A Spiking Neural Network for Visual Color Feature Classification for Pictures with RGB-HSV Model

Abstract

Spiking neural networks (SNNs) are artificial neural network models that are closely mimic natural neural networks. LIF (Leaky Integrate-and-fire) neuron model, population coding and Tempotron supervised learning rules are used to construct a spiking neural network for visual color feature classification based on RGB-HSV (Red, Green, Blue -Hue, Saturation, Value) model. The product of a momentum learning rate and the last weight change is proposed to speed up the training of the SNN. Test results based on a common data set show that the accuracy of the SNN can be up to 90%.