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.

利用归一化流条件可逆神经网络来优化视网膜植入刺激，以解决电极阵列低分辨率和对不同神经节细胞类型的缺乏特异性所带来的亚优刺激问题。流式可逆神经网络及其条件扩展相比其他方法（如平凡降采样、线性模型和前馈卷积神经网络），在使用经过生理验证的仿真工具进行各种指标的视觉重建方面，效果更佳。