Focal electrical stimulation of human retinal ganglion cells for vision restoration

Neural activity shaping in visual prostheses with deep learning

Objective.The visual perception provided by retinal prostheses is limited by the overlapping current spread of adjacent electrodes. This reduces the spatial resolution attainable with unipolar stimulation. Conversely, simultaneous multipolar stimulation guided by the measured neural responses-neural activity shaping (NAS)-can attenuate excessive spread of excitation allowing for more precise control over the pattern of neural activation. However, defining effective multipolar stimulus patterns is a challenging task. Previous attempts focused on analytical solutions based on an assumed linear nonlinear model of retinal response; an analytical model inversion (AMI) approach. Here, we propose a model-free solution for NAS, using artificial neural networks (ANNs) that could be trained with data acquired from the implant.Approach.Our 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. The stimulus generator network is trained on a large dataset of natural images and uses the trained MPN to determine efficient multipolar stimulus patterns by learning its inverse model. We validate our methodin silicousing a realistic model of retinal response to multipolar stimulation.Main results.We show that our ANN-based NAS approach produces sharper retinal activations than the conventional unipolar stimulation strategy. As a theoretical bench-mark of optimal NAS results, we implemented AMI stimulation by inverting the model used to simulate the retina. Our ANN strategy produced equivalent results to AMI, while not being restricted to any specific type of retina model and being three orders of magnitude more computationally efficient.Significance.Our novel protocol provides a method for efficient and personalized retinal stimulation, which may improve the visual experience and quality of life of retinal prosthesis users.

Avoidance of axonal stimulation with sinusoidal epiretinal stimulation

Objective.Neuromodulation, particularly electrical stimulation, necessitates high spatial resolution to achieve artificial vision with high acuity. In epiretinal implants, this is hindered by the undesired activation of distal axons. Here, we investigate focal and axonal activation of retinal ganglion cells (RGCs) in epiretinal configuration for different sinusoidal stimulation frequencies.Approach.RGC responses to epiretinal sinusoidal stimulation at frequencies between 40 and 100 Hz were tested inex-vivophotoreceptor degenerated (rd10) isolated retinae. Experiments were conducted using a high-density CMOS-based microelectrode array, which allows to localize RGC cell bodies and axons at high spatial resolution.Main results.We report current and charge density thresholds for focal and distal axon activation at stimulation frequencies of 40, 60, 80, and 100 Hz for an electrode size with an effective area of 0.01 mm2. Activation of distal axons is avoided up to a stimulation amplitude of 0.23µA (corresponding to 17.3µC cm-2) at 40 Hz and up to a stimulation amplitude of 0.28µA (14.8µC cm-2) at 60 Hz. The threshold ratio between focal and axonal activation increases from 1.1 for 100 Hz up to 1.6 for 60 Hz, while at 40 Hz stimulation frequency, almost no axonal responses were detected in the tested intensity range. With the use of synaptic blockers, we demonstrate the underlying direct activation mechanism of the ganglion cells. Finally, using high-resolution electrical imaging and label-free electrophysiological axon tracking, we demonstrate the extent of activation in axon bundles.Significance.Our results can be exploited to define a spatially selective stimulation strategy avoiding axonal activation in future retinal implants, thereby solving one of the major limitations of artificial vision. The results may be extended to other fields of neuroprosthetics to achieve selective focal electrical stimulation.

Bi-directional electrical recording and stimulation of the intact retina with a screen-printed soft probe: a feasibility study

Introduction

Electrophysiological investigations of intact neural circuits are challenged by the gentle and complex nature of neural tissues. Bi-directional electrophysiological interfacing with the retina, in its intact form, is particularly demanding and currently there is no feasible approach to achieve such investigations. Here we present a feasibility study of a novel soft multi-electrode array suitable for bi-directional electrophysiological study of the intact retina.

Methods

Screen-printed soft electrode arrays were developed and tested. The soft probes were designed to accommodate the curvature of the retina in the eye and offer an opportunity to study the retina in its intact form.

Results

For the first time, we show both electrical recording and stimulation capabilities from the intact retina. In particular, we demonstrate the ability to characterize retina responses to electrical stimulation and reveal stable, direct, and indirect responses compared with ex-vivo conditions.

Discussion

These results demonstrate the unique performances of the new probe while also suggesting that intact retinas retain better stability and robustness than ex-vivo retinas making them more suitable for characterizing retina responses to electrical stimulation.

