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Auto Deep Spiking Neural Network Design Based on an Evolutionary Membrane Algorithm.

Chuang Liu1, Haojie Wang1

  • 1School of Intelligent Science and Information Engineering, Shenyang University, Shenyang 110044, China.

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|August 27, 2025
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Summary
This summary is machine-generated.

This study introduces an evolutionary membrane algorithm to automate deep spiking neural network (DSNN) architecture design. The novel approach optimizes DSNN models efficiently, improving accuracy and reducing resource consumption.

Keywords:
automatic network designdeep learningdeep spiking neural networkevolutionary membrane algorithmimage classificationneural architecture searchspiking neural network

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Area of Science:

  • Artificial Intelligence
  • Computational Neuroscience
  • Machine Learning

Background:

  • Designing deep spiking neural network (DSNN) architectures is complex and resource-intensive, relying heavily on expert knowledge and iterative manual adjustments.
  • Current methods for DSNN architecture optimization consume significant human and hardware resources due to repeated modifications based on performance.
  • The need for automated and efficient DSNN design strategies is critical for advancing research and engineering applications.

Purpose of the Study:

  • To propose and evaluate an innovative evolutionary membrane algorithm for automating the optimization of deep spiking neural network (DSNN) architectures.
  • To reduce the reliance on manual tuning and expert experience in DSNN design by transforming network architecture into an algorithm search space.
  • To enhance the efficiency of DSNN architecture optimization through an early stopping strategy during performance evaluation.

Main Methods:

  • An evolutionary membrane algorithm was developed to automate the construction and design of DSNN architectures.
  • The DSNN architecture was mapped into the search space of the evolutionary membrane algorithm, exploring hyperparameters and candidate operation blocks.
  • An early stopping strategy was integrated into the performance evaluation phase to minimize time loss.

Main Results:

  • The proposed algorithm effectively explored the impact of hyperparameters and candidate operation blocks for DSNN optimization.
  • Optimal DSNN models were identified and evaluated on the CIFAR-10 and CIFAR-100 datasets.
  • Significant improvements in accuracy were observed compared to existing state-of-the-art methods.

Conclusions:

  • The evolutionary membrane algorithm offers a novel and efficient approach for streamlining DSNN architecture design and optimization.
  • This automated method significantly reduces resource consumption and enhances accuracy in DSNN applications.
  • The study demonstrates the potential of evolutionary algorithms in addressing challenges in automated parameter optimization for deep spiking neural networks.