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

Adaptive learning through temporal dynamics of state representation

View ORCID ProfileNiloufar Razmi, View ORCID ProfileMatthew R. Nassar
doi: https://doi.org/10.1101/2020.08.03.231068
Niloufar Razmi
1Psychiatry and Behavioral Sciences Research Center, Mashhad University of Medical Sciences, Mashhad, Iran
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Niloufar Razmi
Matthew R. Nassar
2Robert J. & Nancy D. Carney Institute for Brain Science, Brown University, Providence RI 02912-1821, USA
3Department of Neuroscience, Brown University, Providence RI 02912-1821, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Matthew R. Nassar
  • For correspondence: matthew_nassar@brown.edu
  • Abstract
  • Full Text
  • Info/History
  • Metrics
  • Preview PDF
Loading

Abstract

Humans adjust their learning rate according to local environmental statistics, however existing models of this process have failed to provide mechanistic links to underlying brain signals. Here, we implement a neural network model that uses latent variables from Bayesian inference to shift a neural context representation that controls the “state” to which feedback is associated. Within this model, behavioral signatures of adaptive learning emerge through temporally selective transitions in active states, which also mimic the evolution of neural patterns in orbitofrontal cortex. Transitions to a previous state after encountering a one-off outlier reduce learning, as observed in humans, and provide a mechanistic interpretation for bidirectional learning signals, such as the p300, that relate to learning differentially according to the source of surprising events. Together, our results demonstrate that dynamic latent state representations can afford normative inference and provide a coherent framework for understanding neural signatures of adaptive 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-NC-ND 4.0 International license.
Back to top
PreviousNext
Posted August 04, 2020.
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.
Adaptive learning through temporal dynamics of state representation
(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
Adaptive learning through temporal dynamics of state representation
Niloufar Razmi, Matthew R. Nassar
bioRxiv 2020.08.03.231068; doi: https://doi.org/10.1101/2020.08.03.231068
Digg logo Reddit logo Twitter logo Facebook logo Google logo LinkedIn logo Mendeley logo
Citation Tools
Adaptive learning through temporal dynamics of state representation
Niloufar Razmi, Matthew R. Nassar
bioRxiv 2020.08.03.231068; doi: https://doi.org/10.1101/2020.08.03.231068

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 (3609)
  • Biochemistry (7590)
  • Bioengineering (5533)
  • Bioinformatics (20833)
  • Biophysics (10347)
  • Cancer Biology (7998)
  • Cell Biology (11663)
  • Clinical Trials (138)
  • Developmental Biology (6619)
  • Ecology (10227)
  • Epidemiology (2065)
  • Evolutionary Biology (13648)
  • Genetics (9557)
  • Genomics (12860)
  • Immunology (7932)
  • Microbiology (19575)
  • Molecular Biology (7678)
  • Neuroscience (42193)
  • Paleontology (309)
  • Pathology (1259)
  • Pharmacology and Toxicology (2208)
  • Physiology (3272)
  • Plant Biology (7064)
  • Scientific Communication and Education (1295)
  • Synthetic Biology (1953)
  • Systems Biology (5435)
  • Zoology (1119)