Deep Learning-Based Perceptual Stimulus Encoder for Bionic Vision

• URL: http://arxiv.org/abs/2203.05604v1
• Date: Thu, 10 Mar 2022 19:42:09 GMT
• Title: Deep Learning-Based Perceptual Stimulus Encoder for Bionic Vision
• Authors: Lucas Relic, Bowen Zhang, Yi-Lin Tuan, Michael Beyeler
• Abstract summary: We propose a PSE that is trained in an end-to-end fashion to predict the electrode activation patterns required to produce a desired visual percept. We demonstrate the effectiveness of the encoder on MNIST using a psychophysically validated phosphene model tailored to individual retinal implant users.
• Score: 6.1739856715198
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Retinal implants have the potential to treat incurable blindness, yet the quality of the artificial vision they produce is still rudimentary. An outstanding challenge is identifying electrode activation patterns that lead to intelligible visual percepts (phosphenes). Here we propose a PSE based on CNN that is trained in an end-to-end fashion to predict the electrode activation patterns required to produce a desired visual percept. We demonstrate the effectiveness of the encoder on MNIST using a psychophysically validated phosphene model tailored to individual retinal implant users. The present work constitutes an essential first step towards improving the quality of the artificial vision provided by retinal implants.

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