%0 Journal Article %A James R. Golden %A Cordelia Erickson-Davis %A Nicolas P. Cottaris %A Nikhil Parthasarathy %A Fred Rieke %A David H. Brainard %A Brian A. Wandell %A E. J. Chichilnisky %T Simulation of visual perception and learning with a retinal prosthesis %D 2018 %R 10.1101/206409 %J bioRxiv %P 206409 %X The nature of artificial vision with a retinal prosthesis, and the degree to which the brain can adapt to the unnatural input from such a device, are poorly understood. Therefore, the development of current and future devices may be aided by theory and simulations that help to infer and understand what patients see. A biologically-informed computational framework was developed to predict visual perception and the effect of learning with a subretinal prosthesis. The framework relies on reconstruction of the visual stimulus from retinal responses, to infer the visual information available to the patient. A simulation of the physiological optics of the eye and light responses of the major retinal neurons was used to calculate the optimal linear transformation for reconstructing natural images from retinal activity. The result was then used to reconstruct the visual stimulus during the artificial retinal activation expected from a subretinal prosthesis. The inferred visual perception obtained with prosthesis activation was substantially degraded compared to the inferred perception obtained with normal retinal responses, as expected given the limited resolution and lack of cell type specificity of the prosthesis. Consistent with the importance of cell type specificity, reconstruction using only ON cells, and not OFF cells, was substantially more accurate. Finally, when reconstruction was re-optimized for electrical stimulation, simulating learning by the patient, the accuracy of inferred perception with prosthesis stimulation was closer to that of healthy vision. The reconstruction approach could provide a framework for interpreting patient data in clinical trials, and may be useful for improving prosthesis design. %U https://www.biorxiv.org/content/biorxiv/early/2018/03/05/206409.full.pdf