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Attention Score in Context
| Title |
Subcritical Hopf Bifurcation and Stochastic Resonance of Electrical Activities in Neuron under Electromagnetic Induction
|
|---|---|
| Published in |
Frontiers in Computational Neuroscience, February 2018
|
| DOI | 10.3389/fncom.2018.00006 |
| Pubmed ID | |
| Authors |
Yu-Xuan Fu, Yan-Mei Kang, Yong Xie |
| Abstract |
The FitzHugh-Nagumo model is improved to consider the effect of the electromagnetic induction on single neuron. On the basis of investigating the Hopf bifurcation behavior of the improved model, stochastic resonance in the stochastic version is captured near the bifurcation point. It is revealed that a weak harmonic oscillation in the electromagnetic disturbance can be amplified through stochastic resonance, and it is the cooperative effect of random transition between the resting state and the large amplitude oscillating state that results in the resonant phenomenon. Using the noise dependence of the mean of interburst intervals, we essentially suggest a biologically feasible clue for detecting weak signal by means of neuron model with subcritical Hopf bifurcation. These observations should be helpful in understanding the influence of the magnetic field to neural electrical activity. |
X Demographics
The data shown below were collected from the profiles of 3 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Switzerland | 1 | 33% |
| United States | 1 | 33% |
| Unknown | 1 | 33% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 3 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 18 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 18 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 6 | 33% |
| Student > Ph. D. Student | 3 | 17% |
| Professor | 1 | 6% |
| Unknown | 8 | 44% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 2 | 11% |
| Engineering | 2 | 11% |
| Physics and Astronomy | 2 | 11% |
| Mathematics | 1 | 6% |
| Neuroscience | 1 | 6% |
| Other | 1 | 6% |
| Unknown | 9 | 50% |
Attention Score in Context
This research output has an Altmetric Attention Score of 2. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 09 February 2018.
All research outputs
#14,964,325
of 23,016,919 outputs
Outputs from Frontiers in Computational Neuroscience
#766
of 1,355 outputs
Outputs of similar age
#254,328
of 437,309 outputs
Outputs of similar age from Frontiers in Computational Neuroscience
#12
of 18 outputs
Altmetric has tracked 23,016,919 research outputs across all sources so far. This one is in the 32nd percentile – i.e., 32% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,355 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 6.1. This one is in the 37th percentile – i.e., 37% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 437,309 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 38th percentile – i.e., 38% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 18 others from the same source and published within six weeks on either side of this one. This one is in the 33rd percentile – i.e., 33% of its contemporaries scored the same or lower than it.