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.
journal_name
Trends Cogn Scijournal_title
Trends in cognitive sciencesauthors
O'Reilly RC,Norman KAdoi
10.1016/s1364-6613(02)02005-3subject
Has Abstractpub_date
2002-12-01 00:00:00pages
505-510issue
12eissn
1364-6613issn
1879-307Xpii
S1364661302020053journal_volume
6pub_type
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