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 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...

Long-term Potentiation

Long-term Potentiation

Long-term potentiation, or LTP, is one of the ways by which synaptic plasticity—changes in the strength of chemical synapses—can occur in the brain. LTP is the process of synaptic strengthening that occurs over time between pre- and postsynaptic neuronal connections. The synaptic strengthening of LTP works in opposition to the synaptic weakening of long-term depression (LTD) and together are the main mechanisms that underlie learning and memory.

Real-World Application of Classical Conditioning

Real-World Application of Classical Conditioning

Classical conditioning not only includes the initial pairing of stimuli but also extends to more complex forms, such as higher-order conditioning. Higher-order conditioning involves creating associations beyond the primary conditioned stimulus, resulting in a chain of conditioned responses.
Higher-order, or second-order, conditioning occurs when a neutral stimulus becomes associated with an already established conditioned stimulus through repeated pairings. For instance, if a dog has been...

You might also read

Related Articles

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

Sort by

Same author

On an age structured population model with density-dependent dispersals between two patches.

Mathematical biosciences and engineering : MBE·2019

Same author

Within-Host Viral Dynamics in a Multi-compartmental Environment.

Bulletin of mathematical biology·2019

Same author

Chaotic simulated annealing by a neural network with a variable delay: design and application.

IEEE transactions on neural networks·2011

Same author

Impaired frontal synchronization of spontaneous magnetoencephalographic activity in patients with bipolar disorder.

Neuroscience letters·2008

Same author

Detection of synchronization between chaotic signals: An adaptive similarity-based approach.

Physical review. E, Statistical, nonlinear, and soft matter physics·2008

Same author

Mutual-information-based approach for neural connectivity during self-paced finger lifting task.

Human brain mapping·2007

Search research articles

Related Experiment Video

Updated: Jun 19, 2026

Time-dependent Increase in the Network Response to the Stimulation of Neuronal Cell Cultures on Micro-electrode Arrays

Time-dependent Increase in the Network Response to the Stimulation of Neuronal Cell Cultures on Micro-electrode Arrays

Published on: May 29, 2017

Delayed transiently chaotic neural networks and their application.

Shyan-Shiou Chen¹

¹Department of Mathematics, National Taiwan Normal University, Taipei, Taiwan. sschen@ntnu.edu.tw

Chaos (Woodbury, N.Y.)

|October 2, 2009

Summary

A new delayed transiently chaotic neural network (DTCNN) model improves global minimum searches for the traveling salesman problem (TSP). This novel approach demonstrates enhanced performance over traditional methods, highlighting the significance of time delays in neural systems.

More Related Videos

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

Two-Photon Polymerization 3D-Printing of Micro-scale Neuronal Cell Culture Devices

Two-Photon Polymerization 3D-Printing of Micro-scale Neuronal Cell Culture Devices

Published on: June 7, 2024

Related Experiment Videos

Last Updated: Jun 19, 2026

Time-dependent Increase in the Network Response to the Stimulation of Neuronal Cell Cultures on Micro-electrode Arrays

Time-dependent Increase in the Network Response to the Stimulation of Neuronal Cell Cultures on Micro-electrode Arrays

Published on: May 29, 2017

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

Two-Photon Polymerization 3D-Printing of Micro-scale Neuronal Cell Culture Devices

Two-Photon Polymerization 3D-Printing of Micro-scale Neuronal Cell Culture Devices

Published on: June 7, 2024

Area of Science:

Computational neuroscience
Artificial neural networks
Chaos theory

Background:

Transiently chaotic neural networks (TCNNs) are used for optimization problems like the traveling salesman problem (TSP).
Time delays in neural systems can significantly alter dynamical behavior and computational capabilities.

Purpose of the Study:

To introduce a novel delayed transiently chaotic neural network (DTCNN) model.
To numerically verify the DTCNN's superior performance in solving the TSP compared to traditional TCNNs.
To theoretically analyze the stability and chaotic properties of the proposed DTCNN.

Main Methods:

Development of the delayed transiently chaotic neural network (DTCNN) model.
Geometric construction of a transversal homoclinic orbit to prove Marotto's chaos.
Application of LaSalle's invariance principle to analyze the stability of nonautonomous delayed systems.
Numerical simulations to evaluate DTCNN performance on the traveling salesman problem (TSP).

Main Results:

The DTCNN model demonstrates improved performance in finding the global minimum for the traveling salesman problem (TSP).
Theoretical analysis confirms the existence of chaos in the delayed neural network without a cooling schedule.
Stability analysis of nonautonomous delayed systems was successfully performed using LaSalle's invariance principle.

Conclusions:

The proposed DTCNN model offers enhanced capabilities for solving complex optimization problems like the TSP.
The study underscores the critical role of time delays in the behavior and effectiveness of neural network models.
The findings suggest potential for further research into time-delayed systems for advanced neural computation.