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A Synaptic Pruning-Based Spiking Neural Network for Hand-Written Digits Classification.

Faramarz Faghihi1, Hany Alashwal2, Ahmed A Moustafa3,4

  • 1Machine Listening Lab, University of Bremen, Bremen, Germany.

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Summary

A novel spiking neural network uses synaptic pruning to efficiently learn hand-written digit features from small datasets. This brain-inspired model creates sparse "information channels" for accurate digit classification.

Keywords:
MNIST databaseback-propagationdeep spiking neural networkfeature detectionsynaptic pruning

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Area of Science:

  • Computational Neuroscience
  • Artificial Intelligence
  • Machine Learning

Background:

  • Spiking neural networks (SNNs) offer a biologically plausible alternative to traditional artificial neural networks.
  • Synaptic plasticity and pruning are key mechanisms in biological brain learning and efficiency.
  • Efficient feature extraction from complex datasets like hand-written digits remains a challenge for AI.

Purpose of the Study:

  • To develop and train a novel SNN model inspired by synaptic pruning for hand-written digit feature extraction.
  • To investigate the efficacy of a new learning rule and synaptic pruning for creating specialized information channels.
  • To analyze the impact of feedback inhibition and connectivity rates on network performance.

Main Methods:

  • A three-layer SNN with an output neuron was designed, utilizing a novel learning rule based on pre- and postsynaptic firing rates.
  • Synaptic pruning was implemented to create sparse connection matrices ('information channels') between the first and second layers.
  • The model was trained and tested on the Modified National Institute of Standards and Technology (MNIST) database for digit classification.

Main Results:

  • The developed SNN successfully extracted geometric features of hand-written digits.
  • Synaptic pruning resulted in highly specific 'information channels' for each digit class.
  • The SNN demonstrated effective classification performance with a significantly smaller training dataset compared to conventional deep learning methods.

Conclusions:

  • The proposed SNN model, inspired by synaptic pruning, offers an efficient brain-inspired approach for complex data feature extraction.
  • This method requires minimal training data, highlighting its potential for resource-constrained applications.
  • The study introduces a new class of SNNs for advanced pattern recognition tasks.