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Simulation of visual perception and learning with a retinal prosthesis

James R. Golden, Cordelia Erickson-Davis, Nicolas P. Cottaris, Nikhil Parthasarathy, Fred Rieke, David H. Brainard, View ORCID ProfileBrian A. Wandell, View ORCID ProfileE.J. Chichilnisky
doi: https://doi.org/10.1101/206409
James R. Golden
1Neurosurgery, Ophthalmology & Hansen Experimental Physics Laboratory, Stanford University, Stanford, CA 94305 USA
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Cordelia Erickson-Davis
2School of Medicine, Stanford University, Stanford, CA 94305 USA
6Department of Anthropology, Stanford University, Stanford, CA 94305 USA
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Nicolas P. Cottaris
3Psychology, University of Pennsylvania, Philadelphia, PA 19104 USA
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Nikhil Parthasarathy
1Neurosurgery, Ophthalmology & Hansen Experimental Physics Laboratory, Stanford University, Stanford, CA 94305 USA
7Institute for Computational & Mathematical Engineering, Stanford University, Stanford, CA 94305 USA
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Fred Rieke
4Physiology & Biophysics, University of Washington, Seattle, WA 98105 USA
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David H. Brainard
3Psychology, University of Pennsylvania, Philadelphia, PA 19104 USA
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Brian A. Wandell
5Center for Image Systems Engineering, Stanford University, Stanford, CA 94305 USA
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E.J. Chichilnisky
1Neurosurgery, Ophthalmology & Hansen Experimental Physics Laboratory, Stanford University, Stanford, CA 94305 USA
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  • ORCID record for E.J. Chichilnisky
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Abstract

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 prosthesis patients see. A biologically-informed, extensible computational framework is presented here to predict visual perception and the potential effect of learning with a subretinal prosthesis. The framework relies on linear reconstruction of the 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 activation expected from a subretinal prosthesis in a degenerated retina, as a proxy for inferred visual perception. Several simple observations reveal the potential utility of such a simulation framework. The inferred 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 clinical findings and 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 prosthesis stimulation, simulating idealized learning by the patient, the accuracy of inferred perception was much closer to that of healthy vision. The reconstruction approach thus provides a more complete method for exploring the potential for treating blindness with retinal prostheses than has been available previously. It may also be useful for interpreting patient data in clinical trials, and for improving prosthesis design.

Acknowledgements

We thank Vincent Bismuth and Daniel Palanker for valuable discussions and comments on the manuscript, Nishal Shah for technical assistance and useful input, and Georges Goetz for helpful discussions.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.
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Posted August 31, 2018.
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Simulation of visual perception and learning with a retinal prosthesis
James R. Golden, Cordelia Erickson-Davis, Nicolas P. Cottaris, Nikhil Parthasarathy, Fred Rieke, David H. Brainard, Brian A. Wandell, E.J. Chichilnisky
bioRxiv 206409; doi: https://doi.org/10.1101/206409
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Simulation of visual perception and learning with a retinal prosthesis
James R. Golden, Cordelia Erickson-Davis, Nicolas P. Cottaris, Nikhil Parthasarathy, Fred Rieke, David H. Brainard, Brian A. Wandell, E.J. Chichilnisky
bioRxiv 206409; doi: https://doi.org/10.1101/206409

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