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Two of the main obstacles to the development of epiretinal prosthesis technology are electrodes that require current amplitudes above safety limits to reliably elicit percepts, and a failure to consistently elicit pattern vision. Here, we explored the causes of high current amplitude thresholds and poor spatial resolution within the Argus II epiretinal implant. We measured current amplitude thresholds and two-point discrimination (the ability to determine whether one or two electrodes had been stimulated) in 3 blind participants implanted with Argus II devices. Our data and simulations show that axonal stimulation, lift and retinal damage all play a role in reducing performance in the Argus 2, by either limiting sensitivity and/or reducing spatial resolution. Understanding the relative role of these various factors will be critical for developing and surgically implanting devices that can successfully subserve pattern vision.

Many forms of artificial sight recovery, such as electronic implants and optogenetic proteins, generally cause simultaneous, rather than complementary firing of on- and off-center retinal cells. Here, using virtual patients-sighted individuals viewing distorted input-we examine whether plasticity might compensate for abnormal neuronal population responses. Five participants were dichoptically presented with a combination of original and contrast-reversed images. Each image (I) and its contrast-reverse (I') was filtered using a radial checkerboard (F) in Fourier space and its inverse (F'). [I * F'] + [I' * F] was presented to one eye, and [I * F] + [I' * F'] was presented to the other, such that regions of the image that produced on-center responses in one eye produced off-center responses in the other eye, and vice versa. Participants continuously improved in a naturalistic object discrimination task over 20 one-hour sessions. Pre-training and post-training tests suggest that performance improvements were due to two learning processes: learning to recognize objects with reduced visual information and learning to suppress contrast-reversed image information in a non-eye-selective manner. These results suggest that, with training, it may be possible to adapt to the unnatural on- and off-cell population responses produced by electronic and optogenetic sight recovery technologies.

Model-based analyses open exciting opportunities for understanding neural information processing. In a commentary published in eNeuro, Gardner and Liu (2019) discuss the role of model specification in interpreting results derived from complex models of neural data. As a case study, they suggest that one such analysis, the inverted encoding model (IEM), should not be used to assay properties of "stimulus representations" because the ability to apply linear transformations at various stages of the analysis procedure renders results "arbitrary." Here, we argue that the specification of all models is arbitrary to the extent that an experimenter makes choices based on current knowledge of the model system. However, the results derived from any given model, such as the reconstructed channel response profiles obtained from an IEM analysis, are uniquely defined and are arbitrary only in the sense that changes in the model can predictably change results. IEM-based channel response profiles should therefore not be considered arbitrary when the model is clearly specified and guided by our best understanding of neural population representations in the brain regions being analyzed. Intuitions derived from this case study are important to consider when interpreting results from all model-based analyses, which are similarly contingent upon the specification of the models used.

Degenerative retinal diseases such as retinitis pigmentosa and macular degeneration cause irreversible vision loss in more than 10 million people worldwide. Retinal prostheses, now implanted in over 250 patients worldwide, electrically stimulate surviving cells in order to evoke neuronal responses that are interpreted by the brain as visual percepts ('phosphenes'). However, instead of seeing focal spots of light, current implant users perceive highly distorted phosphenes that vary in shape both across subjects and electrodes. We characterized these distortions by asking users of the Argus retinal prosthesis system (Second Sight Medical Products Inc.) to draw electrically elicited percepts on a touchscreen. Using ophthalmic fundus imaging and computational modeling, we show that elicited percepts can be accurately predicted by the topographic organization of optic nerve fiber bundles in each subject's retina, successfully replicating visual percepts ranging from 'blobs' to oriented 'streaks' and 'wedges' depending on the retinal location of the stimulating electrode. This provides the first evidence that activation of passing axon fibers accounts for the rich repertoire of phosphene shape commonly reported in psychophysical experiments, which can severely distort the quality of the generated visual experience. Overall our findings argue for more detailed modeling of biological detail across neural engineering applications.

A major limitation of current electronic retinal implants is that in addition to stimulating the intended retinal ganglion cells, they also stimulate passing axon fibers, producing perceptual ‘streaks’ that limit the quality of the generated visual experience. Recent evidence suggests a dependence between the shape of the elicited visual percept and the retinal location of the stimulating electrode. However, this knowledge has yet to be incorporated into the surgical placement of retinal implants. Here we systematically explored the space of possible implant configurations to make recommendations for optimal intraocular positioning of the electrode array. Using a psychophysically validated computational model, we demonstrate that better implant placement has the potential to reduce the spatial extent of axonal activation in existing implant users by up to ∼55 %. Importantly, the best implant location, as inferred from a population of simulated virtual patients, is both surgically feasible and is relatively stable across individuals. This study is a first step towards the use of computer simulations in patient-specific planning of retinal implant surgery.

