Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Neural Regulation

Neural Regulation

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.

Physiology of Respiration II: Neurogenic Control of Respiration

Physiology of Respiration II: Neurogenic Control of Respiration

The neurogenic control of respiration coordinates various neural networks and pathways to regulate breathing rate and depth, meeting the body's oxygen and carbon dioxide exchange requirements. This system adapts to physiological and environmental conditions, ensuring optimal breathing patterns.
Central Control
The brainstem is the primary site of central control, hosting respiratory centers:

Neural Control of Respiration

Neural Control of Respiration

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...

Neural Circuits

Neural Circuits

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...

Control Systems

Control Systems

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...

Time-Domain Interpretation of PD Control

Time-Domain Interpretation of PD Control

Proportional-Derivative (PD) control is a widely used control method in various engineering systems to enhance stability and performance. In a system with only proportional control, common issues include high maximum overshoot and oscillation, observed in both the error signal and its rate of change. This behavior can be divided into three distinct phases: initial overshoot, subsequent undershoot, and gradual stabilization.
Consider the example of control of motor torque. Initially, a positive...

You might also read

Related Articles

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

Sort by

Same journal

Universal perceptron and DNA-like learning algorithm for binary neural networks: LSBF and PBF implementations.

IEEE transactions on neural networks·2013

Same journal

Guest editorial: special section on white box nonlinear prediction models.

IEEE transactions on neural networks·2011

Same journal

Data-based fault-tolerant control of high-speed trains with traction/braking notch nonlinearities and actuator failures.

IEEE transactions on neural networks·2011

Same journal

Guest editorial: special section on data-based control, modeling, and optimization.

IEEE transactions on neural networks·2011

Same journal

Neural network-based multiple robot simultaneous localization and mapping.

IEEE transactions on neural networks·2011

Same journal

Data-driven model-free adaptive control for a class of MIMO nonlinear discrete-time systems.

IEEE transactions on neural networks·2011

Search research articles

Related Experiment Video

Updated: Jul 7, 2026

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

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

Published on: March 2, 2015

Optimal control of terminal processes using neural networks.

¹Los Alamos Nat. Lab., NM.

IEEE Transactions on Neural Networks

|January 1, 1996

Summary

This study introduces time-optimal backpropagation through time (BPTT), an extension to neural network training methods. It effectively addresses minimum-time control problems by incorporating trajectory time into cost functions for enhanced controller design.

Area of Science:

Control Theory
Machine Learning
Artificial Intelligence

Background:

Feedforward neural networks approximate functions and implement nonlinear controllers.
Standard backpropagation-through-time (BPTT) training methods are limited in terminal control problems with time-dependent cost functions.

Purpose of the Study:

To extend BPTT for handling terminal control problems where trajectory time is part of the cost function.
To reformulate controller design as a constrained optimization problem incorporating trajectory time.

Main Methods:

Developed an extension to BPTT, termed time-optimal backpropagation through time.
Derived necessary first-order stationary conditions including transversality conditions.
Reformulated controller design as a constrained optimization problem.

More Related Videos

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

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

Published on: May 8, 2021

Related Experiment Videos

Last Updated: Jul 7, 2026

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

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

Published on: March 2, 2015

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

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

Published on: May 8, 2021

Main Results:

The proposed method addresses limitations of standard BPTT in minimum-time control.
New gradient algorithm effectively incorporates trajectory time into the cost function.
Demonstrated effectiveness on benchmark minimum-time control problems.

Conclusions:

The time-optimal BPTT algorithm enhances neural network-based controller design for minimum-time objectives.
This extension provides a viable solution for complex control problems with time constraints.
The method shows promise for advanced applications in robotics and autonomous systems.