Evidence map›Paper›PMID 27145916›Full record

ReviewAnnual review of neuroscience2016

Correlations and Neuronal Population Information.

Adam Kohn, Ruben Coen-Cagli, Ingmar Kanitscheider, Alexandre Pouget

Abstract readReview
In one paragraph

Review in Annual review of neuroscience, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 229 papers.

0numbers the graph read from it
0cells of the map it votes in
229citing 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

229 citing papers in PubMed.

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  11. Distinct system-level computations underlie perceptual variation across the visual field.Proceedings of the National Academy of Sciences of the United States of America · 2026
    Article
  12. Millimeter-scale selective amplification in the developing visual cortex.bioRxiv : the preprint server for biology · 2026
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169 more citing papers are in PubMed but not listed here.

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

4 authors.

Adam KohnDominick Purpura Department of Neuroscience, Albert Einstein College of Medicine, Bronx, New York 10461; email: adam.kohn@einstein.yu.edu.
Ruben Coen-CagliDepartment of Basic Neuroscience, University of Geneva, CH-1211 Geneva, Switzerland; email: Ruben.CoenCagli@unige.ch , alexandre.pouget@unige.ch.
Ingmar KanitscheiderDepartment of Basic Neuroscience, University of Geneva, CH-1211 Geneva, Switzerland; email: Ruben.CoenCagli@unige.ch , alexandre.pouget@unige.ch.
Alexandre PougetDepartment of Basic Neuroscience, University of Geneva, CH-1211 Geneva, Switzerland; email: Ruben.CoenCagli@unige.ch , alexandre.pouget@unige.ch.

Funding

Visual Cortical AdaptationR01EY016774 · NEI · ALBERT EINSTEIN COLLEGE OF MEDICINE, INC · PI KOHN, ADAM · 2008 to 2017
$3.7M
Learning and updating internal visual modelsR01EY024858 · NEI · ALBERT EINSTEIN COLLEGE OF MEDICINE, INC · PI KOHN, ADAM, SCHWARTZ, ODELIA · 2015 to 2017
$1.7M
NEI NIH HHS R01 EY016774NEI NIH HHS R01 EY024858
6 · The paper itself

Abstract

Brain function involves the activity of neuronal populations. Much recent effort has been devoted to measuring the activity of neuronal populations in different parts of the brain under various experimental conditions. Population activity patterns contain rich structure, yet many studies have focused on measuring pairwise relationships between members of a larger population-termed noise correlations. Here we review recent progress in understanding how these correlations affect population information, how information should be quantified, and what mechanisms may give rise to correlations. As population coding theory has improved, it has made clear that some forms of correlation are more important for information than others. We argue that this is a critical lesson for those interested in neuronal population responses more generally: Descriptions of population responses should be motivated by and linked to well-specified function. Within this context, we offer suggestions of where current theoretical frameworks fall short.

Indexed as

Artificial IntelligenceModels, NeurologicalAction PotentialsAnimalsBrainHumansNeuronsStatistics as TopicdecodingFisher informationneural codingneural variabilityperceptiontheoretical neuroscience

Identifiers

PMID27145916
PMCPMC5137197

What OpenQuestion holds

Textmetadata
LicenceTDM
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.