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Scientists and experts have explored the mechanism of animal visual systems for decades for smart image processing and pattern recognition in order to satisfy sophisticated engineering applications. An application of image matching in navigation shows that the animal visual system method is at least 5% better than those classical methods. We apply independent component analysis (ICA) unsupervised learning to demonstrate the pre-attentive simple cells in animal vision. Our results confirm Blakemore and Cooper's (1970) experimental results about the growth of simple cells in the cat V1 area. Furthermore, by applying ICA methodology and the simplex algorithm, unsupervised neural synapse learning could simulate receptive fields in the visual cortex and growth of the visual cortex of a young animal in a special environment. These findings imply that an input image could be efficiently represented by ICA bases.