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Back-Propagation Learning in Deep Spike-By-Spike Networks.

David Rotermund1, Klaus R Pawelzik1

  • 1Institute for Theoretical Physics, University of Bremen, Bremen, Germany.

Frontiers in Computational Neuroscience
|August 29, 2019
PubMed
Summary
This summary is machine-generated.

A new learning rule for Spike-by-Spike (SbS) networks bridges the gap between artificial neural networks (ANNs) and real brains. This advance enables high-performance deep SbS networks for technical applications and neuroscience research.

Keywords:
compressed sensing (CS)deep network (DN)error back propagation (BP) neural networksparsenessspiking network model

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Area of Science:

  • Computational Neuroscience
  • Machine Learning
  • Artificial Intelligence

Background:

  • Artificial neural networks (ANNs) utilize continuous signals, unlike biological brains that use discrete action potentials.
  • Bridging this gap is crucial for developing more brain-like computational models.

Purpose of the Study:

  • To develop a learning rule for optimizing deep Spike-by-Spike (SbS) networks.
  • To enable SbS networks to perform complex tasks comparable to traditional ANNs.

Main Methods:

  • Derivation of a novel learning rule for feed-forward SbS networks.
  • Investigation of the learning rule's properties through simulations.
  • Application of the learning rule to a Deep Convolutional SbS network.

Main Results:

  • The derived learning rule optimizes weight sets for deep SbS networks.
  • A Deep Convolutional SbS network achieved ~99.3% accuracy on MNIST handwritten digit classification.
  • Performance approaches ANN benchmarks without extensive parameter tuning.

Conclusions:

  • The developed learning rule provides a viable method for training deep SbS networks.
  • This approach offers a new foundation for neuroscience research and technical applications.
  • Potential for implementation on specialized computational hardware.