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

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...
Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence of...

You might also read

Related Articles

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

Sort by
Same author

Another K-winners-take-all analog neural network.

IEEE transactions on neural networksยท2008
See all related articles

Related Experiment Video

Updated: Jul 7, 2026

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

Performance analysis for a K-winners-take-all analog neural network: basic theory.

C A Marinov1, B D Calvert

  • 1Dept. of Electr. Eng., Polytech. Univ. of Bucharest, Romania.

IEEE Transactions on Neural Networks
|February 2, 2008
PubMed
Summary

This study refines an analog Hopfield-type neural network for repeated list processing. Computable parameter restrictions enable efficient, high-gain processing of K largest components at a specified rate.

Related Experiment Videos

Last Updated: Jul 7, 2026

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

Area of Science:

  • Artificial Neural Networks
  • Computational Neuroscience
  • Analog Computing

Background:

  • Previous work introduced an analog Hopfield-type neural network for identifying K largest components in real number lists.
  • The network's application for repeated list processing requires understanding parameter constraints for sustained operation.

Purpose of the Study:

  • To determine computable restrictions on network parameters for repeated list processing.
  • To establish analytical bounds for processing time based on circuit parameters, list length, and element separation.
  • To enable practical setting of circuit parameters for desired clocking times.

Main Methods:

  • Mathematical analysis to derive analytical bounds for processing time.
  • Identification of computable restrictions on network parameters.
  • Focus on high-gain functioning of individual neurons.
  • Numerical investigations to validate theoretical predictions and assess parameter influence.

Main Results:

  • Analytical bounds for the time required to process lists were derived.
  • Computable restrictions on parameters were identified for repeated list processing.
  • The accuracy of theoretical predictions was confirmed through numerical simulations.
  • The influence of various parameters on network performance was investigated.

Conclusions:

  • The study provides a framework for setting circuit parameters for efficient, repeated list processing using analog Hopfield-type neural networks.
  • The findings facilitate practical implementation of these networks for tasks requiring high-gain, high-speed component identification.
  • The research validates theoretical predictions and offers insights into parameter optimization for enhanced performance.