Evidence map›Paper›PMID 39712137›Full record

ArticleCognitive neurodynamics2024

Alternating chimera states and synchronization in multilayer neuronal networks with ephaptic intralayer coupling.

Heng Li, Yong Xie

Abstract read
In one paragraph

Article in Cognitive neurodynamics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

2 authors.

Heng LiState Key Laboratory for Strength and Vibration of Mechanical Structures, Shaanxi Engineering Research Center of Nondestructive Testing and Structural Integrity Evaluation, School of Aerospace Engineering, Xi'an Jiaotong University, Xi'an, 710049 China.
Yong XieState Key Laboratory for Strength and Vibration of Mechanical Structures, Shaanxi Engineering Research Center of Nondestructive Testing and Structural Integrity Evaluation, School of Aerospace Engineering, Xi'an Jiaotong University, Xi'an, 710049 China.ORCID 0000-0001-9005-4173

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Over the past decade, most of researches on the communication between the neurons are based on synapses. However, the changes in action potentials in neurons may produce complex electromagnetic fields in the media, which may also have an impact on the electrical activity of neurons. To explore this factor, we construct a two-layer neuronal network composed of identical Hindmarsh-Rose neurons. Each neuron is connected with its neighbors in the layer via magnetic connections and a neuron in the corresponding position of the other layer via electrical synapse. By adjusting the electrical coupling strength and magnetic coupling strength, we find the appearance of alternating chimera states and transient chimera states whenever the intralayer coupling is nonlocal and local, respectively. According to our study, these phenomena have not been studied in multilayer networks of this structure. And it is found that the transient chimera states only could occur when the number of coupled neighbors is small. In addition, the states of two independent networks will affect the final states of networks applying the same sufficiently large interlayer coupling strength. Our study reveals a possible effect of electrical coupling and ephaptic coupling produced together on the dynamic behavior of the neuronal networks. Meanwhile, our results suggest that it makes sense to take electromagnetic induction into neuronal models.

Indexed as

Chimera stateEphaptic couplingMemristive Hindmarsh–Rose neuronsMultilayer neuronal network

Identifiers

PMID39712137
PMCPMC11655895

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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.