Real-Time Optimization of Retinal Ganglion Cell Spatial Activity in Response to Epiretinal Stimulation

Retinal prostheses aim to improve visual perception in patients blinded by photoreceptor degeneration. However, shape and letter perception with these devices is currently limited due to low spatial resolution. Previous research has shown the retinal ganglion cell (RGC) spatial activity and phosphene shapes can vary due to the complexity of retina structure and electrode-retina interactions. Visual percepts elicited by single electrodes differ in size and shapes for different electrodes within the same subject, resulting in interference between phosphenes and an unclear image. Prior work has shown that better patient outcomes correlate with spatially separate phosphenes. In this study we use calcium imaging, in vitro retina, neural networks (NN), and an optimization algorithm to demonstrate a method to iteratively search for optimal stimulation parameters that create focal RGC activation. Our findings indicate that we can converge to stimulation parameters that result in focal RGC activation by sampling less than 1/3 of the parameter space. A similar process implemented clinically can reduce time required for optimizing implant operation and enable personalized fitting of retinal prostheses.

Automated summary

Retinal prostheses aim to improve visual perception in patients with photoreceptor degeneration-induced blindness, but the current limitations in shape and letter perception are due to low spatial resolution. Research has shown that better patient outcomes are correlated with spatially separate phosphenes. By using an optimization algorithm to find optimal stimulation parameters for focal RGC activation, the time required for optimizing implant operation can be reduced.