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 Experiment Videos

Blind signal processing by the adaptive activation function neurons.

S Fiori1

  • 1Department of Industrial Engineering, University of Perugia, Italy. sfr@unipg.it

Neural Networks : the Official Journal of the International Neural Network Society
|September 15, 2000
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

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

Sort by
Same author

PROMPT to improve speech motor abilities in children with cerebral palsy: a wait-list control group trial protocol.

BMC neurology·2022
Same author

LUNCH-Lung Ultrasound for early detection of silent and apparent aspiratioN in infants and young CHildren with cerebral palsy and other developmental disabilities: study protocol of a randomized controlled trial.

BMC pediatrics·2022
Same author

Automating Quantitative Measures of an Established Conventional MRI Scoring System for Preterm-Born Infants Scanned between 29 and 47 Weeks' Postmenstrual Age.

AJNR. American journal of neuroradiology·2021
Same author

Dissection of DLBCL microenvironment provides a gene expression-based predictor of survival applicable to formalin-fixed paraffin-embedded tissue.

Annals of oncology : official journal of the European Society for Medical Oncology·2019
Same author

Dissection of DLBCL microenvironment provides a gene expression-based predictor of survival applicable to formalin-fixed paraffin-embedded tissue.

Annals of oncology : official journal of the European Society for Medical Oncology·2018
Same author

<i>Reply</i>.

AJNR. American journal of neuroradiology·2017
Same journal

TraNce: Type-aware hypergraph neural network with biological mediators for drug repositioning.

Neural networks : the official journal of the International Neural Network Society·2026
Same journal

Decentralized ADMM for factorization-based Low-rank matrix estimation.

Neural networks : the official journal of the International Neural Network Society·2026
Same journal

Memristive neuromorphic circuit design inspired by the neural mechanisms of conditioned fear.

Neural networks : the official journal of the International Neural Network Society·2026
Same journal

Q-learning based asynchronous Boolean control networks stabilization with data loss.

Neural networks : the official journal of the International Neural Network Society·2026
Same journal

New results on prescribed-time synchronization of complex networks via intermittent control.

Neural networks : the official journal of the International Neural Network Society·2026
Same journal

Variance-constrained multi-view ensemble broad network for imbalanced data.

Neural networks : the official journal of the International Neural Network Society·2026
See all related articles

This study introduces an Information Theory learning approach for neural networks with adaptive activation functions. The method enhances Independent Component Analysis (ICA) efficiency and effectiveness compared to existing techniques.

Area of Science:

  • Computational Neuroscience
  • Machine Learning
  • Information Theory

Background:

  • Neural networks often use fixed activation functions, limiting their adaptability.
  • Information theory principles can guide learning by optimizing output distributions.

Purpose of the Study:

  • To develop an Information Theory-based learning theory for adaptive activation functions in neural units.
  • To enhance Independent Component Analysis (ICA) using a novel neural network structure.

Main Methods:

  • Designed a learning theory to flatten the output probability density function (PDF) of neurons, maximizing output entropy.
  • Implemented a neural network using these adaptive activation function neurons.
  • Tested the network's performance on Independent Component Analysis (ICA) tasks.

Related Experiment Videos

Main Results:

  • The proposed neural algorithm demonstrated effectiveness in ICA problems.
  • Achieved superior results compared to the 'Mixture of Densities' (MOD) technique.
  • Showed improved computational complexity and efficiency.

Conclusions:

  • The Information Theory-based learning approach with adaptive activation functions is effective for neural networks.
  • The new method offers a more computationally efficient alternative for ICA.
  • This work advances adaptive neural network design and information-theoretic learning.