Neuroprostheses show potential in restoring lost sensory function and enhancing human capabilities, but the sensations produced by current devices often seem unnatural or distorted. Exact placement of implants and differences in individual perception lead to significant variations in stimulus response, making personalized stimulus optimization a key challenge. Bayesian optimization could be used to optimize patient-specific stimulation parameters with limited noisy observations, but is not feasible for high-dimensional stimuli. Alternatively, deep learning models can optimize stimulus encoding strategies, but typically assume perfect knowledge of patient-specific variations. Here we propose a novel, practically feasible approach that overcomes both of these fundamental limitations. First, a deep encoder network is trained to produce optimal stimuli for any individual patient by inverting a forward model mapping electrical stimuli to visual percepts. Second, a preferential Bayesian optimization strategy utilizes this encoder to optimize patient-specific parameters for a new patient, using a minimal number of pairwise comparisons between candidate stimuli. We demonstrate the viability of this approach on a novel, state-of-the-art visual prosthesis model. We show that our approach quickly learns a personalized stimulus encoder, leads to dramatic improvements in the quality of restored vision, and is robust to noisy patient feedback and misspecifications in the underlying forward model. Overall, our results suggest that combining the strengths of deep learning and Bayesian optimization could significantly improve the perceptual experience of patients fitted with visual prostheses and may prove a viable solution for a range of neuroprosthetic technologies.

翻译：神经假体在恢复丧失的感觉功能和增强人类能力方面展现出潜力，但当前设备产生的感知往往不自然或失真。植入物的精确定位及个体感知差异导致刺激响应显著变化，使得个性化刺激优化成为关键挑战。贝叶斯优化虽能利用有限的含噪观测数据优化患者特异性刺激参数，却难以适用于高维刺激场景。而深度学习模型虽可优化刺激编码策略，却通常假设对患者特异性变异具有完美先验知识。本文提出一种突破这两项根本性限制的新型实用方法：首先，通过训练深度编码网络逆向建模电刺激至视觉感知的映射关系，为任意个体患者生成最优刺激；其次，采用偏好贝叶斯优化策略，利用该编码器通过最少次候选刺激对比较，为新患者优化特异性参数。我们在最新一代视觉假体模型上验证了该方法的可行性，证明其能快速习得个性化刺激编码器，显著提升视觉恢复质量，并对含噪患者反馈及底层前向模型的设定偏差具有鲁棒性。总体结果表明，融合深度学习与贝叶斯优化的优势可显著改善视觉假体患者的感知体验，并可能成为一系列神经假体技术的可行解决方案。