Implantable retinal prostheses offer a promising solution to restore partial vision by circumventing damaged photoreceptor cells in the retina and directly stimulating the remaining functional retinal cells. However, the information transmission between the camera and retinal cells is often limited by the low resolution of the electrode array and the lack of specificity for different ganglion cell types, resulting in suboptimal stimulations. In this work, we propose to utilize normalizing flow-based conditional invertible neural networks to optimize retinal implant stimulation in an unsupervised manner. The invertibility of these networks allows us to use them as a surrogate for the computational model of the visual system, while also encoding input camera signals into optimized electrical stimuli on the electrode array. Compared to other methods, such as trivial downsampling, linear models, and feed-forward convolutional neural networks, the flow-based invertible neural network and its conditional extension yield better visual reconstruction qualities w.r.t. various metrics using a physiologically validated simulation tool.

翻译：植入式视网膜假体提供了一种有前景的解决方案，通过绕过视网膜中受损的光感受器细胞并直接刺激剩余的功能性视网膜细胞，以恢复部分视力。然而，相机与视网膜细胞之间的信息传输常受限于电极阵列的低分辨率以及对不同神经节细胞类型缺乏特异性，从而导致次优的刺激效果。在本工作中，我们提出利用基于归一化流的条件可逆神经网络，以无监督的方式优化视网膜植入物的刺激。这些网络的可逆性使我们能够将其用作视觉系统计算模型的替代品，同时将输入相机信号编码为电极阵列上的优化电刺激。与其他方法（如简单下采样、线性模型和前馈卷积神经网络）相比，基于流的可逆神经网络及其条件扩展在使用生理学验证的仿真工具时，在多种度量指标下均能产生更好的视觉重建质量。