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Mendeley readers
Attention Score in Context
| Title |
Non-Additive Coupling Enables Propagation of Synchronous Spiking Activity in Purely Random Networks
|
|---|---|
| Published in |
PLoS Computational Biology, April 2012
|
| DOI | 10.1371/journal.pcbi.1002384 |
| Pubmed ID | |
| Authors |
Raoul-Martin Memmesheimer, Marc Timme |
| Abstract |
Despite the current debate about the computational role of experimentally observed precise spike patterns it is still theoretically unclear under which conditions and how they may emerge in neural circuits. Here, we study spiking neural networks with non-additive dendritic interactions that were recently uncovered in single-neuron experiments. We show that supra-additive dendritic interactions enable the persistent propagation of synchronous activity already in purely random networks without superimposed structures and explain the mechanism underlying it. This study adds a novel perspective on the dynamics of networks with nonlinear interactions in general and presents a new viable mechanism for the occurrence of patterns of precisely timed spikes in recurrent networks. |
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 % |
|---|---|---|
| Unknown | 2 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 2 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 75 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United Kingdom | 2 | 3% |
| United States | 2 | 3% |
| Germany | 2 | 3% |
| Canada | 1 | 1% |
| Unknown | 68 | 91% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 26 | 35% |
| Student > Ph. D. Student | 23 | 31% |
| Professor | 5 | 7% |
| Professor > Associate Professor | 5 | 7% |
| Student > Master | 4 | 5% |
| Other | 8 | 11% |
| Unknown | 4 | 5% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 20 | 27% |
| Physics and Astronomy | 11 | 15% |
| Computer Science | 11 | 15% |
| Neuroscience | 10 | 13% |
| Mathematics | 6 | 8% |
| Other | 9 | 12% |
| Unknown | 8 | 11% |
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 04 November 2021.
All research outputs
#15,455,365
of 25,806,080 outputs
Outputs from PLoS Computational Biology
#6,595
of 9,043 outputs
Outputs of similar age
#101,609
of 175,060 outputs
Outputs of similar age from PLoS Computational Biology
#65
of 103 outputs
Altmetric has tracked 25,806,080 research outputs across all sources so far. This one is in the 38th percentile – i.e., 38% of other outputs scored the same or lower than it.
So far Altmetric has tracked 9,043 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 20.4. This one is in the 25th percentile – i.e., 25% 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 175,060 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 40th percentile – i.e., 40% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 103 others from the same source and published within six weeks on either side of this one. This one is in the 33rd percentile – i.e., 33% of its contemporaries scored the same or lower than it.