Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Videos

Metalearning and neuromodulation.

Kenji Doya1

  • 1ATR Human Information Science Laboratories, CREST, Japan Science and Technology Corporation, Kyoto. doya@atr.co.jp

Neural Networks : the Official Journal of the International Neural Network Society
|October 10, 2002
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

A computational model of canonical cortical microcircuits for dynamic Bayesian inference and control as inference.

Neuroscience research·2025
Same author

Possible contribution to data-driven primate research: Comment on "Kinematic coding: Measuring information in naturalistic behaviour" by Becchio, Pullar, Scaliti, and Panzeri.

Physics of life reviews·2025
Same author

Optical Neuroimage Studio (OptiNiSt): Intuitive, scalable, extendable framework for optical neuroimage data analysis.

PLoS computational biology·2025
Same author

Information-Theoretical Analysis of Team Dynamics in Football Matches.

Entropy (Basel, Switzerland)·2025
Same author

The differential effect of optogenetic serotonergic manipulation on sustained motor actions and waiting for future rewards in mice.

Frontiers in neuroscience·2024
Same author

A whole-brain analysis of functional connectivity and immediate early gene expression reveals functional network shifts after operant learning.

NeuroImage·2024
Same journal

Exploiting audio-visual modalities in videos: Object detection via multi-stage bilateral coupling network.

Neural networks : the official journal of the International Neural Network Society·2026
Same journal

Reliability-aware modality completion with cross-modal distillation for federated learning with missing modalities.

Neural networks : the official journal of the International Neural Network Society·2026
Same journal

IGFD-Net: Illumination-guided frequency decoupling for polarization image fusion.

Neural networks : the official journal of the International Neural Network Society·2026
Same journal

Multiple-Strategies dung beetle optimizer and its applications in engineering optimization and bankruptcy prediction.

Neural networks : the official journal of the International Neural Network Society·2026
Same journal

Aggregating global-scale pixel-wise forgery cues within a graph.

Neural networks : the official journal of the International Neural Network Society·2026
Same journal

Finite-Time intermittent control for secure synchronization of Neutral-Type stochastic delayed neural networks under aperiodic DoS attacks.

Neural networks : the official journal of the International Neural Network Society·2026
See all related articles

This study proposes that brain neuromodulators like dopamine and serotonin regulate learning. Each system, including noradrenaline and acetylcholine, optimizes specific aspects of prediction and memory.

Area of Science:

  • Computational neuroscience
  • Neurobiology
  • Cognitive science

Background:

  • Ascending neuromodulatory systems play crucial roles in brain function.
  • Understanding their precise roles in learning is an ongoing challenge.
  • Existing models often focus on individual systems, lacking a unified framework.

Purpose of the Study:

  • To present a computational theory integrating the roles of major neuromodulatory systems in brain learning.
  • To elucidate how dopamine, serotonin, noradrenaline, and acetylcholine regulate distributed learning mechanisms.
  • To predict neuromodulator-environment interactions using computational metalearning theory.

Main Methods:

  • Review and synthesis of existing experimental data on neuromodulatory systems.

Related Experiment Videos

  • Development of a computational framework for understanding neuromodulator functions.
  • Application of computational metalearning theory to predict system interactions.

    • Main Results:

    • Dopamine is proposed to signal reward prediction error.
    • Serotonin is theorized to control the time scale of reward prediction.
    • Noradrenaline is hypothesized to regulate randomness in action selection, and acetylcholine the speed of memory update.

    Conclusions:

    • The proposed computational theory offers a unified perspective on neuromodulatory system functions in learning.
    • This framework predicts specific interactions between neuromodulators and the environment.
    • The findings advance our understanding of how global signals regulate distributed learning in the brain.