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Related Experiment Video

Updated: Dec 31, 2025

Analyzing Mitochondrial Morphology Through Simulation Supervised Learning
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Analyzing Mitochondrial Morphology Through Simulation Supervised Learning

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Extreme learning machine for a new hybrid morphological/linear perceptron.

Peter Sussner1, Israel Campiotti2

  • 1Department of Applied Mathematics, University of Campinas, 13083-859, Campinas, SP, Brazil.

Neural Networks : the Official Journal of the International Neural Network Society
|January 1, 2020
PubMed
Summary

This study introduces an extreme learning machine approach to train hybrid morphological/linear perceptrons, overcoming non-differentiability issues common in morphological neural networks (MNNs). The new model shows competitive performance on classification tasks.

Keywords:
ClassificationExtreme learning machineHybrid morphological/linear perceptronLattice computingMathematical morphologyMorphological neural networks

Related Experiment Videos

Last Updated: Dec 31, 2025

Analyzing Mitochondrial Morphology Through Simulation Supervised Learning
12:06

Analyzing Mitochondrial Morphology Through Simulation Supervised Learning

Published on: March 3, 2023

4.6K

Area of Science:

  • Artificial Intelligence
  • Machine Learning
  • Computational Neuroscience

Background:

  • Morphological neural networks (MNNs) utilize mathematical morphology operations at each node.
  • Traditional training methods face challenges due to the non-differentiable nature of MNN aggregation functions.
  • Existing models like morphological perceptrons (MPs) and morphological associative memories are affected by this limitation.

Purpose of the Study:

  • To introduce an extreme learning machine (ELM) approach for training hybrid morphological/linear perceptrons.
  • To address the non-differentiability problem in training MNNs.
  • To evaluate the performance of the proposed ELM-trained model on benchmark classification problems.

Main Methods:

  • Developed an ELM-based training strategy for hybrid morphological/linear perceptrons.
  • Integrated morphological components from established MP models.
  • Applied the model to several standard classification datasets from existing literature.

Main Results:

  • The ELM-trained hybrid morphological/linear perceptron demonstrated effective performance on classification tasks.
  • The model's performance was compared against various related MNNs and hybrid models.
  • Results indicate the viability of the ELM approach for training non-differentiable MNNs.

Conclusions:

  • The proposed ELM approach successfully circumvents the non-differentiability issue in training MNNs.
  • The developed hybrid morphological/linear perceptron offers a competitive alternative for classification problems.
  • This work contributes a novel training methodology for a class of non-differentiable neural networks.