A Computational Model of Phosphene Appearance for Epiretinal Prostheses

Granley, Jacob; Beyeler, Michael

doi:10.1109/embc46164.2021.9629663

Cited by 20 publications

(35 citation statements)

References 29 publications

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“…where φ is sampled from a uniform random distribution spanning the empirically observed range of patient-specific parameters [4,20].…”

Section: Methodsmentioning

confidence: 99%

“…Most previous approaches (e.g., [11,40,42]) use an overly simplified forward model that cannot account for empirical data [4,15]. We overcome this limitation by developing (and inverting) a differentiable version of a neurophysiologically informed and psychophysically validated phosphene model [20] that can account for the effects of stimulus amplitude, frequency, and pulse duration on phosphene appearance.…”

Section: Related Workmentioning

confidence: 99%

“…We overcome this limitation by inverting a phenomenological ("top-down") model constrained by behavioral data that predicts visual perception directly from electrical stimuli [4,20].…”

Section: Related Workmentioning

confidence: 99%

“…To this end, Granley et. al [20] developed a forward model to predict phosphene shape as a function of both neuroanatomical parameters (i.e., retinal location of the stimulating electrode) and stimulus parameters (i.e., pulse frequency, amplitude, and duration). With this model, phosphenes are primarily characterized by two main parameters, ρ and λ, which dictate the size and elongation of the elicited phosphene, respectively (Fig.…”

Section: Introductionmentioning

confidence: 99%

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A Hybrid Neural Autoencoder for Sensory Neuroprostheses and Its Applications in Bionic Vision

Granley¹,

Relic²,

Beyeler³

2022

Preprint

Self Cite

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Sensory neuroprostheses are emerging as a promising technology to restore lost sensory function or augment human capacities. However, sensations elicited by current devices often appear artificial and distorted. Although current models can often predict the neural or perceptual response to an electrical stimulus, an optimal stimulation strategy solves the inverse problem: what is the required stimulus to produce a desired response? Here we frame this as an end-to-end optimization problem, where a deep neural network encoder is trained to invert a known, fixed forward model that approximates the underlying biological system. As a proof of concept, we demonstrate the effectiveness of our hybrid neural autoencoder (HNA) on the use case of visual neuroprostheses. We found that HNA is able to produce high-fidelity stimuli from the MNIST and COCO datasets that outperform conventional encoding strategies and surrogate techniques across all tested conditions. Overall this is an important step towards the long-standing challenge of restoring high-quality vision to people living with incurable blindness and may prove a promising solution for a variety of neuroprosthetic technologies.Preprint. Under review.

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“…where φ is sampled from a uniform random distribution spanning the empirically observed range of patient-specific parameters [4,20].…”

Section: Methodsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

“…We overcome this limitation by inverting a phenomenological ("top-down") model constrained by behavioral data that predicts visual perception directly from electrical stimuli [4,20].…”

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Hybrid Neural Autoencoder for Sensory Neuroprostheses and Its Applications in Bionic Vision

Granley¹,

Relic²,

Beyeler³

2022

Preprint

Self Cite

View full text Add to dashboard Cite

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“…However, evidence suggests that phosphenes often appear distorted (e.g., as simple geometric shapes such as lines, wedges, and blobs) and vary drastically across subjects and electrodes [4]. More recently, SPV models have therefore aimed to explain phosphene appearance as a function of neuroanatomical and psychophysical data [2,5] (Fig. 1A).…”

Section: Related Work 21 Simulated Prosthetic Vision (Spv)mentioning

confidence: 99%

Deep Learning-Based Perceptual Stimulus Encoder for Bionic Vision

Relic¹,

Zhang²,

Tuan³

et al. 2022

Preprint

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Figure 1: A) Simulated prosthetic vision (SPV). Constrained by neuroanatomical and/or psychophysical data, a phosphene model Ψ (e.g., [2]) predicts what a retinal implant user should "see" for any given input stimulus. The predicted percept is typically a nonlinear continuous function of the input stimulus, thus Ψ can be approximated by a generic feedforward neural network (FNN), Ψ, which is amenable to differentiation. B) End-to-end optimization of bionic vision. For a given target percept, a stimulus encoder based on a convolutional neural network (CNN) is trained to predict the combination of active electrodes that generates the percept with the smallest possible reconstruction loss. The FNN approximator is fixed during encoder training.

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Sequential epiretinal stimulation improves discrimination in simple shape discrimination tasks only

et al. 2022

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Objective: Electrical stimulation of the retina can elicit flashes of light called phosphenes, which can be used to restore rudimentary vision for people with blindness. Functional sight requires stimulation of multiple electrodes to create patterned vision, but phosphenes tend to merge together in an uninterpretable way. Sequentially stimulating electrodes in human visual cortex has recently demonstrated that shapes could be “drawn” with better perceptual resolution relative to simultaneous stimulation. The goal of this study was to evaluate if sequential stimulation would also form clearer shapes when the retina is the neural target. Approach: Two human participants with retinitis pigmentosa who had Argus® II retinal prostheses participated in this study. We evaluated different temporal parameters for sequential stimulation in phosphene shape mapping and forced-choice discrimination tasks. For the discrimination tasks, performance was compared between stimulating electrodes simultaneously versus sequentially. Main results: Phosphenes elicited by different electrodes were reported as vastly different shapes. Sequential electrode stimulation outperformed simultaneous stimulation in simple discrimination tasks, in which shapes were created by stimulating 3-4 electrodes, but not in more complex discrimination tasks involving 5+ electrodes. For sequential stimulation, the optimal pulse train duration was 200 ms when stimulating at 20 Hz and the optimal gap interval was tied between 0 and 50 ms. Efficacy of sequential stimulation also depended strongly on selecting electrodes that elicited phosphenes with similar shapes and sizes. Significance: An epiretinal prosthesis can produce coherent simple shapes with a sequential stimulation paradigm, which can be used as rudimentary visual feedback. However, success in creating more complex shapes, such as letters of the alphabet, is still limited. Sequential stimulation may be most beneficial for epiretinal prostheses in simple tasks, such as basic navigation, rather than complex tasks such as object identification.

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A Computational Model of Phosphene Appearance for Epiretinal Prostheses

Cited by 20 publications

References 29 publications

A Hybrid Neural Autoencoder for Sensory Neuroprostheses and Its Applications in Bionic Vision

A Hybrid Neural Autoencoder for Sensory Neuroprostheses and Its Applications in Bionic Vision

Deep Learning-Based Perceptual Stimulus Encoder for Bionic Vision

Sequential epiretinal stimulation improves discrimination in simple shape discrimination tasks only

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