Human-in-the-Loop Optimization for Deep Stimulus Encoding in Visual Prostheses

Neuroprostheses show potential in restoring lost sensory function and enhancing human capabilities, but the sensations produced by current devices often seem unnatural or distorted. Exact placement of implants and differences in individual perception lead to significant variations in stimulus response, making personalized stimulus optimization a key challenge. Bayesian optimization could be used to optimize patient-specific stimulation parameters with limited noisy observations, but is not feasible for high-dimensional stimuli. Alternatively, deep learning models can optimize stimulus encoding strategies, but typically assume perfect knowledge of patient-specific variations. Here we propose a novel, practically feasible approach that overcomes both of these fundamental limitations. First, a deep encoder network is trained to produce optimal stimuli for any individual patient by inverting a forward model mapping electrical stimuli to visual percepts. Second, a preferential Bayesian optimization strategy utilizes this encoder to optimize patient-specific parameters for a new patient, using a minimal number of pairwise comparisons between candidate stimuli. We demonstrate the viability of this approach on a novel, state-of-the-art visual prosthesis model. We show that our approach quickly learns a personalized stimulus encoder, leads to dramatic improvements in the quality of restored vision, and is robust to noisy patient feedback and misspecifications in the underlying forward model. Overall, our results suggest that combining the strengths of deep learning and Bayesian optimization could significantly improve the perceptual experience of patients fitted with visual prostheses and may prove a viable solution for a range of neuroprosthetic technologies.

The Variation of Electrical Pulse Duration Elicits Reliable Network-Mediated Responses of Retinal Ganglion Cells in Normal, Not in Degenerate Primate Retinas

This study aims to investigate the efficacy of electrical stimulation by comparing network-mediated RGC responses in normal and degenerate retinas using a N-methyl-N-nitrosourea (MNU)-induced non-human primate (NHPs) retinitis pigmentosa (RP) model. Adult cynomolgus monkeys were used for normal and outer retinal degeneration (RD) induced by MNU. The network-mediated RGC responses were recorded from the peripheral retina mounted on an 8 × 8 multielectrode array (MEA). The amplitude and duration of biphasic current pulses were modulated from 1 to 50 μA and 500 to 4000 μs, respectively. The threshold charge density for eliciting a network-mediated RGC response was higher in the RD monkeys than in the normal monkeys (1.47 ± 0.13 mC/cm2 vs. 1.06 ± 0.09 mC/cm2, p < 0.05) at a 500 μs pulse duration. The monkeys required a higher charge density than rodents among the RD models (monkeys; 1.47 ± 0.13 mC/cm2, mouse; 1.04 ± 0.09 mC/cm2, and rat; 1.16 ± 0.16 mC/cm2, p < 0.01). Increasing the pulse amplitude and pulse duration elicited more RGC spikes in the normal primate retinas. However, only pulse amplitude variation elicited more RGC spikes in degenerate primate retinas. Therefore, the pulse strategy for primate RD retinas should be optimized, eventually contributing to retinal prosthetics. Given that RD NHP RGCs are not sensitive to pulse duration, using shorter pulses may potentially be a more charge-effective approach for retinal prosthetics.

Inferring light responses of primate retinal ganglion cells using intrinsic electrical signatures

Objective. Retinal implants are designed to stimulate retinal ganglion cells (RGCs) in a way that restores sight to individuals blinded by photoreceptor degeneration. Reproducing high-acuity vision with these devices will likely require inferring the natural light responses of diverse RGC types in the implanted retina, without being able to measure them directly. Here we demonstrate an inference approach that exploits intrinsic electrophysiological features of primate RGCs.Approach.First, ON-parasol and OFF-parasol RGC types were identified using their intrinsic electrical features in large-scale multi-electrode recordings from macaque retina. Then, the electrically inferred somatic location, inferred cell type, and average linear-nonlinear-Poisson model parameters of each cell type were used to infer a light response model for each cell. The accuracy of the cell type classification and of reproducing measured light responses with the model were evaluated.Main results.A cell-type classifier trained on 246 large-scale multi-electrode recordings from 148 retinas achieved 95% mean accuracy on 29 test retinas. In five retinas tested, the inferred models achieved an average correlation with measured firing rates of 0.49 for white noise visual stimuli and 0.50 for natural scenes stimuli, compared to 0.65 and 0.58 respectively for models fitted to recorded light responses (an upper bound). Linear decoding of natural images from predicted RGC activity in one retina showed a mean correlation of 0.55 between decoded and true images, compared to an upper bound of 0.81 using models fitted to light response data.Significance.These results suggest that inference of RGC light response properties from intrinsic features of their electrical activity may be a useful approach for high-fidelity sight restoration. The overall strategy of first inferring cell type from electrical features and then exploiting cell type to help infer natural cell function may also prove broadly useful to neural interfaces.

