Real-Time Optimization of Retinal Ganglion Cell Spatial Activity in Response to Epiretinal Stimulation
Open accessAbstract
Retinal prostheses aim to improve visual perception in patients blinded by photoreceptor degeneration. However, shape and letter perception with these devices is currently limited due to low spatial resolution. Previous research has shown the retinal ganglion cell (RGC) spatial activity and phosphene shapes can vary due to the complexity of retina structure and electrode-retina interactions. Visual percepts elicited by single electrodes differ in size and shapes for different electrodes within the same subject, resulting in interference between phosphenes and an unclear image. Prior work has shown that better patient outcomes correlate with spatially separate phosphenes. In this study we use calcium imaging, in vitro retina, neural networks (NN), and an optimization algorithm to demonstrate a method to iteratively search for optimal stimulation parameters that create focal RGC activation. Our findings indicate that we can converge to stimulation parameters that result in focal RGC activation by sampling less than 1/3 of the parameter space. A similar process implemented clinically can reduce time required for optimizing implant operation and enable personalized fitting of retinal prostheses.
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These approaches supported optimization of the stimulation parameters with neural networks and a genetic algorithm, respectively.
“These approaches supported optimization of the stimulation parameters with neural networks and a genetic algorithm, respectively.”
From Visual Prostheses in the Era of Artificial Intelligence Technology (2025) · cites this paper as [94]
Two studies were evaluated in a mouse model involving the transduction of a calcium indicator and electrophysiological measurements in RGCs to provide an objective measurement of their activity.
“Two studies were evaluated in a mouse model involving the transduction of a calcium indicator94 and electrophysiological measurements93 in RGCs to provide an objective measurement of their activity.”
From Visual Prostheses in the Era of Artificial Intelligence Technology (2025) · cites this paper as [94]
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References 32 · Cited by 10
- Authors
- Haji Ghaffari, D., Akwaboah, A. D., Mirzakhalili, E., & Weiland, J. D.
- Affiliations
- Johns Hopkins University (US)
- University of Michigan (US)
- Venue
- IEEE Transactions on Neural Systems and Rehabilitation Engineering
- Published
- 2021
- Volume
- 29
- Pages
- 2733-2741
- ISSN
- 1534-4320 (ISSN-L)
- 1558-0210
- PMC
- PMC8851408
- OpenAlex
- W4200184177
- Cited by
- 10
- References
- 32
- Citations by year
- 2025: 1 · 2024: 2 · 2023: 7
- FWCI
- 1.04
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- IEEE Transactions on Neural Systems and Rehabilitation Engineering (publishedVersion, cc-by)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion, cc-by)
- Funders
- Research to Prevent Blindness
- National Institutes of Health (NIH) T32 Award (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
- Health Sciences › Medicine › Anesthesiology and Pain Medicine › Pain Management and Treatment
- Keywords
- retinal prosthesis, epiretinal stimulation, retinal ganglion cells, stimulation parameter optimization, calcium imaging, visual perception
- MeSH terms
- Electric Stimulation, Humans, Phosphenes, Retina, Retinal Degeneration, Retinal Ganglion Cells, Visual Prosthesis
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