Predicting Visual Outcomes for Visual Prostheses

A major outstanding challenge is predicting what people “see” when they use their devices.

Instead of seeing focal spots of light, current visual implant users perceive highly distorted percepts, which vary in shape not just across subjects but also across electrodes and often fail to assemble into more complex percepts. Furthermore, phosphenes appear fundamentally different depending on whether they are generated with retinal or cortical implants.

The goal of this project is thus to combine psychophysical and neuroanatomical data that can inform phosphene models capable of linking electrical stimulation directly to perception.

Project Team

Project Lead:

Jacob Granley

PhD Student

Project Affiliates:

Ashley Bruce

MS Student

Yuchen Hou

Research Assistant

Ryan Neydavood

Junior Specialist

Bill Nguyen

Honors Student

Principal Investigator:

Michael Beyeler

Assistant Professor

Collaborators:

Gislin Dagnelie

Associate Professor
Johns Hopkins University

James D. Weiland

Professor
University of Michigan, Ann Arbor

Sandra Rocio Montezuma

Associate Professor
University of Minnesota

Eduardo Fernández Jover

Professor
Universidad Miguel Hernández, Spain

Consultant:

Project Funding

R00EY029329: Virtual prototyping for retinal prosthesis patients
PI: Michael Beyeler (UCSB)

September 2020 - August 2023
National Eye Institute (NEI)
National Institutes of Health (NIH)

Publications

We show that sighted individuals can learn to adapt to the unnatural on- and off-cell population responses produced by electronic and optogenetic sight recovery technologies.

We present a phenomenological model that predicts phosphene appearance as a function of stimulus amplitude, frequency, and pulse duration.

We present an explainable artificiall intelligence (XAI) model fit on a large longitudinal dataset that can predict electrode deactivation in Argus II.

We systematically explored the space of possible implant configurations to make recommendations for optimal intraocular positioning of Argus II.

We show that the perceptual experience of retinal implant users can be accurately predicted using a computational model that simulates each individual patient’s retinal ganglion axon pathways.

The goal of this review is to summarize the vast basic science literature on developmental and adult cortical plasticity with an emphasis on how this literature might relate to the field of prosthetic vision.

pulse2percept is an open-source Python simulation framework used to predict the perceptual experience of retinal prosthesis patients across a wide range of implant configurations.

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