| Title |
Simulation of networks of spiking neurons: A review of tools and strategies
|
|---|---|
| Published in |
Journal of Computational Neuroscience, July 2007
|
| DOI | 10.1007/s10827-007-0038-6 |
| Pubmed ID | |
| Authors |
Romain Brette, Michelle Rudolph, Ted Carnevale, Michael Hines, David Beeman, James M. Bower, Markus Diesmann, Abigail Morrison, Philip H. Goodman, Frederick C. Harris, Milind Zirpe, Thomas Natschläger, Dejan Pecevski, Bard Ermentrout, Mikael Djurfeldt, Anders Lansner, Olivier Rochel, Thierry Vieville, Eilif Muller, Andrew P. Davison, Sami El Boustani, Alain Destexhe |
X Demographics
The data shown below were collected from the profiles of 2 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| France | 1 | 50% |
| Unknown | 1 | 50% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 2 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 1,199 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 36 | 3% |
| United Kingdom | 28 | 2% |
| Germany | 20 | 2% |
| Canada | 11 | <1% |
| France | 10 | <1% |
| Switzerland | 5 | <1% |
| Italy | 5 | <1% |
| Spain | 4 | <1% |
| Japan | 4 | <1% |
| Other | 43 | 4% |
| Unknown | 1033 | 86% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 346 | 29% |
| Researcher | 253 | 21% |
| Student > Master | 173 | 14% |
| Student > Bachelor | 74 | 6% |
| Professor | 52 | 4% |
| Other | 182 | 15% |
| Unknown | 119 | 10% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 232 | 19% |
| Engineering | 217 | 18% |
| Computer Science | 208 | 17% |
| Neuroscience | 155 | 13% |
| Physics and Astronomy | 92 | 8% |
| Other | 147 | 12% |
| Unknown | 148 | 12% |
Attention Score in Context
This research output has an Altmetric Attention Score of 13. 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 14 February 2023.
All research outputs
#2,717,729
of 26,017,215 outputs
Outputs from Journal of Computational Neuroscience
#13
of 333 outputs
Outputs of similar age
#6,064
of 80,611 outputs
Outputs of similar age from Journal of Computational Neuroscience
#1
of 3 outputs
Altmetric has tracked 26,017,215 research outputs across all sources so far. Compared to these this one has done well and is in the 88th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 333 research outputs from this source. They receive a mean Attention Score of 3.5. This one has done particularly well, scoring higher than 95% 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 80,611 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 92% of its contemporaries.
We're also able to compare this research output to 3 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them