Evidence map›Paper›PMID 33089827›Full record

ArticleJournal of neural engineering2020

Computational challenges and opportunities for a bi-directional artificial retina.

Nishal P Shah, E J Chichilnisky

Abstract read
In one paragraph

Article in Journal of neural engineering, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.

0numbers the graph read from it
0cells of the map it votes in
17citing papers in PubMed
2.7field-weighted citation impact, top 9% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

17 citing papers in PubMed, 43 citations in OpenAlex.

  1. Article
  2. Article
  3. Restoration of Sight with Electronic Retinal Prostheses.Annual review of vision science · 2025
    Review
  4. Article
  5. Article
  6. Article
  7. Review
  8. Article
  9. Electronic Retinal Prostheses.Cold Spring Harbor perspectives in medicine · 2023
    Review
  10. Towards aJournal of neural engineering · 2022
    Article
  11. Article
  12. Article
  13. Article
  14. Article
  15. Article
  16. Review
  17. Spiking Characteristics of Network-Mediated Responses Arising in Direction-Selective Ganglion Cells of Rabbit and Mouse Retinas to Electric Stimulation for Retinal Prostheses.IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society · 2021
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors at 1 institution in 1 country.

Nishal P ShahDepartment of Electrical Engineering, Stanford University, Stanford, CA, United States of America.ORCID 0000-0002-1275-0381
E J ChichilniskyHansen Experimental Physics Laboratory, Stanford University, Stanford, CA, United States of America.
Stanford University · US

Funding

Core Grant for Vision ResearchP30EY019005 · NEI · SALK INSTITUTE FOR BIOLOGICAL STUDIES · PI ALBRIGHT, THOMAS D · 2009 to 2018
$7.7M
Patterned Electrical Stimulation of the Retina for High-Resolution ProsthesesR01EY021271 · NEI · STANFORD UNIVERSITY · PI EDUARDO CHICHILNISKY · 2011 to 2026
$4.6M
Bi-directional neural interface for probing parallel visual pathwaysR01EY032900 · NEI · STANFORD UNIVERSITY · PI CHICHILNISKY, EDUARDO, COGAN, STUART F · 2021 to 2024
$2.8M
NEI NIH HHS P30 EY019005NEI NIH HHS R01 EY021271NEI NIH HHS R01 EY032900
6 · The paper itself

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.

Indexed as

Artificial OrgansBlindnessRetinaAnimalsElectric StimulationVisual Perception

Identifiers

PMID33089827
PMCPMC13671150
OpenAlexW3094364418

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.