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Computational principles of learning in the neocortex and hippocampus

Computational principles of learning in the neocortex and hippocampus 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. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Hippocampus Wiley

Computational principles of learning in the neocortex and hippocampus

Hippocampus , Volume 10 (4) – Jan 1, 2000

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References (84)

Publisher
Wiley
Copyright
Copyright © 2000 Wiley‐Liss, Inc.
ISSN
1050-9631
eISSN
1098-1063
DOI
10.1002/1098-1063(2000)10:4<389::AID-HIPO5>3.0.CO;2-P
pmid
10985278
Publisher site
See Article on Publisher Site

Abstract

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.

Journal

HippocampusWiley

Published: Jan 1, 2000

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