Closed-loop Optimization of Retinal Ganglion Cell Responses to Epiretinal Stimulation: A Computational Study
Abstract
Retinal prostheses improve vision for patients with retinal degeneration. However, the shape recognition ability of retinal prostheses users is limited due to the low visual resolution of these devices. Off-target retinal ganglion cell (RGC) activation is an important contributing factor to the low stimulation precision. Previous research has shown RGC spatial activity and perception of shapes by users can be difficult to predict due to the complexity of retina structure and electrode-retina interactions. In this study we demonstrate a method to iteratively search for optimal stimulation parameters that create focal RGC activation in silico. Our findings indicate that stimulation parameters can be customized to each electrode in a closed-loop manner. This approach can potentially eliminate the time-consuming process of searching a broad range of parameters for optimal stimulation outcome and provide more control over personalized fitting of retinal implants.
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Alternative stimulation strategies have not been validated in vivo in humans using perceptually relevant pulse-train stimulation, and most do not address the substantial day-to-day variability in cortical responses that limits clinical reliability.
“While alternative stimulation strategies have been proposed [25–39], none have been validated in vivo in humans using perceptually relevant pulse-train stimulation, and most do not address the substantial day-to-day variability in cortical responses that limits clinical reliability.”
From Deep Learning–Based Control of Electrically Evoked Activity in Human Visual Cortex (2025) · cites this paper as [25]
Several studies have proposed more sophisticated stimulus encoding strategies to recreate a desired neural activity pattern over a given temporal window, but none of these approaches are able to predict the perceptual consequences of the resulting neural activity.
“Several studies have already proposed more sophisticated stimulus encoding strategies to recreate a desired neural activity pattern over a given temporal window (Shah et al., 2019; Spencer et al., 2019; Ghaffari et al., 2021). However, none of these approaches are able to predict the perceptual consequences of the resulting neural activity.”
From Towards a <i>Smart Bionic Eye</i> : AI-powered artificial vision for the treatment of incurable blindness (2022) · cites this paper as Ghaffari et al., 2021
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References 22 · Cited by 10
- Authors
- Ghaffari, D. H., Chang, Y.-C., Mirzakhalili, E., & Weiland, J. D.
- Affiliations
- University of Michigan (US)
- Feinstein Institute for Medical Research (US)
- Venue
- 2021 10th International IEEE/EMBS Conference on Neural Engineering (NER)
- Publisher
- IEEE
- Published
- 2021-05-04
- Pages
- 597-600
- ISSN
- 1948-3554 (ISSN-L)
- OpenAlex
- W3171806752
- Cited by
- 10
- References
- 22
- Citations by year
- 2026: 3 · 2024: 2 · 2023: 2 · 2022: 2 · 2021: 1
- FWCI
- 7.04
- Type
- conference paper
- Language
- en
- Open access
- closed
- Licence
- Locations
- International IEEE/EMBS Conference on Neural Engineering (publishedVersion)
- Funders
- Research to Prevent Blindness
- National Institutes of Health (NIH) (EY013934)
- Topics
- Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Neuroscience and Neural Engineering
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Retinal Development and Disorders
- Life Sciences › Neuroscience › Cognitive Neuroscience › Tactile and Sensory Interactions
- Keywords
- retinal prosthesis, retinal ganglion cells, epiretinal stimulation, closed-loop optimization, stimulation parameter optimization, shape perception, retinal degeneration
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