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Analogue synaptic noise--implications and learning improvements

P J Edwards1, A F Murray

  • 1Department of Electrical Engineering, Edinburgh University, Scotland.

International Journal of Neural Systems
|December 1, 1993
PubMed
Summary
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Adding analogue noise to multilayer perceptron training improves fault tolerance and generalisation. This noise-injection technique enhances learning trajectory and offers benefits for analogue neural very large-scale integration (VLSI) applications.

Area of Science:

  • Artificial Intelligence
  • Machine Learning
  • Computational Neuroscience

Background:

  • Multilayer perceptrons (MLPs) are fundamental to deep learning.
  • Analogue hardware implementations introduce noise, impacting performance.
  • Understanding noise effects is crucial for robust neural network training.

Purpose of the Study:

  • To analyze the impact of analogue noise on synaptic arithmetic during MLP training.
  • To develop a theoretical framework for noise-mediated learning.
  • To investigate noise-injection as a method to improve MLP performance.

Main Methods:

  • Expanding the cost function to incorporate noise-mediated penalty terms.
  • Developing theoretical predictions for noise effects on fault tolerance, generalisation, and learning trajectory.

Related Experiment Videos

  • Conducting extensive simulation experiments on two distinct classification problems.

    • Main Results:

    • Noise-injection demonstrably improves fault tolerance and generalisation ability.
    • The learning trajectory of MLPs is positively influenced by analogue noise.
    • Simulation results strongly substantiate the theoretical predictions.

    Conclusions:

    • Analogue noise, when strategically injected, enhances MLP training and performance.
    • The findings are general for incremental weight adjustment training schemes.
    • The results have significant implications for analogue neural very large-scale integration (VLSI) applications.