Evidence map›Paper›PMID 19562010›Full record

ReviewPLoS computational biology2009

What is stochastic resonance? Definitions, misconceptions, debates, and its relevance to biology.

Mark D McDonnell, Derek Abbott

Abstract readReview
In one paragraph

Review in PLoS computational biology, 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 262 papers.

0numbers the graph read from it
0cells of the map it votes in
262citing 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

262 citing papers in PubMed.

  1. Trial
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  7. Subtle to significant: Enhancing heaviness perception with stochastic resonance.Journal of experimental psychology. Human perception and performance · 2026
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  8. Article
  9. Article
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  11. Article
  12. Article
  13. Review
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  15. segcsvdHuman brain mapping · 2026
    Article
  16. Article
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  19. The Hidden Benefits of Noise: Low-Frequency tRNS and Dynamic Visual Noise Enhance Visual Processing.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2025
    Article
  20. Article

202 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

2 authors.

Mark D McDonnellInstitute for Telecommunications Research, University of South Australia, Mawson Lakes, South Australia, Australia. mark.mcdonnell@unisa.edu.au
Derek Abbott

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Stochastic resonance is said to be observed when increases in levels of unpredictable fluctuations--e.g., random noise--cause an increase in a metric of the quality of signal transmission or detection performance, rather than a decrease. This counterintuitive effect relies on system nonlinearities and on some parameter ranges being "suboptimal". Stochastic resonance has been observed, quantified, and described in a plethora of physical and biological systems, including neurons. Being a topic of widespread multidisciplinary interest, the definition of stochastic resonance has evolved significantly over the last decade or so, leading to a number of debates, misunderstandings, and controversies. Perhaps the most important debate is whether the brain has evolved to utilize random noise in vivo, as part of the "neural code". Surprisingly, this debate has been for the most part ignored by neuroscientists, despite much indirect evidence of a positive role for noise in the brain. We explore some of the reasons for this and argue why it would be more surprising if the brain did not exploit randomness provided by noise--via stochastic resonance or otherwise--than if it did. We also challenge neuroscientists and biologists, both computational and experimental, to embrace a very broad definition of stochastic resonance in terms of signal-processing "noise benefits", and to devise experiments aimed at verifying that random variability can play a functional role in the brain, nervous system, or other areas of biology.

Indexed as

Models, BiologicalStochastic ProcessesBiomedical EngineeringBrainComputational BiologyModels, NeurologicalNeurosciencesSignal Processing, Computer-Assisted

Identifiers

PMID19562010
PMCPMC2660436

What OpenQuestion holds

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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.