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

An efficient training algorithm for dynamic synapse neural networks using trust region methods.

Hassan H Namarvar1, Theodore W Berger

  • 1Department of Biomedical Engineering, University of Southern California, OHE-500, Los Angeles, CA 90089-1451, USA. heidarin@usc.edu

Neural Networks : the Official Journal of the International Neural Network Society
|July 10, 2003
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

Distributed Temporal Coding of Visual Memory Categories in Human Hippocampal Neurons Revealed by an Interpretable Decoding Model.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)·2025
Same author

Distributed Temporal Coding of Visual Memory Categories in Human Hippocampal Neurons.

Research square·2024
Same author

Developing a hippocampal neural prosthetic to facilitate human memory encoding and recall of stimulus features and categories.

Frontiers in computational neuroscience·2024
Same author

Corrigendum: Patterned hippocampal stimulation facilitates memory in patients with a history of head impact and/or brain injury.

Frontiers in human neuroscience·2022
Same author

Patterned Hippocampal Stimulation Facilitates Memory in Patients With a History of Head Impact and/or Brain Injury.

Frontiers in human neuroscience·2022
Same author

Accelerating input-output model estimation with parallel computing for testing hippocampal memory prostheses in human.

Journal of neuroscience methods·2022
Same journal

Exploiting audio-visual modalities in videos: Object detection via multi-stage bilateral coupling network.

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

Reliability-aware modality completion with cross-modal distillation for federated learning with missing modalities.

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

IGFD-Net: Illumination-guided frequency decoupling for polarization image fusion.

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

Multiple-Strategies dung beetle optimizer and its applications in engineering optimization and bankruptcy prediction.

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

Aggregating global-scale pixel-wise forgery cues within a graph.

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

Finite-Time intermittent control for secure synchronization of Neutral-Type stochastic delayed neural networks under aperiodic DoS attacks.

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

We developed a dynamic synapse neural network model using averaged neuron activity. Our trust region optimization method trains large-scale networks more effectively than genetic algorithms.

Area of Science:

  • Computational Neuroscience
  • Artificial Intelligence

Background:

  • Traditional neural networks often lack dynamic synaptic plasticity.
  • Modeling large-scale neural networks presents significant computational challenges.

Purpose of the Study:

  • To formulate a dynamic synapse neural network model based on local neuronal population activity.
  • To introduce and evaluate a novel trust region nonlinear optimization approach for training these networks.

Main Methods:

  • Formulation of a dynamic synapse neural network model.
  • Application of trust region nonlinear optimization for network training.
  • Comparative analysis against genetic algorithms for large-scale network optimization.

Main Results:

Related Experiment Videos

  • The proposed trust region optimization effectively trains dynamic synapse neural networks.
  • Demonstrated superior performance of the new learning method compared to genetic algorithms on large-scale networks.

    • Conclusions:

    • The trust region nonlinear optimization approach offers an effective and efficient method for training dynamic synapse neural networks.
    • This formulation advances the development of biologically plausible and scalable artificial neural networks.