| Title |
Statistics of spike trains in conductance-based neural networks: Rigorous results
|
|---|---|
| Published in |
The Journal of Mathematical Neuroscience, August 2011
|
| DOI | 10.1186/2190-8567-1-8 |
| Pubmed ID | |
| Authors |
Bruno Cessac |
| Abstract |
We consider a conductance-based neural network inspired by the generalized Integrate and Fire model introduced by Rudolph and Destexhe in 1996. We show the existence and uniqueness of a unique Gibbs distribution characterizing spike train statistics. The corresponding Gibbs potential is explicitly computed. These results hold in the presence of a time-dependent stimulus and apply therefore to non-stationary dynamics. |
X Demographics
The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 34 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United Kingdom | 2 | 6% |
| Brazil | 1 | 3% |
| Unknown | 31 | 91% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 16 | 47% |
| Researcher | 5 | 15% |
| Professor > Associate Professor | 4 | 12% |
| Professor | 2 | 6% |
| Student > Postgraduate | 2 | 6% |
| Other | 4 | 12% |
| Unknown | 1 | 3% |
| Readers by discipline | Count | As % |
|---|---|---|
| Mathematics | 9 | 26% |
| Physics and Astronomy | 6 | 18% |
| Neuroscience | 5 | 15% |
| Engineering | 4 | 12% |
| Computer Science | 3 | 9% |
| Other | 4 | 12% |
| Unknown | 3 | 9% |
Attention Score in Context
This research output has an Altmetric Attention Score of 10. 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 04 June 2012.
All research outputs
#3,783,008
of 25,394,764 outputs
Outputs from The Journal of Mathematical Neuroscience
#7
of 79 outputs
Outputs of similar age
#19,115
of 134,647 outputs
Outputs of similar age from The Journal of Mathematical Neuroscience
#2
of 3 outputs
Altmetric has tracked 25,394,764 research outputs across all sources so far. Compared to these this one has done well and is in the 85th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 79 research outputs from this source. They receive a mean Attention Score of 2.7. This one has done particularly well, scoring higher than 91% of its peers.
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