A machine learning framework to optimize optic nerve electrical stimulation for vision restoration
Open accessAbstract
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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Previous attempts at electrically evoking targeted neural activity have shown varied success, often limited to simulations, retinal explants, or prefrontal cortex brain states in non-human primates, yet never in awake humans.
“Previous attempts at electrically evoking targeted neural activity have shown varied success, often limited to simulations [33, 39], retinal explants [27], or prefrontal cortex brain states in non-human primates [49], yet never in awake humans.”
From Deep Learning–Based Control of Electrically Evoked Activity in Human Visual Cortex (2025) · cites this paper as [39]
Machine learning could play a role in optimising stimulation parameters to create more naturalistic electrical stimulation patterns, as shown for optic nerve and cortical prostheses.
“Machine learning could also play a role in optimising stimulation parameters to create more naturalistic electrical stimulation patterns, as shown for optic nerve [192] and cortical [193] prostheses.”
From Advances in visual prostheses: engineering and biological challenges (2022) · cites this paper as [192]
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References 42 · Cited by 17
Research works printed but not linked: 1
- [27]Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., and Fergus, R. (2014) Intriguing properties of neural networks. 2nd Int. Conf. Learn. Represent. ICLR 2014 - Conf. Track Proc. 1–10.
- Authors
- Romeni, S., Zoccolan, D., & Micera, S.
- Affiliations
- École Polytechnique Fédérale de Lausanne (CH)
- Scuola Internazionale Superiore di Studi Avanzati (IT)
- Scuola Superiore Sant'Anna (IT)
- Corresponding author
- Micera, Silvestro
- Venue
- Patterns
- Publisher
- Elsevier BV
- Published
- 2021-07
- Volume
- 2
- Issue
- 7
- Pages
- 100286
- ISSN
- 2666-3899 (ISSN-L)
- PMC
- PMC8276026
- OpenAlex
- W3168356731
- Cited by
- 17
- References
- 42
- Citations by year
- 2026: 3 · 2025: 1 · 2024: 5 · 2023: 4 · 2022: 3 · 2021: 1
- FWCI
- 1.50
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Patterns (publishedVersion, cc-by-nc-nd)
- SSRN Electronic Journal (publishedVersion)
- PubMed Central (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- SDL (submittedVersion, cc-by-nc-nd)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung
- Fondation Bertarelli
- Topics
- Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Neuroscience and Neural Engineering
- Life Sciences › Neuroscience › Cognitive Neuroscience › Tactile and Sensory Interactions
- Life Sciences › Neuroscience › Cognitive Neuroscience › Visual perception and processing mechanisms
- Keywords
- vision restoration, convolutional neural networks, ventral stream, genetic algorithm, stimulation protocol optimization, point source model, neuroprosthetics, visual perception simulation, blind patients
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