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

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
Optimization Problems01:26

Optimization Problems

Optimization problems often involve identifying maximum or minimum values under specific constraints. A well-known example is determining the longest horizontal pipe that can be moved around a right-angled corner, where a 3-meter-wide hallway meets a 2-meter-wide hallway. This scenario, common in architectural design and industrial transport, can be understood conceptually through geometric and trigonometric reasoning.To visualize the problem, consider the pipe as a straight line that touches...
Methods of Medium Optimization01:28

Methods of Medium Optimization

Optimizing growth media enhances microbial proliferation and maximizes product yield. Statistical experimental design methodologies provide structured and reproducible approaches, offering progressively higher levels of robustness and efficiency.The One-Factor-at-a-Time (OFAT) MethodThe One-Factor-at-a-Time (OFAT) method involves adjusting a single variable while keeping all others constant. However, it cannot detect interactions between variables, often leading to suboptimal outcomes when...
Neural Circuits01:25

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...
Ampere-Maxwell's Law: Problem-Solving01:17

Ampere-Maxwell's Law: Problem-Solving

A parallel-plate capacitor with capacitance C, whose plates have area A and separation distance d, is connected to a resistor R and a battery of voltage V. The current starts to flow at t = 0. What is the displacement current between the capacitor plates at time t? From the properties of the capacitor, what is the corresponding real current?
To solve the problem, we can use the equations from the analysis of an RC circuit and Maxwell's version of Ampère's law.
For the first part of the problem,...
Biot-Savart Law: Problem-Solving00:59

Biot-Savart Law: Problem-Solving

The magnitude and direction of a magnetic field created by a steady current can be calculated using the Biot-Savart law.
Consider a mobile phone battery bank as a source of steady current, which flows through the wire connected between the two. What is the magnitude of the magnetic field created by this current at a field point P?
To estimate the magnitude of the total magnetic field, we first consider a small current element of length dl, at a distance r from the field point. Now the following...

You might also read

Related Articles

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

Sort by
Same author

Simulating climate with a synchronization-based supermodel.

Chaos (Woodbury, N.Y.)·2018
Same author

"FORCE" learning in recurrent neural networks as data assimilation.

Chaos (Woodbury, N.Y.)·2018
Same author

Role of atmosphere-ocean interactions in supermodeling the tropical Pacific climate.

Chaos (Woodbury, N.Y.)·2018
Same author

Introduction to focus issue: Synchronization in large networks and continuous media-data, models, and supermodels.

Chaos (Woodbury, N.Y.)·2018
Same author

A pulse sequence optimization method for assessment of nucleus size in q-space analysis of idealized cells.

Journal of magnetic resonance (San Diego, Calif. : 1997)·2013
Same author

Synchronization of extended systems from internal coherence.

Physical review. E, Statistical, nonlinear, and soft matter physics·2009

Related Experiment Video

Updated: Jun 19, 2026

Preparation of Neuronal Co-cultures with Single Cell Precision
09:06

Preparation of Neuronal Co-cultures with Single Cell Precision

Published on: May 20, 2014

A "cellular neuronal" approach to optimization problems.

Gregory S Duane1

  • 1National Center for Atmospheric Research, Boulder, Colorado 80307, USA. gregory.duane@colorado.edu

Chaos (Woodbury, N.Y.)
|October 2, 2009
PubMed
Summary
This summary is machine-generated.

This study generalizes the Hopfield-Tank neural network for the traveling salesman problem using cellular neural networks and oscillator synchronization. It identifies local optima for shortest tours and suggests chaotic intermittency for global optimization.

More Related Videos

Anatomically Inspired Three-dimensional Micro-tissue Engineered Neural Networks for Nervous System Reconstruction, Modulation, and Modeling
10:45

Anatomically Inspired Three-dimensional Micro-tissue Engineered Neural Networks for Nervous System Reconstruction, Modulation, and Modeling

Published on: May 31, 2017

Related Experiment Videos

Last Updated: Jun 19, 2026

Preparation of Neuronal Co-cultures with Single Cell Precision
09:06

Preparation of Neuronal Co-cultures with Single Cell Precision

Published on: May 20, 2014

Anatomically Inspired Three-dimensional Micro-tissue Engineered Neural Networks for Nervous System Reconstruction, Modulation, and Modeling
10:45

Anatomically Inspired Three-dimensional Micro-tissue Engineered Neural Networks for Nervous System Reconstruction, Modulation, and Modeling

Published on: May 31, 2017

Area of Science:

  • Computational neuroscience
  • Artificial intelligence
  • Complex systems

Background:

  • The Hopfield-Tank neural network is a classic model for solving the traveling salesman problem (TSP).
  • Existing models often struggle with representing all tour permutations efficiently and achieving global optimization.

Purpose of the Study:

  • To generalize the Hopfield-Tank architecture for the traveling salesman problem.
  • To explore tour representation using synchronization patterns in cellular neural networks.
  • To investigate methods for achieving global optimization in TSP solutions.

Main Methods:

  • Generalization of the Hopfield-Tank recurrent neural network to a fully interconnected cellular neural network of regular oscillators.
  • Defining tours through synchronization patterns, enabling simultaneous representation of cyclic tour permutations.
  • Analytical investigation of network convergence to local optima using a stationary phase approximation.
  • Exploring simulated annealing and chaotic intermittency for global optimization.

Main Results:

  • The generalized cellular neural network architecture successfully represents tours via synchronization patterns.
  • Analytical methods confirm convergence to local optima, with some corresponding to shortest-distance tours.
  • Simulated annealing is identified as necessary for global optimization, with chaotic intermittency proposed as an alternative.

Conclusions:

  • The proposed cellular neural network architecture offers a novel approach to solving the traveling salesman problem.
  • Synchronization patterns provide an effective mechanism for representing TSP tours and their permutations.
  • Further research into chaotic oscillators may yield more efficient global optimization strategies for complex combinatorial problems.