Evidence map›Paper›PMID 27764674›Full record

ArticleNeuron2016

Neural Variability and Sampling-Based Probabilistic Representations in the Visual Cortex.

Gergő Orbán, Pietro Berkes, József Fiser, Máté Lengyel

Abstract read
In one paragraph

Article in Neuron, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 120 papers.

0numbers the graph read from it
0cells of the map it votes in
120citing 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

120 citing papers in PubMed.

  1. Article
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  7. Review
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  9. Increased Perceptual Reliability Reduces Membrane Potential Variability in Cortical Neurons.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2026
    Article
  10. Review
  11. Neural mechanisms of flexible perceptual inference.PLoS computational biology · 2025
    Article
  12. Article
  13. Article
  14. Continuous partitioning of neuronal variability.bioRxiv : the preprint server for biology · 2025
    Article
  15. Article
  16. Article
  17. Article
  18. Article
  19. Article
  20. Article

60 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.

Gergő OrbánComputational and Biological Learning Lab, Department of Engineering, University of Cambridge, Cambridge CB2 1PZ, UK; MTA Wigner Research Center for Physics, Budapest 1121, Hungary. Electronic address: orban.gergo@wigner.mta.hu.
Pietro BerkesVolen National Center for Complex Systems, Brandeis University, Waltham, MA 02454, USA.
József FiserVolen National Center for Complex Systems, Brandeis University, Waltham, MA 02454, USA; Department of Cognitive Science, Central European University, Budapest 1051, Hungary.
Máté LengyelComputational and Biological Learning Lab, Department of Engineering, University of Cambridge, Cambridge CB2 1PZ, UK; Department of Cognitive Science, Central European University, Budapest 1051, Hungary.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Neural responses in the visual cortex are variable, and there is now an abundance of data characterizing how the magnitude and structure of this variability depends on the stimulus. Current theories of cortical computation fail to account for these data; they either ignore variability altogether or only model its unstructured Poisson-like aspects. We develop a theory in which the cortex performs probabilistic inference such that population activity patterns represent statistical samples from the inferred probability distribution. Our main prediction is that perceptual uncertainty is directly encoded by the variability, rather than the average, of cortical responses. Through direct comparisons to previously published data as well as original data analyses, we show that a sampling-based probabilistic representation accounts for the structure of noise, signal, and spontaneous response variability and correlations in the primary visual cortex. These results suggest a novel role for neural variability in cortical dynamics and computations.

Indexed as

Models, NeurologicalProbabilityAnimalsBayes TheoremHumansNeuronsStochastic ProcessesUncertaintyVisual CortexVisual PerceptionBayesian computationsnatural imagesnoise correlationsnormative modelspontaneous activitystochastic samplingtheoryV1variabilityvision

Identifiers

PMID27764674
PMCPMC5077700

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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.