  In computer vision, different basic blocks are created around different
matrix operations, and models based on different basic blocks have achieved
good results. Good results achieved in vision tasks grants them rationality.
However, these experimental-based models also make deep learning long
criticized for principle and interpretability. Deep learning originated from
the concept of neurons in neuroscience, but recent designs detached natural
neural networks except for some simple concepts. In this paper, we build an
artificial neural network, CVSNet, which can be seen as a computer
implementation for central visual system of the brain. Each block in CVSNet
represents the same vision information as that in brains. In CVSNet, blocks
differs from each other and visual information flows through three independent
pathways and five different blocks. Thus CVSNet is completely different from
the design of all previous models, in which basic blocks are repeated to build
model and information between channels is mixed at the outset. In ablation
experiment, we show the information extracted by blocks in CVSNet and compare
with previous networks, proving effectiveness and rationality of blocks in
CVSNet from experiment side. And in the experiment of object recognition,
CVSNet achieves comparable results to ConvNets, Vision Transformers and MLPs.
