Evidence map›Paper›PMID 27918530›Full record

ArticleNature neuroscience2017

A probabilistic approach to demixing odors.

Agnieszka Grabska-Barwińska, Simon Barthelmé, Jeff Beck, Zachary F Mainen, Alexandre Pouget, Peter E Latham

Abstract read
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In one paragraph

Article in Nature neuroscience, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers.

0numbers the graph read from it
0cells of the map it votes in
34citing 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

34 citing papers in PubMed.

  1. Simultaneous detection and estimation in olfactory sensing.bioRxiv : the preprint server for biology · 2025
    Article
  2. Article
  3. Article
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  5. Haploinsufficiency ofThe Journal of neuroscience : the official journal of the Society for Neuroscience · 2023
    Article
  6. Article
  7. Article
  8. Article
  9. Article
  10. Coupled Dynamics of Stimulus-Evoked Gustatory Cortical and Basolateral Amygdalar Activity.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2023
    Article
  11. Neural Circuits for Fast Poisson Compressed Sensing in the Olfactory Bulb.Advances in neural information processing systems · 2023
    Article
  12. Article
  13. Developmental and evolutionary constraints on olfactory circuit selection.Proceedings of the National Academy of Sciences of the United States of America · 2022
    Article
  14. Article
  15. Disorder and the Neural Representation of Complex Odors.Frontiers in computational neuroscience · 2022
    Article
  16. Article
  17. Article
  18. Article
  19. Representations of uncertainty: where art thou?Current opinion in behavioral sciences · 2021
    Review
  20. A spiking neural program for sensorimotor control during foraging in flying insects.Proceedings of the National Academy of Sciences of the United States of America · 2020
    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

6 authors.

Agnieszka Grabska-BarwińskaGatsby Computational Neuroscience Unit, University College London, London, UK.
Simon BarthelméCNRS, Gipsa-lab, Grenoble, France.
Jeff BeckDepartment of Neurobiology, Duke University Medical School, Durham, North Carolina, USA.
Zachary F MainenChampalimaud Centre for the Unknown, Lisbon, Portugal.
Alexandre PougetGatsby Computational Neuroscience Unit, University College London, London, UK.
Peter E LathamGatsby Computational Neuroscience Unit, University College London, London, UK.ORCID 0000-0001-8713-9328

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The olfactory system faces a hard problem: on the basis of noisy information from olfactory receptor neurons (the neurons that transduce chemicals to neural activity), it must figure out which odors are present in the world. Odors almost never occur in isolation, and different odors excite overlapping populations of olfactory receptor neurons, so the central challenge of the olfactory system is to demix its input. Because of noise and the large number of possible odors, demixing is fundamentally a probabilistic inference task. We propose that the early olfactory system uses approximate Bayesian inference to solve it. The computations involve a dynamical loop between the olfactory bulb and the piriform cortex, with cortex explaining incoming activity from the olfactory receptor neurons in terms of a mixture of odors. The model is compatible with known anatomy and physiology, including pattern decorrelation, and it performs better than other models at demixing odors.

Indexed as

OdorantsAnimalsBayes TheoremMiceNeuronsOlfactory BulbOlfactory PathwaysOlfactory Receptor NeuronsPiriform Cortex

Identifiers

PMID27918530

What OpenQuestion holds

Textmetadata
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Registered trials

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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.