Optimizing Retinal Prosthetic Stimuli with Conditional Invertible Neural Networks

arXiv:2403.04884v1 Announce Type: cross
Abstract: 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 …

abstract arxiv cells cs.cv cs.lg functional however information low networks neural networks restore solution the information type vision