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Design and Implementation of a Spiking Neural Network with Integrate-and-Fire Neuron Model for Pattern Recognition.

Parvaneh Rashvand1, Mohammad Reza Ahmadzadeh1, Farzaneh Shayegh1

  • 1Digital Signal Processing Research Lab, Department of Electrical and Computer Engineering, Isfahan University of Technology, Isfahan 84156-83111, Iran.

International Journal of Neural Systems
|December 23, 2020
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Summary

This study introduces a robust spiking neural network (SNN) inspired by retinal structures. The novel SNN achieves high accuracy on datasets like Iris and MNIST, demonstrating efficient learning and improved performance with feature engineering.

Keywords:
IF neuron modelSpiking neural networksimage recognitionneural codingtraining algorithm

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Area of Science:

  • Computational Neuroscience
  • Artificial Intelligence
  • Machine Learning

Background:

  • Traditional artificial neural networks (ANNs) differ from spiking neural networks (SNNs) in their temporal coding mechanisms.
  • Optimizing SNN parameters based on physiological principles enhances network robustness and information processing.

Purpose of the Study:

  • To implement a robust spiking neural network (SNN) inspired by the center-surround structure of retinal receptive fields.
  • To evaluate the performance of the proposed SNN using the Integrate-and-Fire (IF) neuron model and time-to-first-spike coding.

Main Methods:

  • The proposed SNN utilizes the Integrate-and-Fire (IF) neuron model.
  • Time-to-first-spike coding is employed for network training with a novel learning method.
  • The SNN is evaluated on the Iris, MNIST, and ABIDE1 datasets.

Main Results:

  • Achieved 96.33% accuracy on the Iris dataset with 60 input neurons in 45 iterations.
  • Reached 90.5% accuracy on MNIST with pixel input (600 neurons), improving to 95% with 210 neurons using 14 structural features.
  • Attained 84.42% accuracy on the ABIDE1 dataset for autism classification using Shannon entropy, with 120 iterations.

Conclusions:

  • The implemented SNN demonstrates high accuracy and efficiency across diverse datasets.
  • Feature engineering significantly improves SNN performance and reduces the number of required input neurons.
  • The proposed SNN shows promise for applications in pattern recognition and biomedical data analysis.