Inference of Electrical Stimulation Sensitivity from Recorded Activity of Primate Retinal Ganglion Cells

High-fidelity electronic implants can in principle restore the function of neural circuits by precisely activating neurons via extracellular stimulation. However, direct characterization of the individual electrical sensitivity of a large population of target neurons, to precisely control their activity, can be difficult or impossible. A potential solution is to leverage biophysical principles to infer sensitivity to electrical stimulation from features of spontaneous electrical activity, which can be recorded relatively easily. Here, this approach is developed and its potential value for vision restoration is tested quantitatively using large-scale multielectrode stimulation and recording from retinal ganglion cells (RGCs) of male and female macaque monkeys ex vivo Electrodes recording larger spikes from a given cell exhibited lower stimulation thresholds across cell types, retinas, and eccentricities, with systematic and distinct trends for somas and axons. Thresholds for somatic stimulation increased with distance from the axon initial segment. The dependence of spike probability on injected current was inversely related to threshold, and was substantially steeper for axonal than somatic compartments, which could be identified by their recorded electrical signatures. Dendritic stimulation was largely ineffective for eliciting spikes. These trends were quantitatively reproduced with biophysical simulations. Results from human RGCs were broadly similar. The inference of stimulation sensitivity from recorded electrical features was tested in a data-driven simulation of visual reconstruction, revealing that the approach could significantly improve the function of future high-fidelity retinal implants.SIGNIFICANCE STATEMENT This study demonstrates that individual in situ primate retinal ganglion cells of different types respond to artificially generated, external electrical fields in a systematic manner, in accordance with theoretical predictions, that allows for prediction of electrical stimulus sensitivity from recorded spontaneous activity. It also provides evidence that such an approach could be immensely helpful in the calibration of clinical retinal implants.

High-Fidelity Reproduction of Visual Signals by Electrical Stimulation in the Central Primate Retina

Electrical stimulation of retinal ganglion cells (RGCs) with electronic implants provides rudimentary artificial vision to people blinded by retinal degeneration. However, current devices stimulate indiscriminately and therefore cannot reproduce the intricate neural code of the retina. Recent work has demonstrated more precise activation of RGCs using focal electrical stimulation with multielectrode arrays in the peripheral macaque retina, but it is unclear how effective this can be in the central retina, which is required for high-resolution vision. This work probes the neural code and effectiveness of focal epiretinal stimulation in the central macaque retina, using large-scale electrical recording and stimulation ex vivo The functional organization, light response properties, and electrical properties of the major RGC types in the central retina were mostly similar to the peripheral retina, with some notable differences in density, kinetics, linearity, spiking statistics, and correlations. The major RGC types could be distinguished by their intrinsic electrical properties. Electrical stimulation targeting parasol cells revealed similar activation thresholds and reduced axon bundle activation in the central retina, but lower stimulation selectivity. Quantitative evaluation of the potential for image reconstruction from electrically evoked parasol cell signals revealed higher overall expected image quality in the central retina. An exploration of inadvertent midget cell activation suggested that it could contribute high spatial frequency noise to the visual signal carried by parasol cells. These results support the possibility of reproducing high-acuity visual signals in the central retina with an epiretinal implant.SIGNIFICANCE STATEMENT Artificial restoration of vision with retinal implants is a major treatment for blindness. However, present-day implants do not provide high-resolution visual perception, in part because they do not reproduce the natural neural code of the retina. Here, we demonstrate the level of visual signal reproduction that is possible with a future implant by examining how accurately responses to electrical stimulation of parasol retinal ganglion cells can convey visual signals. Although the precision of electrical stimulation in the central retina was diminished relative to the peripheral retina, the quality of expected visual signal reconstruction in parasol cells was greater. These findings suggest that visual signals could be restored with high fidelity in the central retina using a future retinal implant.

Retinal Prostheses: Engineering and Clinical Perspectives for Vision Restoration

A retinal prosthesis, also known as a bionic eye, is a device that can be implanted to partially restore vision in patients with retinal diseases that have resulted in the loss of photoreceptors (e.g., age-related macular degeneration and retinitis pigmentosa). Recently, there have been major breakthroughs in retinal prosthesis technology, with the creation of numerous types of implants, including epiretinal, subretinal, and suprachoroidal sensors. These devices can stimulate the remaining cells in the retina with electric signals to create a visual sensation. A literature review of the pre-clinical and clinical studies published between 2017 and 2023 is conducted. This narrative review delves into the retinal anatomy, physiology, pathology, and principles underlying electronic retinal prostheses. Engineering aspects are explored, including electrode-retina alignment, electrode size and material, charge density, resolution limits, spatial selectivity, and bidirectional closed-loop systems. This article also discusses clinical aspects, focusing on safety, adverse events, visual function, outcomes, and the importance of rehabilitation programs. Moreover, there is ongoing debate over whether implantable retinal devices still offer a promising approach for the treatment of retinal diseases, considering the recent emergence of cell-based and gene-based therapies as well as optogenetics. This review compares retinal prostheses with these alternative therapies, providing a balanced perspective on their advantages and limitations. The recent advancements in retinal prosthesis technology are also outlined, emphasizing progress in engineering and the outlook of retinal prostheses. While acknowledging the challenges and complexities of the technology, this article highlights the significant potential of retinal prostheses for vision restoration in individuals with retinal diseases and calls for continued research and development to refine and enhance their performance, ultimately improving patient outcomes and quality of life.