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 Concept Videos

Machines: Problem Solving II01:30

Machines: Problem Solving II

791
Machines are complex structures consisting of movable, pin-connected multi-force members that work together to transmit forces. Consider a lifting tong carrying a 100 kg load. It comprises movable sections DAF and CBG linked together with member AB.
791
Machines: Problem Solving I01:22

Machines: Problem Solving I

841
A toggle clamp is a mechanical device commonly used for holding and clamping objects in various applications, such as woodworking, metalworking, and assembly operations. Consider a toggle clamp subjected to a force of 200 N at the handle. The vertical clamping force can be calculated, provided the dimensions of the toggle clamp are known.
The toggle clamp system is a machine structure consisting of movable, pin-connected multi-force members that form a stabilized system to transmit forces. The...
841
Machines01:19

Machines

1.2K
Machines are complex structures consisting of movable, pin-connected multi-force members that work together to transmit forces. One example of a machine is the cutting plier, which is used to cut wires by applying forces to its handles. When equal and opposite forces are exerted on the handles of the cutting plier, they cause the cutting edges to come together and apply equal and opposite reaction forces on the wire, which are greater than the applied forces.
A free-body diagram of the...
1.2K
Control Systems01:10

Control Systems

1.7K
Control systems are everywhere in contemporary society, influencing diverse applications from aerospace to automated manufacturing. These systems can be found naturally within biological processes, such as blood sugar regulation and heart rate adjustment in response to stress, as well as in man-made systems like elevators and automated vehicles. A control system is essentially a network of subsystems and processes that collaboratively convert specific inputs into desired outputs.
At the heart...
1.7K
Neural Circuits01:25

Neural Circuits

3.0K
Neural circuits and neuronal pools are two of the main structures found in the nervous system. Neural circuits are networks of neurons that work together to carry out a specific task or process. They consist of interconnected neurons and glial cells, which provide structural and metabolic support.
Neuronal pools are collections of nerve cells with similar functions and interact through chemical and electrical signals. These pools include both interneurons (the central neural circuit nodes that...
3.0K
Hierarchy of Motor Control01:18

Hierarchy of Motor Control

5.6K
The hierarchy of motor control refers to the different levels of organization and processing involved in controlling movement in the body. These levels range from higher cortical areas involved in planning and decision-making to lower spinal cord reflexes that respond automatically to external stimuli.
5.6K

You might also read

Related Articles

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

Sort by
Same author

Self-supervised learning reduces label noise in sharp wave ripple classification.

Scientific reports·2025
Same author

Recognition of post-learning alteration of hippocampal ripples by convolutional neural network differs in the wild-type and AD mice.

Scientific reports·2021
Same author

Adaptive Tracking Control for Robots With an Interneural Computing Scheme.

IEEE transactions on neural networks and learning systems·2017
Same author

Decirculation process in neural network dynamics.

IEEE transactions on neural networks and learning systems·2014
Same journal

Intervention Feasible Region and Driver Risk Capacity Aware Human-Machine Collaborative Safe Trajectory Planning.

IEEE transactions on neural networks and learning systems·2026
Same journal

A Unified Differential Denoising Learning Framework With a Pre-Trained Model and Fuzzy Graph Networks for Drug-Drug Interaction Prediction.

IEEE transactions on neural networks and learning systems·2026
Same journal

Self-Supervised Continuous Dynamic Graph Representation Learning via Hawkes Processes.

IEEE transactions on neural networks and learning systems·2026
Same journal

cPU: Consistent Risk Estimator for Positive-Unlabeled Learning.

IEEE transactions on neural networks and learning systems·2026
Same journal

Tuning-Free Latent Diffusion Models for Ultrahigh-Resolution Image Editing.

IEEE transactions on neural networks and learning systems·2026
Same journal

Hidden Data Recovery and Forecasting via Next-Generation Reservoir Computing With Multiscale Delay Selection.

IEEE transactions on neural networks and learning systems·2026
See all related articles

Related Experiment Video

Updated: Apr 30, 2026

Real-Time Proxy-Control of Re-Parameterized Peripheral Signals using a Close-Loop Interface
11:54

Real-Time Proxy-Control of Re-Parameterized Peripheral Signals using a Close-Loop Interface

Published on: May 8, 2021

4.2K

Operator control of interneural computing machines.

Mau-Hsiang Shih, Feng-Sheng Tsai

    IEEE Transactions on Neural Networks and Learning Systems
    |May 9, 2014
    PubMed
    Summary
    This summary is machine-generated.

    Researchers developed an interneural computing machine (INCM) using plastic connections. This machine mimics neural population dynamics, generating rhythmic and multiphasic activity patterns through activity-dependent plasticity.

    More Related Videos

    Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
    11:18

    Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

    Published on: March 2, 2015

    11.5K
    A Fully Automated and Highly Versatile System for Testing Multi-cognitive Functions and Recording Neuronal Activities in Rodents
    09:13

    A Fully Automated and Highly Versatile System for Testing Multi-cognitive Functions and Recording Neuronal Activities in Rodents

    Published on: May 3, 2012

    15.0K

    Related Experiment Videos

    Last Updated: Apr 30, 2026

    Real-Time Proxy-Control of Re-Parameterized Peripheral Signals using a Close-Loop Interface
    11:54

    Real-Time Proxy-Control of Re-Parameterized Peripheral Signals using a Close-Loop Interface

    Published on: May 8, 2021

    4.2K
    Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
    11:18

    Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

    Published on: March 2, 2015

    11.5K
    A Fully Automated and Highly Versatile System for Testing Multi-cognitive Functions and Recording Neuronal Activities in Rodents
    09:13

    A Fully Automated and Highly Versatile System for Testing Multi-cognitive Functions and Recording Neuronal Activities in Rodents

    Published on: May 3, 2012

    15.0K

    Area of Science:

    • Computational Neuroscience
    • Artificial Intelligence
    • Neuro-inspired Computing

    Background:

    • Motor cortex generates complex movements via rhythmic, oscillatory neural signals.
    • Mimicking these dynamic neural patterns is key for advanced computing architectures.
    • Existing computing machines lack the flexibility of biological neural systems.

    Purpose of the Study:

    • To design and control a novel computing machine inspired by neural population dynamics.
    • To investigate activity-dependent plasticity for network structure modification.
    • To enable the machine to generate rhythmic and multiphasic activity patterns.

    Main Methods:

    • Designed an interneural computing machine (INCM) with random, plastic connections.
    • Developed a mechanical method to measure collective neural ensemble firing.
    • Derived two plasticity operators (synchronous and self-sustaining activity measures) for network modification.

    Main Results:

    • Plasticity operators enabled activity-dependent modification of the INCM network structure.
    • The operator control mechanism successfully modified neural population dynamics.
    • The INCM demonstrated the ability to produce rhythmic, oscillatory activity and multiphasic population responses.

    Conclusions:

    • The developed interneural computing machine (INCM) effectively mimics biological neural population dynamics.
    • Activity-dependent plasticity, guided by operator control, is crucial for generating complex neural patterns.
    • This approach offers a pathway for designing new computing machines inspired by neurobiology.