A bio-inspired spiking neural network encoding color-biased images

Abstract

Color selectivity and color constancy are important properties of human visual system, enabling human not only to distinguish different colors but also to perceive objects' real color invariant of the colorful illumination on them. In order to get a robust and biomimetic color image encoding method for color-biased images, we propose a spiking neural network (SNN) to model how the color selectivity and color constancy appear in human visual cortex. The hierarchical structure of the our SNN is consistent with human visual pathway from retina to secondary visual cortex(V2). The feed-forward connections are structured simulating the single opponent and double opponent receptive fields in cortex, and are simulated using excitatory and inhibitory synaptic connections. Lateral connections in cortex is also employed. Unsupervised learning rule: Spike-Timing-Dependent-Plasticity (STDP) is applied during the network training process under stimuli of natural images. After training, neurons response discriminatively to different color stimuli and the hue map is drawn to show preferred color of every neuron. And the hue map of our network highly ensembles biologically experiment result. Finally color-preferring neurons are used to encode color images in several methods. And classification tests are done using the commonly used SFU Lab dataset, showing the encoding methods are robust to color-biased situations.