Retina Gap Junction Networks Facilitate Blind Denoising in the Visual Hierarchy

Gap junctions in the retina are electrical synapses, which strength is regulated byambient light conditions. Such tunable synapses are crucial for the denoising function of the early visual system. However, it is unclear that how the plastic gap junction network processes unknown noise, specifically how this process works synergistically with the brain’s higher visual centers. Inspired by the electrically coupled photoreceptors, we develop a computational model of the gap junction filter (G-filter). We show that G-filter is an effective blind denoiser that converts different noise distributions into a similar form. Next, since deep convolutional neural networks (DCNNs) functionally reflect some intrinsic features of the visual cortex, we combine G-filter with DCNNs as retina and ventral visual pathways to investigate the relationship between retinal denoising processing and the brain’s high-level functions. In the image denoising and reconstruction task, G-filter dramatically improve the classic deep denoising convolutional neural network (DnCNN)’s ability to process blind noise. Further, we find that the gap junction strength of the G-filter modulates the receptive field of DnCNN’s output neurons by the Integrated Gradients method. At last, in the image classification task, G-filter strengthens the defense of state-of-the-arts DCNNs (ResNet50, VGG19 and InceptionV3) against blind noise attacks, far exceeding human performance when noise is large. Our results indicate G-filter significantly enhance DCNNs’ ability on various blind denoising tasks, implying an essential role for retina gap junction networks in high-level visual processing. ### Competing Interest Statement The authors have declared no competing interest.