Evidence map›Paper›PMID 28835531›Full record

ReviewJournal of neurophysiology2017

The divisive normalization model of V1 neurons: a comprehensive comparison of physiological data and model predictions.

Tadamasa Sawada, Alexander A Petrov

Open access · bronzeAbstract readReview
In one paragraph

Review in Journal of neurophysiology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.

0numbers the graph read from it
0cells of the map it votes in
16citing papers in PubMed
0.5field-weighted citation impact, top 37% 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

16 citing papers in PubMed, 20 citations in OpenAlex.

  1. Review
  2. Article
  3. V1 neurons are tuned to perceptual borders in natural scenes.Proceedings of the National Academy of Sciences of the United States of America · 2024
    Article
  4. Combined manifestation of two geometric visual illusions.Attention, perception & psychophysics · 2024
    Article
  5. Article
  6. Article
  7. Effects of normalized summation in the visual illusion of extent.Attention, perception & psychophysics · 2023
    Article
  8. Article
  9. Article
  10. Divisive normalization is an efficient code for multivariate Pareto-distributed environments.Proceedings of the National Academy of Sciences of the United States of America · 2022
    Article
  11. High-value decisions are fast and accurate, inconsistent with diminishing value sensitivity.Proceedings of the National Academy of Sciences of the United States of America · 2022
    Article
  12. Divisive normalization unifies disparate response signatures throughout the human visual hierarchy.Proceedings of the National Academy of Sciences of the United States of America · 2021
    Article
  13. Article
  14. Article
  15. Article
  16. 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 2 institutions in 2 countries.

Tadamasa SawadaSchool of Psychology, National Research University Higher School of Economics, Moscow, Russia; and tada.masa.sawada@gmail.com.ORCID 0000-0002-7167-1464
Alexander A PetrovDepartment of Psychology, Ohio State University, Columbus, Ohio.
National Research University Higher School of Economics · RUThe Ohio State University · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The physiological responses of simple and complex cells in the primary visual cortex (V1) have been studied extensively and modeled at different levels. At the functional level, the divisive normalization model (DNM; Heeger DJ.

Indexed as

Models, NeurologicalAnimalsHumansNeuronsVisual Cortexcomplex cellscomputational modelingdivisive normalizationprimary visual cortex (V1)simple cells

Identifiers

PMID28835531
PMCPMC5814712
OpenAlexW2534873031

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.