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X Demographics
Mendeley readers
Attention Score in Context
| Title |
The Energy Coding of a Structural Neural Network Based on the Hodgkin–Huxley Model
|
|---|---|
| Published in |
Frontiers in Neuroscience, March 2018
|
| DOI | 10.3389/fnins.2018.00122 |
| Pubmed ID | |
| Authors |
Zhenyu Zhu, Rubin Wang, Fengyun Zhu |
| Abstract |
Based on the Hodgkin-Huxley model, the present study established a fully connected structural neural network to simulate the neural activity and energy consumption of the network by neural energy coding theory. The numerical simulation result showed that the periodicity of the network energy distribution was positively correlated to the number of neurons and coupling strength, but negatively correlated to signal transmitting delay. Moreover, a relationship was established between the energy distribution feature and the synchronous oscillation of the neural network, which showed that when the proportion of negative energy in power consumption curve was high, the synchronous oscillation of the neural network was apparent. In addition, comparison with the simulation result of structural neural network based on the Wang-Zhang biophysical model of neurons showed that both models were essentially consistent. |
X Demographics
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Brazil | 1 | 14% |
| Switzerland | 1 | 14% |
| Unknown | 5 | 71% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 7 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 35 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 35 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 5 | 14% |
| Researcher | 5 | 14% |
| Student > Master | 5 | 14% |
| Student > Bachelor | 3 | 9% |
| Student > Postgraduate | 2 | 6% |
| Other | 5 | 14% |
| Unknown | 10 | 29% |
| Readers by discipline | Count | As % |
|---|---|---|
| Engineering | 8 | 23% |
| Neuroscience | 6 | 17% |
| Physics and Astronomy | 2 | 6% |
| Biochemistry, Genetics and Molecular Biology | 1 | 3% |
| Agricultural and Biological Sciences | 1 | 3% |
| Other | 5 | 14% |
| Unknown | 12 | 34% |
Attention Score in Context
This research output has an Altmetric Attention Score of 100. 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 21 December 2018.
All research outputs
#424,166
of 25,382,440 outputs
Outputs from Frontiers in Neuroscience
#188
of 11,542 outputs
Outputs of similar age
#9,742
of 344,853 outputs
Outputs of similar age from Frontiers in Neuroscience
#9
of 242 outputs
Altmetric has tracked 25,382,440 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 98th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 11,542 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 11.0. This one has done particularly well, scoring higher than 98% of its peers.
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 344,853 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 97% of its contemporaries.
We're also able to compare this research output to 242 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 96% of its contemporaries.