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

机译：受生物启发的尖刺神经网络，对色彩偏向的图像进行编码

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摘要

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.
机译：颜色选择性和颜色恒定性是人类视觉系统的重要属性，使人类不仅可以区分不同的颜色，而且还可以感知物体上彩色照明的真实颜色不变性。为了获得用于偏色图像的鲁棒且仿生的彩色图像编码方法，我们提出了一个尖峰神经网络（SNN）来建模颜色选择性和颜色恒定性在人的视觉皮层中的显示方式。我们的SNN的层次结构与人类从视网膜到次要视觉皮层（V2）的视觉路径一致。前馈连接的结构模拟了皮质中的单个对手和双对手接收场，并使用兴奋性和抑制性突触连接进行了模拟。皮质中也使用了横向连接。无监督学习规则：在自然图像的刺激下，网络训练过程中应用了“尖峰计时依赖可塑性”（STDP）。训练后，神经元对不同的颜色刺激做出区分性反应，并绘制色相图以显示每个神经元的首选颜色。而且我们网络的色调图高度集成了生物学实验结果。最后，颜色偏爱的神经元通过几种方法用于对彩色图像进行编码。并使用常用的SFU Lab数据集进行了分类测试，显示了编码方法对于偏色情况是可靠的。

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