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Automatic extraction of distinctive features from a visual information stream is challenging due to the large amount of information contained in most image data. In recent years deep neural networks (DNNs) have gained outstanding popularity for solving visual information processing tasks. This study reports novel contributions, including a new DNN architecture and training method, which increase the fidelity of DNN-based representations to encodings extracted by visual processing neurons. Ourdoi:10.1016/j.ins.2016.02.034 fatcat:f3yuzlpto5fjdo5pqn23hg2k6q