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Learning to see again: biological constraints on cortical plasticity and the implications for sight restoration technologies
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Abstract
The ‘bionic eye’—so long a dream of the future—is finally becoming a reality with retinal prostheses available to patients in both the US and Europe. However, clinical experience with these implants has made it apparent that the visual information provided by these devices differs substantially from normal sight. Consequently, the ability of patients to learn to make use of this abnormal retinal input plays a critical role in whether or not some functional vision is successfully regained. The goal of the present review is to summarize the vast basic science literature on developmental and adult cortical plasticity with an emphasis on how this literature might relate to the field of prosthetic vision. We begin with describing the distortion and information loss likely to be experienced by visual prosthesis users. We then define cortical plasticity and perceptual learning, and describe what is known, and what is unknown, about visual plasticity across the hierarchy of brain regions involved in visual processing, and across different stages of life. We close by discussing what is known about brain plasticity in sight restoration patients and discuss biological mechanisms that might eventually be harnessed to improve visual learning in these patients.
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“However, this number might underestimate the real needs of implanted patients due to the perceptual and behavioural learning required to adapt to artificial vision [121,122].”
From Advances in visual prostheses: engineering and biological challenges (2022) · cites this paper as [121]
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Zhao, Z., & Asakawa, T. (2025) · BioScience Trends · doi:10.5582/bst.2025.01341 · Cited by 1
Kasowski, J., Varshney, A., & Beyeler, M. (2025) · ACM Symposium on Virtual Reality Software and Technology (VRST) · doi:10.1145/3756884.3766003 · Cited by 2
Self-citationArth, K. (2018) · theses.fr (ABES) · doi:10.70675/d5697db0z82e1z4b25z81c7zf85e85367636 · Cited by 1
Metadata
- Authors
- Beyeler, M., Rokem, A., Boynton, G. M., & Fine, I.
- Affiliations
- University of Washington (US)
- Corresponding author
- Beyeler, Michael
- Venue
- Journal of Neural Engineering
- Publisher
- IOP Publishing
- Published
- 2017-08-22
- Volume
- 14
- Issue
- 5
- Pages
- 051003
- ISSN
- 1741-2552 (ISSN-L)
- 1741-2560
- PMC
- PMC5953572
- OpenAlex
- W2950139901
- Cited by
- 102
- References
- 230
- Citations by year
- 2026: 4 · 2025: 15 · 2024: 8 · 2023: 2 · 2022: 16 · 2021: 13 · 2020: 15 · 2019: 12 · 2018: 9 · 2017: 7
- FWCI
- 1.45
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Journal of Neural Engineering (publishedVersion)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- eScholarship (California Digital Library) (submittedVersion)
- Funders
- Gordon and Betty Moore Foundation
- Washington Research Foundation (Funds for Innovation in Neuroengineering and Data-Intensive Discovery (MB))
- Alfred P Sloan Foundation
- Topics
- Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Neuroscience and Neural Engineering
- Life Sciences › Neuroscience › Cognitive Neuroscience › Visual perception and processing mechanisms
- Life Sciences › Neuroscience › Cognitive Neuroscience › Tactile and Sensory Interactions
- Keywords
- retinal prosthesis, cortical plasticity, visual plasticity, perceptual learning, vision restoration, hierarchical visual processing, developmental plasticity, adult plasticity, visual learning
- MeSH terms
- Humans, Learning, Neuronal Plasticity, Visual Cortex, Visual Perception, Visual Prosthesis, Optogenetics
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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