Skip to main content
bioRxiv
  • Home
  • About
  • Submit
  • ALERTS / RSS
Advanced Search
New Results

Integration of New Information in Memory: New Insights from a Complementary Learning Systems Perspective

View ORCID ProfileJames L. McClelland, Bruce L. McNaughton, View ORCID ProfileAndrew K. Lampinen
doi: https://doi.org/10.1101/2020.01.17.909804
James L. McClelland
1Stanford University, Stanford, CA 94305, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for James L. McClelland
  • For correspondence: jlmcc@stanford.edu
Bruce L. McNaughton
2University of California, Irvine, Irvine, CA 92697, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Andrew K. Lampinen
1Stanford University, Stanford, CA 94305, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Andrew K. Lampinen
  • Abstract
  • Full Text
  • Info/History
  • Metrics
  • Supplementary material
  • Data/Code
  • Preview PDF
Loading

Abstract

According to complementary learning systems theory, integrating new memories into the neocortex of the brain without interfering with what is already known depends on a gradual learning process, interleaving new items with previously learned items. However, empirical studies show that information consistent with prior knowledge can be integrated very quickly. We use artificial neural networks with properties like those we attribute to the neocortex to develop a theoretical understanding of the role of consistency with prior knowledge in putatively neocortex-like learning systems, providing new insights into when integration will be fast or slow and how integration might be made more efficient when the items to be learned are hierarchically structured. The work relies on deep linear networks that capture the qualitative aspects of the learning dynamics of the more complex non-linear networks used in previous work. The time course of learning in these networks can be linked to the hierarchical structure in the training data, captured mathematically as a set of dimensions that correspond to the branches in the hierarchy. In this context, a new item to be learned can be characterized as having aspects that project onto previously known dimensions, and others that require adding a new branch/dimension. The projection onto the known dimensions can be learned rapidly without interleaving, but learning the new dimension requires gradual interleaved learning. When a new item only overlaps with items within one branch of a hierarchy, interleaving can focus on the previously-known items within this branch, resulting in faster integration with less inter-leaving overall. The discussion considers how the brain might exploit these facts to make learning more efficient and highlights predictions about what aspects of new information might be hard or easy to learn.

Footnotes

  • jlmcc{at}stanford.edu, bruce.mcnaughton{at}uleth.ca, lampinen{at}stanford.edu

  • https://github.com/lampinen/integration_CLS

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
Back to top
PreviousNext
Posted January 19, 2020.
Download PDF

Supplementary Material

Data/Code
Email

Thank you for your interest in spreading the word about bioRxiv.

NOTE: Your email address is requested solely to identify you as the sender of this article.

Enter multiple addresses on separate lines or separate them with commas.
Integration of New Information in Memory: New Insights from a Complementary Learning Systems Perspective
(Your Name) has forwarded a page to you from bioRxiv
(Your Name) thought you would like to see this page from the bioRxiv website.
CAPTCHA
This question is for testing whether or not you are a human visitor and to prevent automated spam submissions.
Share
Integration of New Information in Memory: New Insights from a Complementary Learning Systems Perspective
James L. McClelland, Bruce L. McNaughton, Andrew K. Lampinen
bioRxiv 2020.01.17.909804; doi: https://doi.org/10.1101/2020.01.17.909804
Digg logo Reddit logo Twitter logo Facebook logo Google logo LinkedIn logo Mendeley logo
Citation Tools
Integration of New Information in Memory: New Insights from a Complementary Learning Systems Perspective
James L. McClelland, Bruce L. McNaughton, Andrew K. Lampinen
bioRxiv 2020.01.17.909804; doi: https://doi.org/10.1101/2020.01.17.909804

Citation Manager Formats

  • BibTeX
  • Bookends
  • EasyBib
  • EndNote (tagged)
  • EndNote 8 (xml)
  • Medlars
  • Mendeley
  • Papers
  • RefWorks Tagged
  • Ref Manager
  • RIS
  • Zotero
  • Tweet Widget
  • Facebook Like
  • Google Plus One

Subject Area

  • Neuroscience
Subject Areas
All Articles
  • Animal Behavior and Cognition (3586)
  • Biochemistry (7545)
  • Bioengineering (5495)
  • Bioinformatics (20729)
  • Biophysics (10294)
  • Cancer Biology (7950)
  • Cell Biology (11610)
  • Clinical Trials (138)
  • Developmental Biology (6586)
  • Ecology (10168)
  • Epidemiology (2065)
  • Evolutionary Biology (13578)
  • Genetics (9520)
  • Genomics (12817)
  • Immunology (7906)
  • Microbiology (19503)
  • Molecular Biology (7641)
  • Neuroscience (41982)
  • Paleontology (307)
  • Pathology (1254)
  • Pharmacology and Toxicology (2192)
  • Physiology (3259)
  • Plant Biology (7018)
  • Scientific Communication and Education (1293)
  • Synthetic Biology (1947)
  • Systems Biology (5418)
  • Zoology (1113)