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

Basic module for an adaptive control system based on neurone information processing.

R D Orpwood1

  • 1Bath Institute of Medical Engineering, St Martin's Hospital, Wellsway, UK.

Journal of Biomedical Engineering
|April 1, 1988
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 possible neural mechanism underlying consciousness based on the pattern processing capabilities of pyramidal neurons in the cerebral cortex.

Journal of theoretical biology·1994
Same author

Computer simulation of neurone pattern processing.

Journal of biomedical engineering·1992
Same author

Clinical experience in rehabilitation robotics.

Journal of biomedical engineering·1991
Same author

Development of a robot arm and workstation for the disabled.

Journal of biomedical engineering·1990
Same author

Mechanisms of association learning in the post-synaptic neurone.

Journal of theoretical biology·1990
Same author

Design methodology for aids for the disabled.

Journal of medical engineering & technology·1990

This study models neurones, incorporating association learning and memory. The neurone model demonstrates pattern recognition capabilities, paving the way for advanced adaptive control systems in assistive devices.

Area of Science:

  • Neuroscience
  • Biophysics
  • Robotics

Background:

  • Adaptive control systems are crucial for advanced assistive devices like robotic aids and prostheses.
  • Existing adaptive networks often use simplified neurone models, neglecting key biological features.
  • Neurones possess association learning and memory capabilities, which are vital for sophisticated information processing.

Purpose of the Study:

  • To develop a neurone model that incorporates association learning and memory.
  • To investigate the information processing capabilities of neurones, specifically pattern recognition.
  • To create a simplified algorithm for neurone information processing suitable for computational applications.

Main Methods:

  • Modeled the NMDA-type glutamate receptor using finite difference equations derived from reaction dynamics.

Related Experiment Videos

  • Simulated molecular concentrations within the receptor over time.
  • Developed a whole neurone model with multiple glutamate receptor regions and a simplified algorithmic version.

    • Main Results:

    • Demonstrated association learning occurring at the glutamate receptor level.
    • Showed that a neurone model can recognize patterns of inputs.
    • Created a simplified algorithm capturing essential neurone information processing features.

    Conclusions:

    • Neurone models incorporating learning and memory can exhibit complex information processing, including pattern recognition.
    • The developed model provides a foundation for designing more sophisticated adaptive control systems.
    • A simplified algorithmic model offers a computationally efficient approach to simulating neurone behavior for applications in robotics and prosthetics.