Evidence map›Paper›PMID 22080956›Full record

ArticleNature2011

Neuronal filtering of multiplexed odour representations.

Francisca Blumhagen, Peixin Zhu, Jennifer Shum, Yan-Ping Zhang Schärer, Emre Yaksi, Karl Deisseroth, Rainer W Friedrich

Abstract read
PubMed Publisher
In one paragraph

Article in Nature, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 56 papers.

0numbers the graph read from it
0cells of the map it votes in
56citing papers in PubMed
5.9field-weighted citation impact, top 3% of its field
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

56 citing papers in PubMed, 112 citations in OpenAlex.

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  13. Differentially synchronized spiking enables multiplexed neural coding.Proceedings of the National Academy of Sciences of the United States of America · 2019
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  20. A Rate-Reduced Neuron Model for Complex Spiking Behavior.Journal of mathematical neuroscience · 2017
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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

7 authors at 3 institutions in 3 countries.

Francisca BlumhagenFriedrich Miescher Institute for Biomedical Research, Maulbeerstrasse 66, 4058 Basel, Switzerland.
Peixin Zhu
Jennifer Shum
Yan-Ping Zhang Schärer
Emre Yaksi
Karl Deisseroth
Rainer W Friedrich
Friedrich Miescher Institute · CHStanford University · USNeuroelectronics Research Flanders · BE

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Neuronal activity patterns contain information in their temporal structure, indicating that information transfer between neurons may be optimized by temporal filtering. In the zebrafish olfactory bulb, subsets of output neurons (mitral cells) engage in synchronized oscillations during odour responses, but information about odour identity is contained mostly in non-oscillatory firing rate patterns. Using optogenetic manipulations and odour stimulation, we found that firing rate responses of neurons in the posterior zone of the dorsal telencephalon (Dp), a target area homologous to olfactory cortex, were largely insensitive to oscillatory synchrony of mitral cells because passive membrane properties and synaptic currents act as low-pass filters. Nevertheless, synchrony influenced spike timing. Moreover, Dp neurons responded primarily during the decorrelated steady state of mitral cell activity patterns. Temporal filtering therefore tunes Dp neurons to components of mitral cell activity patterns that are particularly informative about precise odour identity. These results demonstrate how temporal filtering can extract specific information from multiplexed neuronal codes.

Indexed as

Models, NeurologicalAction PotentialsAnimalsNeuronsOdorantsOlfactory BulbOlfactory PathwaysPhotic StimulationPhysical StimulationTime FactorsZebrafish

Identifiers

PMID22080956
OpenAlexW2002830276

What OpenQuestion holds

Textmetadata
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.