Evidence map›Paper›PMID 30366563›Full record

ReviewTrends in neurosciences2018

The Redemption of Noise: Inference with Neural Populations.

Rodrigo Echeveste, Máté Lengyel

Abstract readReview
In one paragraph

Review in Trends in neurosciences, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

  1. Article
  2. Noise in Cognition: Bug or Feature?Perspectives on psychological science : a journal of the Association for Psychological Science · 2025
    Article
  3. Brain-like Flexible Visual Inference by Harnessing Feedback-Feedforward Alignment.Advances in neural information processing systems · 2023
    Article
  4. Article
  5. 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.

Rodrigo EchevesteComputational and Biological Learning Laboratory, Department of Engineering, University of Cambridge, Cambridge, UK.
Máté LengyelComputational and Biological Learning Laboratory, Department of Engineering, University of Cambridge, Cambridge, UK; Department of Cognitive Science, Central European University, Budapest, Hungary. Electronic address: m.lengyel@eng.cam.ac.uc.uk.

Funding

Wellcome Trust
6 · The paper itself

Abstract

In 2006, Ma et al. (Nat. Neurosci. 1006;9:1432-1438) presented an elegant theory for how populations of neurons might represent uncertainty to perform Bayesian inference. Critically, according to this theory, neural variability is no longer a nuisance, but rather a vital part of how the brain encodes probability distributions and performs computations with them.

Indexed as

Models, NeurologicalAnimalsBayes TheoremBrainHumansNerve NetNeuronsProbabilityBayesian inferencecortexneural networkneural variabilityperceptionuncertainty

Identifiers

PMID30366563
PMCPMC6416224

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.