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觉往往不自然或失真。植入物的精确位置和个体感知差异导致刺激响应存在显著变化，使得个性化刺激优化成为关键挑战。贝叶斯优化可用于在有限噪声观测下优化患者特异性刺激参数，但该方法不适用于高维刺激。另一方面，深度学习模型可以优化刺激编码策略，但通常假设对患者特异性差异具有完美认知。本文提出一种新颖且实际可行的方法，克服了上述两个根本性限制。首先，通过逆向将电刺激映射到视觉感知的前向模型，训练深度编码器网络为任何个体患者生成最优刺激。其次，利用偏好贝叶斯优化策略，通过候选刺激间最少的成对比较，为新患者优化其特异性参数。我们在最新的先进视觉假体模型上验证了该方法的可行性。结果表明，该方法能快速学习个性化刺激编码器，显著提升恢复视觉质量，并对噪声患者反馈及底层前向模型的错误设定具有鲁棒性。总体而言，我们的研究结果表明，结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，并可能成为多种神经假体技术的可行解决方案。