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Understanding and modeling the function of the neurons and neural systems are primary goal of systems neuroscience. Sparse coding theory demonstrates that the neurons in primary visual cortex form a sparse representation of natural scenes in the viewpoint of statistics. In this paper, we propose a novel sparse coding model based on structural similarity (SS_SC) for natural image feature extraction. The advantage for our model is to be able to preserve structural information from a scene, whichdoi:10.1109/icassp.2010.5495707 dblp:conf/icassp/LiSLS10a fatcat:6ybejmshivdpnol7oalgyupjwe