Evidence map›Paper›PMID 34556573›Full record

ArticleProceedings of the National Academy of Sciences of the United States of America2021

Scene statistics and noise determine the relative arrangement of receptive field mosaics.

Na Young Jun, Greg D Field, John Pearson

Abstract read
In one paragraph

Article in Proceedings of the National Academy of Sciences of the United States of America, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. Efficient coding, channel capacity, and the emergence of retinal mosaics.Advances in neural information processing systems · 2022
    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

3 authors.

Na Young JunDepartment of Neurobiology, Duke University, Durham, NC 27708.ORCID 0000-0002-8841-3947
Greg D FieldDepartment of Neurobiology, Duke University, Durham, NC 27708.ORCID 0000-0001-5942-2679
John PearsonDepartment of Neurobiology, Duke University, Durham, NC 27708; john.pearson@duke.edu.ORCID 0000-0002-9876-7837

Funding

Receptive field coordination across mosaics of diverse retinal ganglion cell types in the mammalian retinaR01EY031396 · NEI · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI FIELD, GREGORY DARIN · 2020 to 2023
$1.4M
NEI NIH HHS R01 EY031396
6 · The paper itself

Abstract

Many sensory systems utilize parallel ON and OFF pathways that signal stimulus increments and decrements, respectively. These pathways consist of ensembles or grids of ON and OFF detectors spanning sensory space. Yet, encoding by opponent pathways raises a question: How should grids of ON and OFF detectors be arranged to optimally encode natural stimuli? We investigated this question using a model of the retina guided by efficient coding theory. Specifically, we optimized spatial receptive fields and contrast response functions to encode natural images given noise and constrained firing rates. We find that the optimal arrangement of ON and OFF receptive fields exhibits a transition between aligned and antialigned grids. The preferred phase depends on detector noise and the statistical structure of the natural stimuli. These results reveal that noise and stimulus statistics produce qualitative shifts in neural coding strategies and provide theoretical predictions for the configuration of opponent pathways in the nervous system.

Indexed as

Models, NeurologicalNoiseAnimalsHumansPhotic StimulationRetinaSignal-To-Noise RatioVisual FieldsVisual PathwaysVisual Perceptioncomputational modelefficient codingmosaicretina

Identifiers

PMID34556573
PMCPMC8488585

What OpenQuestion holds

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LicenceCC BY
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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.