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Cited by 39Neuroscience and Neural EngineeringRetinal Development and DisordersNeural dynamics and brain function

Computational challenges and opportunities for a bi-directional artificial retina

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Shah, Nishal P, Chichilnisky, E. J.·2020·Journal of Neural Engineering
DOI 10.1088/1741-2552/aba8b1PMC PMC13671150
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Abstract

A future artificial retina that can restore high acuity vision in blind people will rely on the capability to both read (observe) and write (control) the spiking activity of neurons using an adaptive, bi-directional and high-resolution device. Although current research is focused on overcoming the technical challenges of building and implanting such a device, exploiting its capabilities to achieve more acute visual perception will also require substantial computational advances. Using high-density large-scale recording and stimulation in the primate retina with an ex vivo multi-electrode array lab prototype, we frame several of the major computational problems, and describe current progress and future opportunities in solving them. First, we identify cell types and locations from spontaneous activity in the blind retina, and then efficiently estimate their visual response properties by using a low-dimensional manifold of inter-retina variability learned from a large experimental dataset. Second, we estimate retinal responses to a large collection of relevant electrical stimuli by passing current patterns through an electrode array, spike sorting the resulting recordings and using the results to develop a model of evoked responses. Third, we reproduce the desired responses for a given visual target by temporally dithering a diverse collection of electrical stimuli within the integration time of the visual system. Together, these novel approaches may substantially enhance artificial vision in a next-generation device.

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  • Achieving a Smart Bionic Eye will require engineering next-generation visual prostheses with large electrode counts and developing sophisticated AI systems.

    “Achieving this ambitious goal will certainly require the engineering of next-generation visual prostheses with large electrode counts (Ferlauto et al., 2018; Shah and Chichilnisky, 2020; Chen et al., 2020) and the development of sophisticated AI systems.”

    From Towards a <i>Smart Bionic Eye</i> : AI-powered artificial vision for the treatment of incurable blindness (2022) · cites this paper as Shah and Chichilnisky, 2020

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Authors
Shah, N. P., & Chichilnisky, E. J.
Affiliations
  • Stanford University (US)
  • Stanford Medicine (US)
Corresponding author
  • Shah, Nishal P
  • Chichilnisky, E. J.
ORCID
Venue
Journal of Neural Engineering
Publisher
IOP Publishing
Published
2020-10-01
Volume
17
Issue
5
Pages
055002
ISSN
  • 1741-2552 (ISSN-L)
  • 1741-2560
DOI
10.1088/1741-2552/aba8b1
PMC
PMC13671150
OpenAlex
W3094364418
Cited by
39
References
13
Citations by year
2026: 2 · 2025: 10 · 2024: 6 · 2023: 5 · 2022: 11 · 2021: 4 · 2020: 1
FWCI
2.41
Type
journal article
Language
en
Open access
Licence
Locations
  • Journal of Neural Engineering (publishedVersion)
  • PubMed (publishedVersion)
  • PubMed Central (submittedVersion)
Funders
  • Wu Tsai Neurosciences Institute
  • National Eye Institute (EY021271)
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
  • Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
Keywords
artificial retina, retinal prosthesis, spontaneous retinal activity, manifold learning, microelectrode arrays, spike sorting, electrical stimulation, dithering, visual perception
MeSH terms
Animals, Artificial Organs, Blindness, Electric Stimulation, Retina, Visual Perception

About these checks

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.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

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