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 Video

Updated: Aug 29, 2025

Functional Imaging with Reinforcement, Eyetracking, and Physiological Monitoring
08:47

Functional Imaging with Reinforcement, Eyetracking, and Physiological Monitoring

Published on: November 13, 2008

11.5K

Estimating Reward Function from Medial Prefrontal Cortex Cortical Activity using Inverse Reinforcement Learning.

Jieyuan Tan, Xiang Shen, Xiang Zhang

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |September 10, 2022
    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

    BIRC3 (Encoding Cellular Inhibitor of Apoptosis Protein 2) Variants Result in Dysregulated Receptor-Interacting Protein Kinase 1 Signaling Leading to Increased Epithelial Cell Death and Are Associated With Monogenic Crohn's Disease.

    Gastroenterology·2026
    Same author

    Reconfigurable on-chip polarizers enabled by phase-change-material-mediated loss engineering.

    Optics express·2026
    Same author

    A 'frost formation'-inspired near-infrared-responsive nitric oxide-releasing hydrogel for enhancing fat graft survival.

    Regenerative biomaterials·2026
    Same author

    Research on the Physiological Response Mechanism and Expression of Key Leaf Color Genes in 'Duojiao' Crabapple Under Partial Shading.

    Plants (Basel, Switzerland)·2026
    Same author

    Erratum: [Corrigendum] Proliferation, migration and invasion of triple negative breast cancer cells are suppressed by berbamine via the PI3K/Akt/MDM2/p53 and PI3K/Akt/mTOR signaling pathways.

    Oncology letters·2026
    Same author

    Dual-interface assembled silanized amide nanoclusters: driving light-carbon synergy to enhance photosynthesis in wheat.

    Journal of colloid and interface science·2026

    This study introduces inverse reinforcement learning (IRL) to estimate internal reward signals from brain activity for brain-machine interfaces (BMIs). This approach aids decoder training, achieving performance comparable to external rewards.

    Area of Science:

    • Neuroscience
    • Machine Learning
    • Biomedical Engineering

    Background:

    • Reinforcement learning (RL) is used in brain-machine interfaces (BMIs) to map neural signals to intentions via reward signals.
    • Current RL-BMIs often rely on external rewards, which can be difficult to design for complex tasks and may not reflect internal subject evaluation.
    • Extracting internal reward signals directly from neural activity is crucial for autonomous learning BMIs.

    Purpose of the Study:

    • To propose and validate an inverse reinforcement learning (IRL) method for estimating internal reward functions from neural data.
    • To improve the training of decoders in RL-based BMIs by incorporating internally derived reward signals.
    • To explore the potential of IRL for creating more autonomous and adaptive BMIs.

    Main Methods:

    More Related Videos

    Studying Food Reward and Motivation in Humans
    12:09

    Studying Food Reward and Motivation in Humans

    Published on: March 19, 2014

    23.6K
    Operant Protocols for Assessing the Cost-benefit Analysis During Reinforced Decision Making by Rodents
    07:05

    Operant Protocols for Assessing the Cost-benefit Analysis During Reinforced Decision Making by Rodents

    Published on: September 10, 2018

    6.1K

    Related Experiment Videos

    Last Updated: Aug 29, 2025

    Functional Imaging with Reinforcement, Eyetracking, and Physiological Monitoring
    08:47

    Functional Imaging with Reinforcement, Eyetracking, and Physiological Monitoring

    Published on: November 13, 2008

    11.5K
    Studying Food Reward and Motivation in Humans
    12:09

    Studying Food Reward and Motivation in Humans

    Published on: March 19, 2014

    23.6K
    Operant Protocols for Assessing the Cost-benefit Analysis During Reinforced Decision Making by Rodents
    07:05

    Operant Protocols for Assessing the Cost-benefit Analysis During Reinforced Decision Making by Rodents

    Published on: September 10, 2018

    6.1K
    • Applied inverse Q-learning (IQL), an IRL algorithm, to estimate internal reward functions.
    • Utilized neural data from the medial prefrontal cortex (mPFC) of a rat performing a two-lever-press discrimination task.
    • Trained RL decoders using the estimated internal reward information and compared performance against decoders trained with external rewards.

    Main Results:

    • The IRL-derived internal reward information successfully guided the training of the RL decoder for the movement task.
    • The decoding performance achieved using the internal reward model was comparable to that obtained with external rewards.
    • Validated the effectiveness of using IRL to model internal reward functions from neural activity.

    Conclusions:

    • Inverse reinforcement learning (IRL) is a viable method for estimating internal reward models from neural data in brain-machine interfaces (BMIs).
    • This approach holds promise for enhancing the design of autonomous learning BMIs by leveraging the subject's internal evaluation.
    • The findings support the development of more adaptive and sophisticated BMI systems.