ArticlePatterns (New York, N.Y.)2021
A machine learning framework to optimize optic nerve electrical stimulation for vision restoration.
Article in Patterns (New York, N.Y.), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Article
- Deep Learning-Based Control of Electrically Evoked Activity in Human Visual Cortex.bioRxiv : the preprint server for biology · 2025Article
- From innovation to clinic: Emerging strategies harnessing electrically conductive polymers to enhance electrically stimulated peripheral nerve repair.Materials today. Bio · 2025Article
- Article
- Decoding electroencephalographic responses to visual stimuli compatible with electrical stimulation.APL bioengineering · 2024Article
- Advancing sensory neuroprosthetics using artificial brain networks.Patterns (New York, N.Y.) · 2021Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Optic nerve electrical stimulation is a promising technique to restore vision in blind subjects. Machine learning methods can be used to select effective stimulation protocols, but they require a model of the stimulated system to generate enough training data. Here, we use a convolutional neural network (CNN) as a model of the ventral visual stream. A genetic algorithm drives the activation of the units in a layer of the CNN representing a cortical region toward a desired pattern, by refining the activation imposed at a layer representing the optic nerve. To simulate the pattern of activation elicited by the sites of an electrode array, a simple point-source model was introduced and its optimization process was investigated for static and dynamic scenes. Psychophysical data confirm that our stimulation evolution framework produces results compatible with natural vision. Machine learning approaches could become a very powerful tool to optimize and personalize neuroprosthetic systems.
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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.