Visual Prosthesis

Visual prostheses aim to restore sight to the blind by electrically stimulating the visual cortex, but current devices produce limited and distorted vision. Research focuses on improving the quality of perceived images through advanced stimulus encoding strategies, employing deep learning models like neural autoencoders and Bayesian optimization to personalize stimulation parameters and predict perceptual outcomes based on brain-like convolutional neural networks. These efforts leverage computational models of the visual pathway to optimize implant design and improve the resolution and field of view, ultimately aiming to enhance the visual acuity and naturalness of prosthetic vision. This work has significant implications for both understanding the neural code of vision and developing effective treatments for blindness.