Neural Activity Shaping in Visual Prostheses with Deep Learning

Abstract

ObjectiveThe visual perception provided by retinal prostheses is poor and limited to images constructed of phosphenes generated by the electrodes. One limiting factor has been the conventional strategy used to encode the target image into a stimulation pattern. Under this strategy, if the electrode density is high, the current spread of neighbouring unipolar stimuli overlaps, leading to blurred images. Simultaneous multipolar stimulation guided by the measured neural responses can attenuate excessive spread of excitation and allows for a more precise electrical input to the retina. However, it is far from trivial to predict what multipolar stimulus pattern will elicit the desired retinal response for a given target image. Here, we propose to solve this problem using an Artificial Neural Network (ANN) that could be trained with data acquired from the implant itself. ApproachOur method consists of two ANNs trained sequentially. The Measurement Predictor Network (MPN) is trained on data from the implant and is used to predict how the retina responds to multipolar stimulation by learning the forward model. The Stimulus Generator Network (STG) is trained on a large dataset of natural images and uses the trained MPN to determine efficient multipolar stimulus patterns by learning the inverse model. We validate our method in silico using a realistic model of retinal response to multipolar stimulation. Main ResultsWe show that the simulated retinal activations elicited with our ANN-based approach are considerably sharper when compared with the conventional method used in existing devices. The SGN finds multipolar stimulation patterns that are tuned to a specific retina, thus providing patient-specific stimuli. Also, due to its small computational cost, the SGN can output stimulation patterns at a very high rate. SignificanceOur novel protocol opens the door to personalized multipolar retinal stimulation, which may improve the visual experience and quality of life of retinal prosthesis users.