  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.
