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The convergence analysis of SpikeProp algorithm with smoothing L1∕2 regularization.

Junhong Zhao1, Jacek M Zurada2, Jie Yang3

  • 1School of Mathematical Sciences, Dalian University of Technology, Dalian 116024, China.

Neural Networks : the Official Journal of the International Neural Network Society
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PubMed
Summary

This study introduces a novel SpikeProp algorithm for spiking neural networks (SNNs) using L1/2 regularization. This method enhances training efficiency and network sparsity for improved brain-inspired computing.

Keywords:
ConvergenceSmoothing regularizationSparsitySpikePropSpiking neural networks

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Area of Science:

  • Computational Neuroscience
  • Artificial Intelligence
  • Machine Learning

Background:

  • Spiking Neural Networks (SNNs) mimic the human brain, incorporating temporal dynamics beyond traditional ANNs.
  • SNNs offer biological plausibility but face computational challenges during training.
  • Existing training methods for SNNs can be computationally intensive.

Purpose of the Study:

  • To present a novel, computationally efficient training algorithm for SNNs.
  • To improve the sparsity and reduce the complexity of SNNs.
  • To demonstrate the convergence and generalization capabilities of the proposed algorithm.

Main Methods:

  • Introduced a SpikeProp algorithm with a smoothing L1/2 regularization term in the error function.
  • Developed a mathematical proof for the algorithm's convergence under specific conditions.
  • Evaluated the algorithm's performance on benchmark datasets: XOR, Iris, and Wisconsin Breast Cancer classification.

Main Results:

  • The L1/2 regularization promotes network sparsity by reducing smaller weights.
  • The algorithm demonstrated proven convergence under reasonable assumptions.
  • Empirical testing showed effective convergence speed, rate, and generalization on classification tasks.

Conclusions:

  • The proposed SpikeProp algorithm offers an efficient approach to training SNNs.
  • The L1/2 regularization effectively induces sparsity, potentially reducing computational load.
  • The algorithm shows promise for practical applications in brain-inspired AI.