Electrical Stimulation of the Retina to Produce Artificial Vision

Weiland, James D.; Walston, Steven T.; Humayun, Mark S.

doi:10.1146/annurev-vision-111815-114425

Cited by 73 publications

(68 citation statements)

References 116 publications

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“…a single retinal ganglion cell (RGC)) severely limits spatial resolution [13][14][15][16] , while desensitization of retinal neurons to repetitive electrical stimulation impedes high temporal resolution [17][18][19] . Acuity of prosthesis-mediated vision therefore reaches only a fraction of healthy visual acuity, leaving patients legally blind despite wearing a retinal implant 20 . Another reason why bionic vision comes short of natural vision is that the image processing mechanisms of the intricate retinal network cannot be triggered selectively by retinal implants; pulsatile stimuli do not separately activate channels like the ON and the OFF pathway 21 .…”

Section: Introductionmentioning

confidence: 99%

Probing and predicting ganglion cell responses to smooth electrical stimulation in healthy and blind mouse retina

Hoefling

Berens

Zeck

2019

Preprint

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Retinal implants are used to replace lost photoreceptors in blind patients suffering from retinopathies such as retinitis pigmentosa. Patients wearing implants regain some rudimentary visual function. However, it is severely limited compared to normal vision because non-physiological stimulation strategies fail to selectively activate different retinal pathways at sufficient spatial and temporal resolution. The development of improved stimulation strategies is rendered difficult by the large space of potential stimuli. Here we systematically explore a subspace of potential stimuli by electrically stimulating healthy and blind mouse retina in epiretinal configuration using smooth Gaussian white noise delivered by a high-density CMOS-based microelectrode array. We identify linear filters of retinal ganglion cells (RGCs) by fitting a linear-nonlinear-Poisson (LNP) model. Our stimulus evokes fast, reliable, and spatially confined spiking responses in RGC which are accurately predicted by the LNP model. Furthermore, we find diverse shapes of linear filters in the linear stage of the model, suggesting diverse preferred electrical stimuli of RGCs. Our smooth electrical stimulus could provide a starting point of a model-guided search for improved stimuli for retinal prosthetics.

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Section: Introductionmentioning

confidence: 99%

Probing and predicting ganglion cell responses to smooth electrical stimulation in healthy and blind mouse retina

Hoefling

Berens

Zeck

2019

Preprint

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“…Jansonius and colleagues determined parameter values by fitting Equations (6)(7)(8) to the topographical layout of 55 eyes from 55 human subjects (for details see 1). The attentive reader might notice that Equation (8) To apply the axon map to the eyes of our subjects, we first transformed the original coordinate system ( , ) to Cartesian coordinates ( , ) with the foveal pit located at (0, 0), and then set the coordinates of the optic disc ( od , od ) to the values estimated from fundus photography ( Table 3).…”

Section: Axon Map Modelmentioning

confidence: 99%

“…retinal prostheses is to help alleviate these incurable conditions by electrically stimulating 40 surviving cells in the retina (for a recent review, see Weiland et al, 2016). The hope is that 41 electrically evoked neuronal responses will be transmitted to the brain and interpreted by the 42 subject as visual percepts ('phosphenes').…”

mentioning

confidence: 99%

A model of ganglion axon pathways accounts for percepts elicited by retinal implants

Beyeler

Nanduri

Weiland

et al. 2018

Preprint

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12Degenerative retinal diseases such as retinitis pigmentosa and macular degeneration cause 13 irreversible vision loss in more than 10 million people worldwide. Retinal prostheses, now 14 implanted in more than 250 patients worldwide, electrically stimulate surviving cells in order to 15 evoke neuronal responses that are interpreted by the brain as visual percepts ('phosphenes'). 16However, instead of seeing focal spots of light, users of current epiretinal devices perceive highly 17 distorted phosphenes, which vary in shape not just across subjects but also across electrodes, 18 resulting in distorted percepts. We characterized these distortions by asking users of the Argus 19retinal prosthesis system (Second Sight Medical Products, Inc.) to draw percepts elicited by single-20 electrode stimulation on a touchscreen. Based on ophthalmic fundus photographs, we then 21 developed a computational model of the topographic organization of optic nerve fiber bundles in 22 each subject's retina, and used this model to successfully simulate predicted patient percepts. Our 23 model shows that activation of passing axon fibers contributes to the rich repertoire of phosphene 24shapes reported by patients in our psychophysical measurements, successfully replicating visual 25 percepts ranging from 'blobs' to oriented 'streaks' and 'wedges' depending on the retinal location 26 of the stimulating electrode. This model provides a first step towards future devices that 27 incorporate stimulation strategies tailored to each individual patient's retinal neurophysiology. 28 Impact 29 Current retinal implant users report seeing distorted and often elongated shapes rather than small 30 focal spots of light that match the shape of the implant electrodes. Here we show that the perceptual 31 experience of retinal implant users can be accurately predicted using a computational model that 32simulates each individual patient's retinal ganglion axon pathways. This opens up the possibility 33 for future devices that incorporate stimulation strategies tailored to each individual patient's retina. 34Wuyyuru, V., Sahel, J., Stanga, P., et al. (2013). The Argus II epiretinal prosthesis system allows 595 letter and word reading and long-term function in patients with profound vision loss. Br J 596Ophthalmol 97, 632-636. 597

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“…This bypasses the natural presynaptic neurotransmitter excitation and causes the activated neurons to stimulate their postsynaptic targets. Therefore, selective spatiotemporal modulation of retinal neurons with an array of electrodes should allow a prosthesis to coordinate retinal activity in place of natural photoreceptor input [WWH16].…”

Section: Introductionmentioning

confidence: 99%

“…These vary in their user interface, light-detection method, signal processing, and microelectrode placement within the retina (for a recent review see [WWH16]). As far as our model is concerned, the critical factor is the placement of the microelectrodes on the retina (Fig.…”

Section: Introductionmentioning

confidence: 99%

pulse2percept: A Python-based simulation framework for bionic vision

Beyeler¹,

Boynton²,

Fine³

et al. 2017

Proceedings of the 16th Python in Science Conference

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Abstract-By 2020 roughly 200 million people worldwide will suffer from photoreceptor diseases such as retinitis pigmentosa and age-related macular degeneration, and a variety of retinal sight restoration technologies are being developed to target these diseases. One technology, analogous to cochlear implants, uses a grid of electrodes to stimulate remaining retinal cells. Two brands of retinal prostheses are currently approved for implantation in patients with late stage photoreceptor disease. Clinical experience with these implants has made it apparent that the vision restored by these devices differs substantially from normal sight. To better understand the outcomes of this technology, we developed pulse2percept, an open-source Python implementation of a computational model that predicts the perceptual experience of retinal prosthesis patients across a wide range of implant configurations. A modular and extensible user interface exposes the different building blocks of the software, making it easy for users to simulate novel implants, stimuli, and retinal models. We hope that this library will contribute substantially to the field of medicine by providing a tool to accelerate the development of visual prostheses.

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Electrical Stimulation of the Retina to Produce Artificial Vision

Cited by 73 publications

References 116 publications

Probing and predicting ganglion cell responses to smooth electrical stimulation in healthy and blind mouse retina

Probing and predicting ganglion cell responses to smooth electrical stimulation in healthy and blind mouse retina

A model of ganglion axon pathways accounts for percepts elicited by retinal implants

pulse2percept: A Python-based simulation framework for bionic vision

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