ArticleiScience2024
A computational model to design wide field-of-view optic nerve neuroprostheses.
Article in iScience, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Article
- Neural Vision Restoration in Ophthalmology.Annals of biomedical engineering · 2026Review
- The optimization of neuroprosthetic interfaces relying on biophysical and surrogate digital twins.npj biomedical innovations · 2026Review
- Deep Learning-Based Control of Electrically Evoked Activity in Human Visual Cortex.bioRxiv : the preprint server for biology · 2025Article
- Decoding electroencephalographic responses to visual stimuli compatible with electrical stimulation.APL bioengineering · 2024Article
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Retinal stimulation (RS) allows restoring vision in blind patients, but it covers only a narrow region of the visual field. Optic nerve stimulation (ONS) has the potential to produce visual perceptions spanning the whole visual field, but it produces very irregular phosphenes. We introduced a geometrical model converting retinal and optic nerve firing rates into visual perceptions and vice versa and a method to estimate the best perceptions elicitable through an electrode configuration. We then compared in silico ONS and RS through simulated prosthetic vision of static and dynamic visual scenes. Both simulations and SPV experiments showed that it might be possible to reconstruct natural visual scenes with ONS and RS, and that ONS wide field-of-view allows the perception of more detail in dynamic scenarios than RS. Our findings suggest that ONS could represent an interesting approach for vision restoration and that our model can be used to optimize it.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.