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Computational principles of learning in the neocortex and hippocampus
Author(s) -
O'Reilly Randall C.,
Rudy Jerry W.
Publication year - 2000
Publication title -
hippocampus
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.767
H-Index - 155
eISSN - 1098-1063
pISSN - 1050-9631
DOI - 10.1002/1098-1063(2000)10:4<389::aid-hipo5>3.0.co;2-p
Subject(s) - neocortex , hebbian theory , hippocampus , recall , habituation , episodic memory , psychology , neuroscience , interference theory , computer science , amnesia , artificial intelligence , cognitive psychology , cognitive science , cognition , working memory , artificial neural network
We present an overview of our computational approach towards understanding the different contributions of the neocortex and hippocampus in learning and memory. The approach is based on a set of principles derived from converging biological, psychological, and computational constraints. The most central principles are that the neocortex employs a slow learning rate and overlapping distributed representations to extract the general statistical structure of the environment, while the hippocampus learns rapidly, using separated representations to encode the details of specific events while suffering minimal interference. Additional principles concern the nature of learning (error‐driven and Hebbian), and recall of information via pattern completion. We summarize the results of applying these principles to a wide range of phenomena in conditioning, habituation, contextual learning, recognition memory, recall, and retrograde amnesia, and we point to directions of current development. Hippocampus 10:389–397, 2000 © 2000 Wiley‐Liss, Inc.