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Modular Spiking Neural Membrane Systems for Image Classification.

Iris Ermini1, Claudio Zandron1

  • 1Dipartimento di Informatica, Sistemistica e Comunicazione, Università degli Studi di Milano-Bicocca, Viale Sarca 336/14 Milano 20126, Italy.

International Journal of Neural Systems
|March 7, 2024
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Summary
This summary is machine-generated.

This study introduces Modular Spiking Neural P (MSNP) systems, a novel bio-inspired approach for complex image classification tasks. MSNP systems effectively manage large numbers of classes by dividing problems into smaller, modular components, demonstrating promising results in accuracy and energy efficiency.

Keywords:
Spiking neural networksimage classification problemsspiking neural membrane systems

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Area of Science:

  • Computational Neuroscience
  • Artificial Intelligence
  • Machine Learning

Background:

  • Spiking Neural P (SNP) systems are third-generation neural networks inspired by biological neurons.
  • SNP systems offer flexible architectures for bio-inspired machine learning algorithms.
  • Handling image classification with numerous classes presents significant structural complexity challenges.

Purpose of the Study:

  • To propose Modular Spiking Neural P (MSNP) systems for image classification problems with a high number of classes.
  • To address the structural complexity of large-scale classification by employing a modular network design.
  • To evaluate the performance of MSNP systems on the Oxford Flowers 102 dataset, considering accuracy and energy consumption.

Main Methods:

  • Developed a novel MSNP system architecture composed of modules, each focusing on a specific class.
  • Trained the MSNP system using the Oxford Flowers 102 dataset, comprising over 8000 images across 102 flower species.
  • Evaluated the model's accuracy and energy consumption.

Main Results:

  • The MSNP system achieved good results on the image classification task.
  • Model performance was found to be sensitive to image quality factors such as frequency, pose variation, centering, and subject visibility.
  • The modular approach effectively managed the complexity of classifying a large number of similar and varied classes.

Conclusions:

  • MSNP systems offer a viable bio-inspired solution for complex, large-scale image classification.
  • The modular design aids in managing network complexity for high-class scenarios.
  • Future work should focus on improving robustness to variations in image quality for enhanced real-world applicability.