Evidence map›Paper›PMID 28174614›Full record

ArticleCognitive neurodynamics2017

Weak electric fields detectability in a noisy neural network.

Jia Zhao, Bin Deng, Yingmei Qin, Cong Men, Jiang Wang, Xile Wei, Jianbing Sun

Open access · bronzeAbstract read
In one paragraph

Article in Cognitive neurodynamics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.

0numbers the graph read from it
0cells of the map it votes in
14citing papers in PubMed
4.1field-weighted citation impact, top 6% 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

14 citing papers in PubMed, 29 citations in OpenAlex.

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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

7 authors at 3 institutions in 1 country.

Jia ZhaoKey Laboratory of Cognition and Personality (Ministry of Education) and School of Psychology, Southwest University, Chongqing, China.
Bin DengSchool of Electrical Engineering and Automation, Tianjin University, Tianjin, China.
Yingmei QinTianjin Key Laboratory of Information Sensing and Inteligent Control, Tianjin University of Technology and Education, Tianjin, China.
Cong MenSchool of Electrical Engineering and Automation, Tianjin University, Tianjin, China.
Jiang WangSchool of Electrical Engineering and Automation, Tianjin University, Tianjin, China.
Xile WeiSchool of Electrical Engineering and Automation, Tianjin University, Tianjin, China.
Jianbing SunSchool of Electrical Engineering and Automation, Tianjin University, Tianjin, China.
Tianjin University · CNSouthwest University · CNTianjin University of Technology and Education · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

We investigate the detectability of weak electric field in a noisy neural network based on Izhikevich neuron model systematically. The neural network is composed of excitatory and inhibitory neurons with similar ratio as that in the mammalian neocortex, and the axonal conduction delays between neurons are also considered. It is found that the noise intensity can modulate the detectability of weak electric field. Stochastic resonance (SR) phenomenon induced by white noise is observed when the weak electric field is added to the network. It is interesting that SR almost disappeared when the connections between neurons are cancelled, suggesting the amplification effects of the neural coupling on the synchronization of neuronal spiking. Furthermore, the network parameters, such as the connection probability, the synaptic coupling strength, the scale of neuron population and the neuron heterogeneity, can also affect the detectability of the weak electric field. Finally, the model sensitivity is studied in detail, and results show that the neural network model has an optimal region for the detectability of weak electric field signal.

Indexed as

DetectabilityIzhikevich neuron modelStochastic resonanceWeak electric field

Identifiers

PMID28174614
PMCPMC5264753
OpenAlexW2519751733

What OpenQuestion 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 OpenQuestion graph.