Abstract
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.
Highlights
Retinal implants help improve functional vision for patients blinded by retinal degenerative diseases such as age-related macular degeneration and retinitis pigmentosa [1]–[3]
We developed neural network (NN) models of retinal ganglion cells (RGC) spatial activity and a real-time optimization method to search for stimulation parameters that elicit focal responses from in vitro retina
The performance of neural networks (NN) was quantified as the mean squared error (MSE) between the learned objective function maps and the experimental objective function values
Summary
Retinal implants help improve functional vision for patients blinded by retinal degenerative diseases such as age-related macular degeneration and retinitis pigmentosa [1]–[3]. Percepts are created by electrically stimulating the remaining cells of the retina, including retinal ganglion cells (RGC) and bipolar cells. Patients with implants report improvements in perceiving light, detecting motion, and following lines on the ground while walking. Their ability to recognize shapes and letters is currently limited [4], [5]. The ability to precisely stimulate target neurons and avoid off-target activation is critical to create focal, non-overlapping percepts. Unintended axonal activation is an important factor that contributes to elongated responses and low resolution of retinal stimulation. Creating focal percepts is important for better patient outcomes with artificial vision systems
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