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Mendeley readers
Attention Score in Context
| Title |
Linearization of excitatory synaptic integration at no extra cost
|
|---|---|
| Published in |
Journal of Computational Neuroscience, January 2018
|
| DOI | 10.1007/s10827-017-0673-5 |
| Pubmed ID | |
| Authors |
Danielle Morel, Chandan Singh, William B Levy |
| Abstract |
In many theories of neural computation, linearly summed synaptic activation is a pervasive assumption for the computations performed by individual neurons. Indeed, for certain nominally optimal models, linear summation is required. However, the biophysical mechanisms needed to produce linear summation may add to the energy-cost of neural processing. Thus, the benefits provided by linear summation may be outweighed by the energy-costs. Using voltage-gated conductances in a relatively simple neuron model, this paper quantifies the cost of linearizing dendritically localized synaptic activation. Different combinations of voltage-gated conductances were examined, and many are found to produce linearization; here, four of these models are presented. Comparing the energy-costs to a purely passive model, reveals minimal or even no additional costs in some cases. |
X Demographics
The data shown below were collected from the profiles of 4 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 | 4 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 4 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 19 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 19 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Bachelor | 4 | 21% |
| Student > Master | 3 | 16% |
| Student > Ph. D. Student | 3 | 16% |
| Student > Doctoral Student | 1 | 5% |
| Lecturer | 1 | 5% |
| Other | 3 | 16% |
| Unknown | 4 | 21% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 8 | 42% |
| Engineering | 3 | 16% |
| Mathematics | 1 | 5% |
| Neuroscience | 1 | 5% |
| Earth and Planetary Sciences | 1 | 5% |
| Other | 0 | 0% |
| Unknown | 5 | 26% |
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 02 February 2018.
All research outputs
#13,578,918
of 23,018,998 outputs
Outputs from Journal of Computational Neuroscience
#141
of 310 outputs
Outputs of similar age
#220,106
of 441,127 outputs
Outputs of similar age from Journal of Computational Neuroscience
#2
of 4 outputs
Altmetric has tracked 23,018,998 research outputs across all sources so far. This one is in the 39th percentile – i.e., 39% of other outputs scored the same or lower than it.
So far Altmetric has tracked 310 research outputs from this source. They receive a mean Attention Score of 3.5. This one has gotten more attention than average, scoring higher than 52% 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 441,127 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 48th percentile – i.e., 48% 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. This one has scored higher than 2 of them.