Learning to See via Epiretinal Implant Stimulation in silico with Model-Based Deep Reinforcement Learning
Researchers have developed a novel approach using model-based deep reinforcement learning to improve visual prosthetics. The system trains an agent to assemble both isotropic and anisotropic shapes, mimicking phosphenes generated by epiretinal implants, to render intelligible images in a simulated retinal environment. This method aims to enhance visual acuity for individuals with conditions like age-related macular degeneration by more effectively utilizing anisotropic phosphenes, representing a step towards better artificially restored vision. AI
IMPACT This research could lead to more effective visual prosthetics by improving image rendering for patients with retinal degeneration.