Enhanced Image Classification through Customized Convolutional Spiking Neural Network

Ashok Kumar Saini, Rajesh Kumar, Naveen Gehlot, Seema Verma

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Spiking Neural Networks (SNNs) are deemed to provide biological realism. Also, it has more computational power than Artificial Neural Networks (ANNs) due to its utilization of spikes for information transmission and encoding. However, their shallow structures impose structural limitations, restricting the feature extraction capabilities of conventional SNNs. This study aims to improve the feature extraction capability of SNNs by leveraging the proficient feature extraction skills of Convolutional Neural Networks (CNNs). Our proposed model, Customized Convolutional Spiking Neural Network (CCSNN), combines CNN for feature learning with SNNs for cognitive skills. On the Digit-MNIST, Fashion-MNIST, and Letter-MNIST datasets, CCSNN surpasses previous models using fewer neurons and less training data, enhancing the biological realism of image classification models. In this study, CCSNN achieved impressive results on the Digit-MNIST, Fashion-MNIST, and Letter-MNIST datasets, with accuracies of 99.10%, 91.80%, and 99.30%, respectively, compared to conventional SNN.

Original languageEnglish
Title of host publication2024 Parul International Conference on Engineering and Technology, PICET 2024
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9798350369748
DOIs
Publication statusPublished - 2024
Externally publishedYes
Event6th Parul International Conference on Engineering and Technology, PICET 2024 - Vadodara, India
Duration: 3 May 20244 May 2024

Publication series

Name2024 Parul International Conference on Engineering and Technology, PICET 2024

Conference

Conference6th Parul International Conference on Engineering and Technology, PICET 2024
Country/TerritoryIndia
CityVadodara
Period3/05/244/05/24

Keywords

  • Classification
  • Convolutional Neural Network (CNN)
  • MNIST
  • Spiking Neural Network (SNN)

ASJC Scopus subject areas

  • Safety, Risk, Reliability and Quality
  • Health Informatics
  • Fluid Flow and Transfer Processes
  • Artificial Intelligence
  • Computer Science Applications
  • Signal Processing
  • Information Systems and Management
  • Electrical and Electronic Engineering

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