Evidence map›Paper›PMID 26763780›Full record

ArticleJournal of neurophysiology2016

A population of gap junction-coupled neurons drives recurrent network activity in a developing visual circuit.

Zhenyu Liu, Christopher M Ciarleglio, Ali S Hamodi, Carlos D Aizenman, Kara G Pratt

Abstract read
In one paragraph

Article in Journal of neurophysiology, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. 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

5 authors.

Zhenyu LiuDepartment of Zoology and Physiology, University of Wyoming, Laramie, Wyoming; and.
Christopher M CiarleglioDepartment of Neuroscience, Brown University, Providence, Rhode Island.
Ali S HamodiDepartment of Zoology and Physiology, University of Wyoming, Laramie, Wyoming; and.
Carlos D AizenmanDepartment of Neuroscience, Brown University, Providence, Rhode Island.
Kara G PrattDepartment of Zoology and Physiology, University of Wyoming, Laramie, Wyoming; and kpratt4@uwyo.edu.

Funding

Training Programs in Systems and Behavioral NeuroscienceT32MH019118 · NIMH · BROWN UNIVERSITY · PI CONNORS, BARRY W · 1989 to 2013
$1.9M
NIGMS NIH HHS P30-GM-32128NIMH NIH HHS T32 MH019118NIMH NIH HHS T32 MH019118-19
6 · The paper itself

Abstract

In many regions of the vertebrate brain, microcircuits generate local recurrent activity that aids in the processing and encoding of incoming afferent inputs. Local recurrent activity can amplify, filter, and temporally and spatially parse out incoming input. Determining how these microcircuits function is of great interest because it provides glimpses into fundamental processes underlying brain computation. Within the Xenopus tadpole optic tectum, deep layer neurons display robust recurrent activity. Although the development and plasticity of this local recurrent activity has been well described, the underlying microcircuitry is not well understood. Here, using a whole brain preparation that allows for whole cell recording from neurons of the superficial tectal layers, we identified a physiologically distinct population of excitatory neurons that are gap junctionally coupled and through this coupling gate local recurrent network activity. Our findings provide a novel role for neuronal coupling among excitatory interneurons in the temporal processing of visual stimuli.

Indexed as

AnimalsExcitatory Postsynaptic PotentialsGap JunctionsInterneuronsNeurons, AfferentSuperior ColliculiVisual PerceptionXenopusgap junctionsmicrocircuitoptic tectumrecurrent activityXenopus tadpole

Identifiers

PMID26763780
PMCPMC4808126

What Socratic 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 Socratic graph.