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.

翻译：视网膜植入物是治疗退行性视网膜疾病的一种有前景的方案。尽管已有大量模型被开发用于模拟诱发视觉感知（即“光幻视”）的外观，但这些模型往往仅关注空间特性，或未能充分捕捉临床试验中观察到的复杂时间动态——这些动态会因植入体技术、受试者个体差异及刺激条件的不同而呈现显著差异。本文提出了两种计算模型，旨在精确预测不同刺激条件下光幻视的消退与持续现象，并通过九名Argus II视网膜假体系统使用者的行为数据进行交叉验证。两种模型将光幻视感知的时间进程划分为离散区间，将消退与持续过程分解为正弦分量或指数分量。其中，谱模型在预测光幻视强度随时间变化方面达到了当前最优性能（所有受试者的斯皮尔曼相关系数r=0.7）。总体而言，本研究通过深化对光幻视时间动态的理解，为增强假体视觉功能奠定了基础。