Abstract: Retinal prostheses, now implanted in over 250 patients worldwide, electrically stimulate surviving cells in order to evoke neuronal responses that are interpreted by the brain as visual percepts (‘phosphenes’). However, instead of seeing focal spots of light, current implant users perceive highly distorted phosphenes that vary in shape both across subjects and electrodes. We characterized these distortions by asking users of the Argus retinal prosthesis system (Second Sight Medical Products) to draw elicited percepts on a touchscreen. We found that phosphene shape could be accurately predicted by simulating the topographic organization of nerve fiber bundles in each subject’s retina. Our model shows that activation of ganglion axons contributes to a rich repertoire of phosphene shapes, successfully replicating percepts ranging from ‘blobs’ to oriented ‘streaks’ and ‘wedges’ depending on electrode location. This work provides a first step towards future devices that incorporate stimulation strategies tailored to each individual patient’s retinal neurophysiology. One Sentence Summary: We show that the perceptual experience of retinal implant users can be accurately predicted using a computational model that simulates the topographic organization of each individual patient’s retinal ganglion axon pathways.

The 'bionic eye'-so long a dream of the future-is finally becoming a reality with retinal prostheses available to patients in both the US and Europe. However, clinical experience with these implants has made it apparent that the visual information provided by these devices differs substantially from normal sight. Consequently, the ability of patients to learn to make use of this abnormal retinal input plays a critical role in whether or not some functional vision is successfully regained. The goal of the present review is to summarize the vast basic science literature on developmental and adult cortical plasticity with an emphasis on how this literature might relate to the field of prosthetic vision. We begin with describing the distortion and information loss likely to be experienced by visual prosthesis users. We then define cortical plasticity and perceptual learning, and describe what is known, and what is unknown, about visual plasticity across the hierarchy of brain regions involved in visual processing, and across different stages of life. We close by discussing what is known about brain plasticity in sight restoration patients and discuss biological mechanisms that might eventually be harnessed to improve visual learning in these patients.

In 2000, monocular vision was restored to M. M., who had been blind between the ages of 3 and 46 years. Tests carried out over 2 years following the surgery revealed impairments of 3-D form, object, and face processing and an absence of object- and face-selective blood-oxygen-level-dependent responses in ventral visual cortex. In the present research, we reexamined M. M. to test for experience-dependent recovery of visual function. Behaviorally, M. M. remains impaired in 3-D form, object, and face processing. Accordingly, we found little to no evidence of the category-selective organization within ventral visual cortex typically associated with face, body, scene, or object processing. We did observe remarkably normal object selectivity within lateral occipital cortex, consistent with M. M.'s previously reported shape-discrimination performance. Together, these findings provide little evidence for recovery of high-level visual function after more than a decade of visual experience in adulthood.

To link to full-text access for this article, visit this link: http://dx.doi.org/10.1016/j.tins.2006.02.008 Byline: Bart Krekelberg (a), Geoffrey M. Boynton (b), Richard J.A. van Wezel (c) Abstract: Functional magnetic resonance imaging adaptation (fMRIa) is an increasingly popular method that aims to provide insight into the functional properties of subpopulations of neurons within an imaging voxel. The technique relies on the assumption that neural adaptation reduces activity when two successive stimuli activate the same subpopulation but not when they stimulate different subpopulations. Here, we assess the validity of fMRIa by comparing single-cell recordings with functional imaging of orientation, motion and face processing. We find that fMRIa provides novel insight into neural representations in the human brain. However, network responses in general and adaptation in particular are more complex than is often assumed, and an unequivocal interpretation of fMRIa results can be achieved only with great care. Author Affiliation: (a) Rutgers University, Center for Molecular and Behavioral Neuroscience, 197 University Avenue, Newark, NJ 07102, USA (b) The Salk Institute for Biological Studies, Systems Neurobiology Laboratories, 10010 North Torrey Pines Road, La Jolla, CA 92037, USA (c) Utrecht University, Helmholtz Institute, Padualaan 8, 3584 CH, Utrecht, The Netherlands