Predicting the Temporal Dynamics of Prosthetic Vision

TL;DR

This paper introduces two computational models that accurately predict the complex temporal dynamics of phosphene perception in retinal prosthesis users, improving understanding and simulation of prosthetic vision over time.

Contribution

The study presents novel models that capture phosphene fading and persistence dynamics, validated on behavioral data from multiple users, advancing prosthetic vision simulation.

Findings

Spectral model achieves state-of-the-art prediction accuracy (r = 0.7).

Models effectively decompose phosphene fading into sinusoidal or exponential components.

Provides a foundation for improving prosthetic vision by understanding temporal phosphene dynamics.

Abstract

Retinal implants are a promising treatment option for degenerative retinal disease. While numerous models have been developed to simulate the appearance of elicited visual percepts ("phosphenes"), these models often either focus solely on spatial characteristics or inadequately capture the complex temporal dynamics observed in clinical trials, which vary heavily across implant technologies, subjects, and stimulus conditions. Here we introduce two computational models designed to accurately predict phosphene fading and persistence under varying stimulus conditions, cross-validated on behavioral data reported by nine users of the Argus II Retinal Prosthesis System. Both models segment the time course of phosphene perception into discrete intervals, decomposing phosphene fading and persistence into either sinusoidal or exponential components. Our spectral model demonstrates state-of-the-art predictions of phosphene intensity over time (r = 0.7 across all participants). Overall, this study lays the groundwork for enhancing prosthetic vision by improving our understanding of phosphene temporal dynamics.