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Brain-inspired chaotic backpropagation for MLP.

Peng Tao1, Jie Cheng2, Luonan Chen3

  • 1Key Laboratory of Systems Health Science of Zhejiang Province, School of Life Science, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Hangzhou 310024, China; Key Laboratory of Systems Biology, Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, Shanghai 200031, China.

Neural Networks : the Official Journal of the International Neural Network Society
|August 26, 2022
PubMed
Summary
This summary is machine-generated.

The chaotic backpropagation (CBP) algorithm enhances deep learning by integrating neural chaos, overcoming local minima issues common in the standard backpropagation (BP) algorithm for better optimization and generalization.

Keywords:
Chaotic neural networkError backpropagationGlobal optimizationMultilayer perception

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Area of Science:

  • Artificial Intelligence
  • Computational Neuroscience

Background:

  • The standard backpropagation (BP) algorithm is fundamental to deep learning but often gets trapped in local minima due to its gradient dynamics.
  • Real brain learning may leverage chaotic dynamics, suggesting potential improvements for artificial neural networks.

Purpose of the Study:

  • To introduce the chaotic backpropagation (CBP) algorithm, inspired by biological neural processes.
  • To enhance the optimization and generalization capabilities of deep learning models.

Main Methods:

  • Integrating intrinsic neuronal chaos into the BP algorithm to create CBP.
  • Validating CBP performance on datasets like cifar10 using multilayer perceptrons (MLP).

Main Results:

  • CBP demonstrates significantly improved optimization and generalization compared to BP and its variants.
  • Validation on datasets like cifar10 confirms CBP's superior performance for MLPs.

Conclusions:

  • CBP offers a generalized form of BP with global searching abilities, inspired by brain learning.
  • CBP has the potential to complement or replace BP in deep learning and offers insights into brain learning mechanisms.