Evidence map›Paper›PMID 31481671›Full record

ArticleNature communications2019

Propagation of temporal and rate signals in cultured multilayer networks.

Jérémie Barral, Xiao-Jing Wang, Alex D Reyes

Abstract read
In one paragraph

Article in Nature communications, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing 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

12 citing papers in PubMed.

  1. Article
  2. Review
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. An Overview of In Vitro Biological Neural Networks for Robot Intelligence.Cyborg and bionic systems (Washington, D.C.) · 2023
    Review
  9. Review
  10. Article
  11. Article
  12. 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

3 authors.

Jérémie BarralCenter for Neural Science, New York University, New York, NY, USA. jeremie.barral@gmail.com.ORCID http://orcid.org/0000-0001-9658-7659
Xiao-Jing WangCenter for Neural Science, New York University, New York, NY, USA.
Alex D ReyesCenter for Neural Science, New York University, New York, NY, USA.

Funding

Recurrent Neual Circuit Basis of Time Integration and Decision MakingR01MH062349 · NIMH · YALE UNIVERSITY · PI WANG, XIAO-JING · 2001 to 2022
$7.2M
NIMH NIH HHS R01 MH062349
6 · The paper itself

Abstract

Analyses of idealized feedforward networks suggest that several conditions have to be satisfied in order for activity to propagate faithfully across layers. Verifying these concepts experimentally has been difficult owing to the vast number of variables that must be controlled. Here, we cultured cortical neurons in a chamber with sequentially connected compartments, optogenetically stimulated individual neurons in the first layer with high spatiotemporal resolution, and then monitored the subthreshold and suprathreshold potentials in subsequent layers. Brief stimuli delivered to the first layer evoked a short-latency transient response followed by sustained activity. Rate signals, carried by the sustained component, propagated reliably through 4 layers, unlike idealized feedforward networks, which tended strongly towards synchrony. Moreover, temporal jitter in the stimulus was transformed into a rate code and transmitted to the last layer. This novel mode of propagation occurred in the balanced excitatory-inhibitory regime and is mediated by NMDA-mediated receptors and recurrent activity.

Indexed as

Signal TransductionAction PotentialsAnimalsCells, CulturedCerebral CortexFemaleMaleMiceNeuronsOptogeneticsReaction TimeReceptors, N-Methyl-D-AspartateReceptors, N-Methyl-D-Aspartate

Identifiers

PMID31481671
PMCPMC6722076

What OpenQuestion holds

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LicenceCC BY
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.