsychologia · doi:10.1016/j.neuropsychologia.2007.06.015 · Cited by 1,471
Continual Learning with Deep Generative Replay
Shin, H., Lee, J. K., Kim, J., & Kim, J. (2017) · arXiv · doi:10.48550/arxiv.1705.08690 · Cited by 1,385
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 ›
Are you an author, or do you see a problem with this page? Request a review
