Spencer, M. J. | Search bioRxiv

bioRxiv2023

Neural Activity Shaping in Visual Prostheses with Deep Learning

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.

bioengineering↗

bioRxiv2022

Quantifying Visual Acuity for Pre-Clinical Testing of Visual Prostheses.

Visual prostheses currently restore only limited vision. More research and pre-clinical work are required to improve the devices and stimulation strategies that are used to induce neural activity that results in visual perception. Evaluation of candidate strategies and devices requires an objective way to convert measured and modelled patterns of neural activity into a quantitative measure of visual acuity. This study presents an approach that compares evoked patterns of neural activation with target and reference patterns. A d-prime measure of discriminability determines whether the evoked neural activation pattern is sufficient to discriminate between the target and reference patterns and thus provide a quantified level of visual perception in the clinical Snellen and MAR scales. The measure was accurate in providing an estimate of the perceivable feature sizes in scaled standardized "C" and "E" optotypes. The approach was used to assess the visual acuity provided by two alternative stimulation strategies applied to simulated retinal implants with different phosphene sizes and electrode pitch configurations. It was found that when there is substantial overlap in neural activity generated by different electrodes, an estimate of acuity based only upon electrode pitch is incorrect; our proposed method gives an accurate result in these circumstances. Quantification of visual acuity using this approach in pre-clinical development will allow for more rapid and accurate prototyping of improved devices and neural stimulation strategies.

bioengineering↗