Jove
Visualize
Contact Us

Related Concept Videos

Feedback control systems01:26

Feedback control systems

815
Feedback control systems are categorized in various ways based on their design, analysis, and signal types.
Linear feedback systems are theoretical models that simplify analysis and design. These systems operate under the principle that their output is directly proportional to their input within certain ranges. For instance, an amplifier in a control system behaves linearly as long as the input signal remains within a specific range. However, most physical systems exhibit inherent nonlinearity...
815
Hierarchy of Motor Control01:18

Hierarchy of Motor Control

6.9K
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.
6.9K
Neural Control of Respiration01:18

Neural Control of Respiration

6.0K
The neural regulation of respiration is a meticulously coordinated process primarily controlled by the respiratory centers located within the brainstem. These centers, composed of specialized neurons, transmit nerve impulses that control the contraction and relaxation of our respiratory muscles.
Respiratory Centers in the Brainstem
Two primary areas comprise the respiratory center: the medullary respiratory center in the medulla oblongata and the pontine respiratory group in the pons. The...
6.0K
Open and closed-loop control systems01:17

Open and closed-loop control systems

2.1K
Control systems are foundational elements in automation and engineering. They are broadly categorized into open-loop and closed-loop systems. These classifications hinge on the presence or absence of feedback mechanisms, significantly influencing the system's performance, complexity, and application.
An open-loop control system operates without feedback from the output. It consists of two primary elements: the controller and the controlled process. The controller receives an input signal...
2.1K
Neural Regulation01:37

Neural Regulation

45.2K
Digestion begins with a cephalic phase that prepares the digestive system to receive food. When our brain processes visual or olfactory information about food, it triggers impulses in the cranial nerves innervating the salivary glands and stomach to prepare for food.
45.2K
Positive and Negative Feedback Loops01:18

Positive and Negative Feedback Loops

26.6K
Animal organs and organ systems constantly adjust to internal and external changes through a process called homeostasis ("steady state"). Examples of these changes include regulation of the level of glucose or calcium in the blood or internal responses to external temperatures. Homeostasis requires  maintaining an internal dynamic equilibrium:
26.6K

You might also read

Related Articles

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

Sort by
Same author

Coupling radiative, conductive and convective heat-transfers in a single Monte Carlo algorithm: A general theoretical framework for linear situations.

PloS one·2023
Same author

Federated learning for predicting histological response to neoadjuvant chemotherapy in triple-negative breast cancer.

Nature medicine·2023
Same author

Optimising sounds for the driving of sleep oscillations by closed-loop auditory stimulation.

Journal of sleep research·2022
Same author

Dendritic sodium spikes endow neurons with inverse firing rate response to correlated synaptic activity.

Journal of computational neuroscience·2018
Same author

Addressing nonlinearities in Monte Carlo.

Scientific reports·2018
Same author

Slow feature analysis with spiking neurons and its application to audio stimuli.

Journal of computational neuroscience·2016
Same journal

Harmonic memory in phasor neural networks.

Biological cybernetics·2026
Same journal

Correction: Decreased spinal inhibition leads to undiversified locomotor patterns.

Biological cybernetics·2026
Same journal

Foundational issues of network models in biology.

Biological cybernetics·2026
Same journal

Dynamical mechanisms for coordinating long-term working memory based on the precision of spike-timing in cortical neurons.

Biological cybernetics·2026
Same journal

Distinct dopaminergic spike-timing-dependent plasticity rules are suited to different functional roles.

Biological cybernetics·2026
Same journal

Fluctuation-response relations for a two-stage population of spiking neurons stimulated by common noise.

Biological cybernetics·2026
See all related articles
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: Apr 16, 2026

An Experimental Platform to Study the Closed-loop Performance of Brain-machine Interfaces
10:51

An Experimental Platform to Study the Closed-loop Performance of Brain-machine Interfaces

Published on: March 10, 2011

14.4K

Ideomotor feedback control in a recurrent neural network.

Mathieu Galtier1

  • 1Minds, Jacobs University Bremen, Bremen, Germany, mathieu.galtier@gmx.com.

Biological Cybernetics
|March 11, 2015
PubMed
Summary
This summary is machine-generated.

This study introduces a novel neural network architecture for controlling unknown environments, achieving coherent behavior through predictive learning for both perception and action. The approach demonstrates promising performance and biological plausibility for artificial intelligence.

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

11.0K
Force and Position Control in Humans - The Role of Augmented Feedback
06:31

Force and Position Control in Humans - The Role of Augmented Feedback

Published on: June 19, 2016

8.3K

Related Experiment Videos

Last Updated: Apr 16, 2026

An Experimental Platform to Study the Closed-loop Performance of Brain-machine Interfaces
10:51

An Experimental Platform to Study the Closed-loop Performance of Brain-machine Interfaces

Published on: March 10, 2011

14.4K
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

11.0K
Force and Position Control in Humans - The Role of Augmented Feedback
06:31

Force and Position Control in Humans - The Role of Augmented Feedback

Published on: June 19, 2016

8.3K

Area of Science:

  • Computational Neuroscience
  • Artificial Intelligence
  • Machine Learning

Background:

  • Controlling unknown environments is a significant challenge in artificial intelligence.
  • Existing models often struggle with real-time adaptation and biological plausibility.

Purpose of the Study:

  • To present a novel neural network architecture for autonomous control in unknown environments.
  • To implement a biologically plausible ideomotor control mechanism using concurrent learning rules.

Main Methods:

  • Utilized a randomly connected recurrent neural network (RNN).
  • Implemented two concurrent learning rules: one for stimulus prediction (perception) and one for target time-series matching (action).
  • Simultaneously read and fed back perception and action from the RNN.

Main Results:

  • The interaction between the two learning principles led to coherent network behavior.
  • Numerical simulations demonstrated promising performance of the proposed architecture.
  • The approach showed potential for development into a local and biologically plausible algorithm.

Conclusions:

  • The presented neural network architecture effectively controls unknown environments through integrated perception-action learning.
  • The ideomotor control principles offer a promising direction for developing more adaptive and biologically realistic AI systems.
  • The model's performance suggests its suitability for complex robotic and AI applications.