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

A neural network model of hippocampal contributions to category learning

View ORCID ProfileJelena Sučević, View ORCID ProfileAnna C. Schapiro
doi: https://doi.org/10.1101/2022.01.12.476051
Jelena Sučević
1Department of Experimental Psychology, University of Oxford
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Jelena Sučević
  • For correspondence: jelena.sucevic@psy.ox.ac.uk
Anna C. Schapiro
2Department of Psychology, University of Pennsylvania
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Anna C. Schapiro
  • Abstract
  • Full Text
  • Info/History
  • Metrics
  • Preview PDF
Loading

Abstract

In addition to its critical role in encoding individual episodes, the hippocampus is capable of extracting regularities across experiences. This ability is central to category learning, and a growing literature indicates that the hippocampus indeed makes important contributions to this kind of learning. Using a neural network model that mirrors the anatomy of the hippocampus, we investigated the mechanisms by which the hippocampus may support novel category learning. We simulated three category learning paradigms and evaluated the network’s ability to categorize and to recognize specific exemplars in each. We found that the trisynaptic pathway within the hippocampus—connecting entorhinal cortex to dentate gyrus, CA3, and CA1—was critical for remembering individual exemplars, reflecting the rapid binding and pattern separation functions of this circuit. The monosynaptic pathway from entorhinal cortex to CA1, in contrast, was responsible for detecting the regularities that define category structure, made possible by the use of distributed representations and a slower learning rate. Together, the simulations provide an account of how the hippocampus and its constituent pathways support novel category learning.

Competing Interest Statement

The authors have declared no competing interest.

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 4.0 International license.
Back to top
PreviousNext
Posted January 13, 2022.
Download PDF
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.
A neural network model of hippocampal contributions to category learning
(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
A neural network model of hippocampal contributions to category learning
Jelena Sučević, Anna C. Schapiro
bioRxiv 2022.01.12.476051; doi: https://doi.org/10.1101/2022.01.12.476051
Digg logo Reddit logo Twitter logo Facebook logo Google logo LinkedIn logo Mendeley logo
Citation Tools
A neural network model of hippocampal contributions to category learning
Jelena Sučević, Anna C. Schapiro
bioRxiv 2022.01.12.476051; doi: https://doi.org/10.1101/2022.01.12.476051

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 (4113)
  • Biochemistry (8816)
  • Bioengineering (6519)
  • Bioinformatics (23463)
  • Biophysics (11791)
  • Cancer Biology (9209)
  • Cell Biology (13324)
  • Clinical Trials (138)
  • Developmental Biology (7439)
  • Ecology (11410)
  • Epidemiology (2066)
  • Evolutionary Biology (15152)
  • Genetics (10438)
  • Genomics (14044)
  • Immunology (9171)
  • Microbiology (22155)
  • Molecular Biology (8812)
  • Neuroscience (47570)
  • Paleontology (350)
  • Pathology (1428)
  • Pharmacology and Toxicology (2491)
  • Physiology (3730)
  • Plant Biology (8081)
  • Scientific Communication and Education (1437)
  • Synthetic Biology (2221)
  • Systems Biology (6038)
  • Zoology (1253)