Evidence map›Paper›PMID 35023828›Full record

ArticleeLife2022

Shallow neural networks trained to detect collisions recover features of visual loom-selective neurons.

Baohua Zhou, Zifan Li, Sunnie Kim, John Lafferty, Damon A Clark

Abstract read
In one paragraph

Article in eLife, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.

0numbers the graph read from it
0cells of the map it votes in
14citing papers in PubMed
–field-weighted citation impact
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

14 citing papers in PubMed.

  1. Fly navigational responses exploit plume-specific odor motion and gradient cues.Proceedings of the National Academy of Sciences of the United States of America · 2026
    Article
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Visual looming is a primitive for human emotion.bioRxiv : the preprint server for biology · 2024
    Article
  8. Article
  9. Article
  10. Article
  11. Article
  12. Article
  13. Article
  14. 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

5 authors.

Baohua ZhouDepartment of Molecular, Cellular and Developmental Biology, Yale University, New Haven, United States.ORCID 0000-0002-2627-9447
Zifan LiDepartment of Statistics and Data Science, Yale University, New Haven, United States.
Sunnie KimDepartment of Statistics and Data Science, Yale University, New Haven, United States.ORCID 0000-0002-8901-7233
John LaffertyDepartment of Statistics and Data Science, Yale University, New Haven, United States.
Damon A ClarkDepartment of Molecular, Cellular and Developmental Biology, Yale University, New Haven, United States.ORCID 0000-0001-8487-700X

Funding

Integrating visual counterevidence to detect self-motion in a small visual circuitR01EY026555 · NEI · YALE UNIVERSITY · PI CLARK, DAMON ALISTAIR · 2016 to 2025
$4.0M
NEI NIH HHS R01 EY026555
6 · The paper itself

Abstract

Animals have evolved sophisticated visual circuits to solve a vital inference problem: detecting whether or not a visual signal corresponds to an object on a collision course. Such events are detected by specific circuits sensitive to visual looming, or objects increasing in size. Various computational models have been developed for these circuits, but how the collision-detection inference problem itself shapes the computational structures of these circuits remains unknown. Here, inspired by the distinctive structures of LPLC2 neurons in the visual system of

Indexed as

Computer SimulationNerve NetAnimalsDrosophilaMotion PerceptionPhotic StimulationSensory Receptor CellsD. melanogasterinferenceloom detectionmachine learningneurosciencereceptive fieldstask optimizationvisual circuits

Identifiers

PMID35023828
PMCPMC8849349

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.