Improvement of Spiking Neural Network with Bit Planes and Color Models

TL;DR

This paper introduces a novel bit plane and color model-based coding method to enhance spiking neural network performance on image tasks without increasing model size, validated through extensive experiments.

Contribution

It presents the first integration of bit plane representation and color models in SNNs to improve accuracy and efficiency.

Findings

Performance gain across multiple datasets

Effective coding strategy without increasing model size

First to consider bit planes and color models in SNNs

Abstract

Spiking neural network (SNN) has emerged as a promising paradigm in computational neuroscience and artificial intelligence, offering advantages such as low energy consumption and small memory footprint. However, their practical adoption is constrained by several challenges, prominently among them being performance optimization. In this study, we present a novel approach to enhance the performance of SNN for images through a new coding method that exploits bit plane representation. Our proposed technique is designed to improve the accuracy of SNN without increasing model size. Also, we investigate the impacts of color models of the proposed coding process. Through extensive experimental validation, we demonstrate the effectiveness of our coding strategy in achieving performance gain across multiple datasets. To the best of our knowledge, this is the first research that considers bit planes and color models in the context of SNN. By leveraging the unique characteristics of bit planes, we hope to unlock new potentials in SNNs performance, potentially paving the way for more efficient and effective SNNs models in future researches and applications.