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Mimicking the human ear on the basis of auditory models has become a viable approach in many applications by now. However, only a few attempts have been made to extend the scope of physiological ear models to be employed in cochlear implants (CI). Contemporary CI systems rely on much simpler filter banks and simulate the natural signal processing of a healthy cochlea to only a very limited extent. When looking at rehabilitation outcomes, current systems seem to have reached their peak potential, which signals the need for better algorithms and/or technologies. In this paper, we present a novel sound processing strategy, SAM (Stimulation based on Auditory Modeling), that is based on neurophysiological models of the human ear and can be employed in auditory prostheses. It incorporates active cochlear filtering (basilar membrane and outer hair cells) along with the mechanoelectrical transduction of the inner hair cells, so that several psychoacoustic phenomena are accounted for inherently. Although possible, current implementation does not make use of parallel stimulation of the electrodes, which matches state-of-the-art CI hardware. This paper elaborates on SAM's signal processing and provides a computational evaluation of the strategy. Results show that aspects of normal cochlear processing that are missing in common strategies can be replicated by SAM. This is supposed to improve overall CI user performance, which we have at least partly proven in a pilot study with implantees.