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In computer vision, different basic blocks are created around differentmatrix operations, and models based on different basic blocks have achievedgood results. Good results achieved in vision tasks grants them rationality.However, these experimental-based models also make deep learning longcriticized for principle and interpretability. Deep learning originated fromthe concept of neurons in neuroscience, but recent designs detached naturalneural networks except for some simple concepts. In this paper, we build anartificial neural network, CVSNet, which can be seen as a computerimplementation for central visual system of the brain. Each block in CVSNetrepresents the same vision information as that in brains. In CVSNet, blocksdiffers from each other and visual information flows through three independentpathways and five different blocks. Thus CVSNet is completely different fromthe design of all previous models, in which basic blocks are repeated to buildmodel and information between channels is mixed at the outset. In ablationexperiment, we show the information extracted by blocks in CVSNet and comparewith previous networks, proving effectiveness and rationality of blocks inCVSNet from experiment side. And in the experiment of object recognition,CVSNet achieves comparable results to ConvNets, Vision Transformers and MLPs.

CVSNet: A Computer Implementation for Central Visual System of The Brain