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pulse2percept: A Python-based simulation framework for bionic vision

Michael Beyeler
Department of Psychology, University of Washington
Institute for Neuroengineering, University of Washington
eScience Institute, University of Washington

Geoffrey M. Boynton
Department of Psychology, University of Washington

Ione Fine
Department of Psychology, University of Washington

Ariel Rokem
eScience Institute, University of Washington
Institute for Neuroengineering, University of Washington

Video: https://github.com/uwescience/pulse2percept

Abstract

By 2020 roughly 200 million people worldwide will suffer from photoreceptor diseases such as retinitis pigmentosa and age-related macular degeneration, and a variety of retinal sight restoration technologies are being developed to target these diseases. One technology, analogous to cochlear implants, uses a grid of electrodes to stimulate remaining retinal cells. Two brands of retinal prostheses are currently approved for implantation in patients with late stage photoreceptor disease. Clinical experience with these implants has made it apparent that the vision restored by these devices differs substantially from normal sight. To better understand the outcomes of this technology, we developed pulse2percept, an open-source Python implementation of a computational model that predicts the perceptual experience of retinal prosthesis patients across a wide range of implant configurations. A modular and extensible user interface exposes the different building blocks of the software, making it easy for users to simulate novel implants, stimuli, and retinal models. We hope that this library will contribute substantially to the field of medicine by providing a tool to accelerate the development of visual prostheses.

Keywords

bionic vision, retinal implant, pulse2percept, prosthesis

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