Evidence map›Paper›PMID 30733509›Full record

ArticleScientific reports2019

Strong, weak and neuron type dependent lateral inhibition in the olfactory bulb.

Ronit Shmuel, Lavi Secundo, Rafi Haddad

Open access · goldAbstract read
In one paragraph

Article in Scientific reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed
1.7field-weighted citation impact, top 20% 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

11 citing papers in PubMed, 20 citations in OpenAlex.

  1. Article
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  4. Review
  5. Article
  6. Connectivity and dynamics in the olfactory bulb.PLoS computational biology · 2022
    Article
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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

3 authors at 2 institutions in 1 country.

Ronit ShmuelGonda Multidisciplinary Brain Research Center, Bar-Ilan University, Ramat-Gan, Israel.
Lavi SecundoDepartment of Neurobiology, Weizmann Institute of Science, Rehovot, 7610001, Israel.
Rafi HaddadGonda Multidisciplinary Brain Research Center, Bar-Ilan University, Ramat-Gan, Israel. rafi.haddad@biu.ac.il.
Bar-Ilan University · ILWeizmann Institute of Science · IL

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In many sensory systems, different sensory features are transmitted in parallel by several different types of output neurons. In the mouse olfactory bulb, there are only two output neuron types, the mitral and tufted cells (M/T), which receive similar odor inputs, but they are believed to transmit different odor characteristics. How these two neuron types deliver different odor information is unclear. Here, by combining electrophysiology and optogenetics, it is shown that distinct inhibitory networks modulate M/T cell responses differently. Overall strong lateral inhibition was scarce, with most neurons receiving lateral inhibition from a handful of unorganized surrounding glomeruli (~5% on average). However, there was a considerable variability between different neuron types in the strength and frequency of lateral inhibition. Strong lateral inhibition was mostly found in neurons locked to the first half of the respiration cycle. In contrast, weak inhibition arriving from many surrounding glomeruli was relatively more common in neurons locked to the late phase of the respiration cycle. Proximal neurons could receive different levels of inhibition. These results suggest that there is considerable diversity in the way M/T cells process odors so that even neurons that receive the same odor input transmit different odor information to the cortex.

Indexed as

Neural InhibitionAnimalsFemaleMaleMiceNeuronsOlfactory Bulb

Identifiers

PMID30733509
PMCPMC6367436
OpenAlexW2921800251

What OpenQuestion holds

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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.