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Evolving artificial neural networks with feedback.

Sebastian Herzog1, Christian Tetzlaff1, Florentin Wörgötter1

  • 1Third Institute of Physics, Universität Göttingen, Friedrich-Hund Platz 1, 37077 Göttingen, Germany; Bernstein Center for Computational Neuroscience, Friedrich-Hund Platz 1, 37077 Göttingen, Germany.

Neural Networks : the Official Journal of the International Neural Network Society
|December 25, 2019
PubMed
Summary
This summary is machine-generated.

Artificial neural networks benefit from incorporating feedback connections, inspired by the brain. This study introduces a novel method to add feedback, significantly enhancing network performance and learning capabilities.

Keywords:
Convolutional neural networkDeep learningFeedbackTransfer entropy

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Area of Science:

  • Artificial Intelligence
  • Computational Neuroscience

Background:

  • Biological neural networks feature a high proportion of feedback connections, typically with small synaptic weights.
  • The integration of feedback mechanisms into artificial neural networks remains underexplored.

Purpose of the Study:

  • To investigate the impact of feedback connections on artificial neural network performance.
  • To develop a method for identifying and implementing feedback connections in deep networks.

Main Methods:

  • Utilized transfer entropy to identify feedback candidates within the feed-forward paths of deep networks.
  • Employed genetic programming to determine optimal synaptic weights for feedback connections.
  • Validated the approach on 36,000 configurations of small conventional neural networks for a non-linear classification task.

Main Results:

  • Added approximately 70% more connections to convolutional layers, all with small weights.
  • Achieved substantial performance improvements across various standard benchmark tasks and network architectures.
  • Demonstrated that feedback consistently reduces total entropy, leading to performance increases.

Conclusions:

  • The proposed method for introducing feedback connections offers a novel approach to enhance artificial neural network learning.
  • This technique can supplement existing methods like error backpropagation, introducing a new dimension to network training.
  • The findings suggest feedback connections are a generic mechanism for improving network efficiency and performance.