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Mendeley readers
Attention Score in Context
| Title |
A Stochastic Version of the Jansen and Rit Neural Mass Model: Analysis and Numerics
|
|---|---|
| Published in |
The Journal of Mathematical Neuroscience, August 2017
|
| DOI | 10.1186/s13408-017-0046-4 |
| Pubmed ID | |
| Authors |
Markus Ableidinger, Evelyn Buckwar, Harald Hinterleitner |
| Abstract |
Neural mass models provide a useful framework for modelling mesoscopic neural dynamics and in this article we consider the Jansen and Rit neural mass model (JR-NMM). We formulate a stochastic version of it which arises by incorporating random input and has the structure of a damped stochastic Hamiltonian system with nonlinear displacement. We then investigate path properties and moment bounds of the model. Moreover, we study the asymptotic behaviour of the model and provide long-time stability results by establishing the geometric ergodicity of the system, which means that the system-independently of the initial values-always converges to an invariant measure. In the last part, we simulate the stochastic JR-NMM by an efficient numerical scheme based on a splitting approach which preserves the qualitative behaviour of the solution. |
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 % |
|---|---|---|
| Colombia | 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 43 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 43 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 8 | 19% |
| Researcher | 7 | 16% |
| Student > Bachelor | 4 | 9% |
| Student > Doctoral Student | 4 | 9% |
| Student > Master | 4 | 9% |
| Other | 7 | 16% |
| Unknown | 9 | 21% |
| Readers by discipline | Count | As % |
|---|---|---|
| Neuroscience | 12 | 28% |
| Engineering | 9 | 21% |
| Unspecified | 2 | 5% |
| Medicine and Dentistry | 2 | 5% |
| Mathematics | 1 | 2% |
| Other | 4 | 9% |
| Unknown | 13 | 30% |
Attention Score in Context
This research output has an Altmetric Attention Score of 1. 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 08 August 2017.
All research outputs
#17,887,870
of 22,996,001 outputs
Outputs from The Journal of Mathematical Neuroscience
#51
of 80 outputs
Outputs of similar age
#227,583
of 317,853 outputs
Outputs of similar age from The Journal of Mathematical Neuroscience
#3
of 4 outputs
Altmetric has tracked 22,996,001 research outputs across all sources so far. This one is in the 22nd percentile – i.e., 22% of other outputs scored the same or lower than it.
So far Altmetric has tracked 80 research outputs from this source. They receive a mean Attention Score of 2.6. 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 317,853 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 28th percentile – i.e., 28% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 4 others from the same source and published within six weeks on either side of this one.