The impact of synchronous versus asynchronous electrical stimulation in artificial vision

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.

Towards biologically plausible phosphene simulation for the differentiable optimization of visual cortical prostheses

Blindness affects millions of people around the world. A promising solution to restoring a form of vision for some individuals are cortical visual prostheses, which bypass part of the impaired visual pathway by converting camera input to electrical stimulation of the visual system. The artificially induced visual percept (a pattern of localized light flashes, or 'phosphenes') has limited resolution, and a great portion of the field's research is devoted to optimizing the efficacy, efficiency, and practical usefulness of the encoding of visual information. A commonly exploited method is non-invasive functional evaluation in sighted subjects or with computational models by using simulated prosthetic vision (SPV) pipelines. An important challenge in this approach is to balance enhanced perceptual realism, biologically plausibility, and real-time performance in the simulation of cortical prosthetic vision. We present a biologically plausible, PyTorch-based phosphene simulator that can run in real-time and uses differentiable operations to allow for gradient-based computational optimization of phosphene encoding models. The simulator integrates a wide range of clinical results with neurophysiological evidence in humans and non-human primates. The pipeline includes a model of the retinotopic organization and cortical magnification of the visual cortex. Moreover, the quantitative effects of stimulation parameters and temporal dynamics on phosphene characteristics are incorporated. Our results demonstrate the simulator's suitability for both computational applications such as end-to-end deep learning-based prosthetic vision optimization as well as behavioral experiments. The modular and open-source software provides a flexible simulation framework for computational, clinical, and behavioral neuroscientists working on visual neuroprosthetics.

The Influence of Phosphene Synchrony in Driving Object Binding in a Simulation of Artificial Vision

Purpose

Electrical microstimulation techniques used in visual prostheses are designed to restore visual function following acquired blindness. Patterns of induced focal percepts, known as phosphenes, are achieved by applying localized electrical pulses to the visual pathway to bypass the impaired site in order to convey images from the external world. Here, we use a simulation of artificial vision to manipulate relationships between individual phosphenes to observe the effects on object binding and perception. We hypothesize that synchronous phosphene presentation will facilitate object perception as compared to asynchronous presentation.

Methods

A model system that tracks gaze position of normal, sighted participants to present patterns of phosphenes on a computer screen was used to simulate prosthetic vision. Participants performed a reading task at varying font sizes (1.1-1.4 logMAR) and under varying levels of phosphene temporal noise while reading accuracy and speed were measured.

Results

Reading performance was significantly affected by temporal noise in phosphene presentation, with increasing desynchronization leading to lower reading scores. A drop in performance was also observed when the total latency between the gaze position and phosphene update was increased without adding temporal noise.

Conclusions

Object perception (here, text perception) is enhanced with synchronously presented phosphenes as compared to asynchronously presented ones. These results are fundamental for developing an efficient temporal pattern of stimulation and for the creation of high-fidelity prosthetic vision.

Micro-magnetic stimulation of primary visual cortex induces focal and sustained activation of secondary visual cortex

Cortical visual prostheses that aim to restore sight to the blind require the ability to create neural activity in the visual cortex. Electric stimulation delivered via microelectrodes implanted in the primary visual cortex (V1) has been the most common approach, although conventional electrodes may not effectively confine activation to focal regions and thus the acuity they create may be limited. Magnetic stimulation from microcoils confines activation to single cortical columns of V1 and thus may prove to be more effective than conventional microelectrodes, but the ability of microcoils to drive synaptic connections has not been explored. Here, we show that magnetic stimulation of V1 using microcoils induces spatially confined activation in the secondary visual cortex (V2) in mouse brain slices. Single-loop microcoils were fabricated using platinum-iridium flat microwires, and their effectiveness was evaluated using calcium imaging and compared with that of monopolar and bipolar electrodes. Our results show that compared to the electrodes, the microcoils better confined activation to a small region in V1. In addition, they produced more precise and sustained activation in V2. The finding that microcoil-based stimulation propagates to higher visual centres raises the possibility that complex visual perception, e.g. that requiring sustained synaptic inputs, may be achievable. This article is part of the theme issue 'Advanced neurotechnologies: translating innovation for health and well-being'.

Improving the spatial resolution of artificial vision using midget retinal ganglion cell populations modelled at the human fovea

OBJECTIVE

Retinal prostheses seek to create artificial vision by stimulating surviving retinal neurons of patients with profound vision impairment. Notwithstanding tremendous research efforts, the performance of all implants tested to date has remained rudimentary, incapable of overcoming the threshold for legal blindness. To maximize the perceptual efficacy of retinal prostheses, a device must be capable of controlling retinal neurons with greater spatiotemporal precision. Most studies of retinal stimulation were derived from either non-primate species or the peripheral primate retina. We investigated if artificial stimulation could leverage the high spatial resolution afforded by the neural substrates at the primate fovea and surrounding regions to achieve improved percept qualities.

APPROACH

We began by developing a new computational model capable of generating anatomically accurate retinal ganglion cell (RGC) populations within the human central retina. Next, multiple RGC populations across the central retina were stimulated in-silico to compare clinical and recently proposed neurostimulation configurations based on their ability to improve perceptual efficacy and reduce activation thresholds.

MAIN RESULTS

Our model uniquely upholds eccentricity-dependent characteristics such as RGC density and dendritic field diameter, whilst incorporating anatomically accurate features such as axon projection and three-dimensional RGC layering, features often forgone in favor of reduced computational complexity. Following epiretinal stimulation, the RGCs in our model produced response patterns in shapes akin to the complex percepts reported in clinical trials. Our results also demonstrated that even within the neuron-dense central retina, epiretinal stimulation using a multi-return hexapolar electrode arrangement could reliably achieve spatially focused RGC activation and could achieve single-cell excitation in 74% of all tested locations.

SIGNIFICANCE

This study establishes an anatomically accurate three-dimensional model of the human central retina and demonstrates the potential for an epiretinal hexapolar configuration to achieve consistent, spatially confined retinal responses, even within the neuron-dense foveal region. Our results promote the prospect and optimization of higher spatial resolution in future epiretinal implants.

Effects of Depth-Based Object Isolation in Simulated Retinal Prosthetic Vision

Visual retinal prostheses aim to restore vision for blind individuals who suffer from outer retinal degenerative diseases, such as retinitis pigmentosa and age-related macular degeneration. Perception through retinal prostheses is very limited, but it can be improved by applying object isolation. We used an object isolation algorithm based on integral imaging to isolate objects of interest according to their depth from the camera and applied image processing manipulation to the isolated-object images. Subsequently, we applied a spatial prosthetic vision simulation that converted the isolated-object images to phosphene images. We compared the phosphene images for two types of input images, the original image (before applying object isolation), and the isolated-object image to illustrate the effects of object isolation on simulated prosthetic vision without and with multiple spatial variations of phosphenes, such as size and shape variations, spatial shifts, and dropout rate. The results show an improvement in the perceived shape, contrast, and dynamic range (number of gray levels) of objects in the phosphene image.