Evidence map›Paper›PMID 31338567›Full record

ArticleJournal of mathematical biology2019

Networks of random trees as a model of neuronal connectivity.

Fioralba Ajazi, Valérie Chavez-Demoulin, Tatyana Turova

Abstract read
In one paragraph

Article in Journal of mathematical biology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. 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.

Fioralba AjaziDepartment of Mathematical Statistics, Faculty of Science, Lund University, Sölvegatan 18, 22100, Lund, Sweden.
Valérie Chavez-DemoulinFaculty of Business and Economics, University of Lausanne, 1015, Lausanne, Switzerland.
Tatyana TurovaDepartment of Mathematical Statistics, Faculty of Science, Lund University, Sölvegatan 18, 22100, Lund, Sweden. tatyana@maths.lth.se.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

We provide an analysis of a randomly grown 2-d network which models the morphological growth of dendritic and axonal arbors. From the stochastic geometry of this model we derive a dynamic graph of potential synaptic connections. We estimate standard network parameters such as degree distribution, average shortest path length and clustering coefficient, considering all these parameters as functions of time. Our results show that even a simple model with just a few parameters is capable of representing a wide spectra of architecture, capturing properties of well-known models, such as random graphs or small world networks, depending on the time of the network development. The introduced model allows not only rather straightforward simulations but it is also amenable to a rigorous analysis. This provides a base for further study of formation of synaptic connections on such networks and their dynamics due to plasticity.

Indexed as

ConnectomeModels, NeurologicalAnimalsComputer SimulationHumansMathematical ConceptsNerve NetNeuronal PlasticityStochastic ProcessesBranching processNeuronal networkRandom graph

Identifiers

PMID31338567
PMCPMC6800872

What OpenQuestion holds

Textmetadata
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.