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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Bumps in Small-World Networks
|
|---|---|
| Published in |
Frontiers in Computational Neuroscience, June 2016
|
| DOI | 10.3389/fncom.2016.00053 |
| Pubmed ID | |
| Authors |
Carlo R. Laing |
| Abstract |
We consider a network of coupled excitatory and inhibitory theta neurons which is capable of supporting stable spatially-localized "bump" solutions. We randomly add long-range and simultaneously remove short-range connections within the network to form a small-world network and investigate the effects of this rewiring on the existence and stability of the bump solution. We consider two limits in which continuum equations can be derived; bump solutions are fixed points of these equations. We can thus use standard numerical bifurcation analysis to determine the stability of these bumps and to follow them as parameters (such as rewiring probabilities) are varied. We find that under some rewiring schemes bumps are quite robust, whereas in other schemes they can become unstable via Hopf bifurcation or even be destroyed in saddle-node bifurcations. |
X Demographics
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Australia | 1 | 33% |
| Chile | 1 | 33% |
| Switzerland | 1 | 33% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 2 | 67% |
| Practitioners (doctors, other healthcare professionals) | 1 | 33% |
Mendeley readers
The data shown below were compiled from readership statistics for 23 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United Kingdom | 1 | 4% |
| Unknown | 22 | 96% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 10 | 43% |
| Student > Ph. D. Student | 6 | 26% |
| Professor | 2 | 9% |
| Student > Bachelor | 1 | 4% |
| Professor > Associate Professor | 1 | 4% |
| Other | 1 | 4% |
| Unknown | 2 | 9% |
| Readers by discipline | Count | As % |
|---|---|---|
| Physics and Astronomy | 5 | 22% |
| Engineering | 3 | 13% |
| Neuroscience | 3 | 13% |
| Agricultural and Biological Sciences | 2 | 9% |
| Mathematics | 2 | 9% |
| Other | 4 | 17% |
| Unknown | 4 | 17% |
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 16 June 2016.
All research outputs
#14,855,186
of 22,877,793 outputs
Outputs from Frontiers in Computational Neuroscience
#765
of 1,345 outputs
Outputs of similar age
#212,039
of 352,336 outputs
Outputs of similar age from Frontiers in Computational Neuroscience
#23
of 36 outputs
Altmetric has tracked 22,877,793 research outputs across all sources so far. This one is in the 33rd percentile – i.e., 33% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,345 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 6.2. This one is in the 36th percentile – i.e., 36% 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 352,336 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 37th percentile – i.e., 37% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 36 others from the same source and published within six weeks on either side of this one. This one is in the 25th percentile – i.e., 25% of its contemporaries scored the same or lower than it.