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Attention Score in Context
| Title |
Optimum neural tuning curves for information efficiency with rate coding and finite-time window
|
|---|---|
| Published in |
Frontiers in Computational Neuroscience, June 2015
|
| DOI | 10.3389/fncom.2015.00067 |
| Pubmed ID | |
| Authors |
Fang Han, Zhijie Wang, Hong Fan, Xiaojuan Sun |
| Abstract |
An important question for neural encoding is what kind of neural systems can convey more information with less energy within a finite time coding window. This paper first proposes a finite-time neural encoding system, where the neurons in the system respond to a stimulus by a sequence of spikes that is assumed to be Poisson process and the external stimuli obey normal distribution. A method for calculating the mutual information of the finite-time neural encoding system is proposed and the definition of information efficiency is introduced. The values of the mutual information and the information efficiency obtained by using Logistic function are compared with those obtained by using other functions and it is found that Logistic function is the best one. It is further found that the parameter representing the steepness of the Logistic function has close relationship with full entropy, and that the parameter representing the translation of the function associates with the energy consumption and noise entropy tightly. The optimum parameter combinations for Logistic function to maximize the information efficiency are calculated when the stimuli and the properties of the encoding system are varied respectively. Some explanations for the results are given. The model and the method we proposed could be useful to study neural encoding system, and the optimum neural tuning curves obtained in this paper might exhibit some characteristics of a real neural system. |
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 % |
|---|---|---|
| Switzerland | 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 20 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Australia | 1 | 5% |
| Unknown | 19 | 95% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 5 | 25% |
| Researcher | 4 | 20% |
| Student > Master | 3 | 15% |
| Student > Doctoral Student | 1 | 5% |
| Lecturer > Senior Lecturer | 1 | 5% |
| Other | 1 | 5% |
| Unknown | 5 | 25% |
| Readers by discipline | Count | As % |
|---|---|---|
| Engineering | 4 | 20% |
| Neuroscience | 4 | 20% |
| Agricultural and Biological Sciences | 3 | 15% |
| Physics and Astronomy | 1 | 5% |
| Social Sciences | 1 | 5% |
| Other | 0 | 0% |
| Unknown | 7 | 35% |
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 27 May 2015.
All research outputs
#22,759,802
of 25,374,917 outputs
Outputs from Frontiers in Computational Neuroscience
#1,238
of 1,463 outputs
Outputs of similar age
#239,921
of 281,105 outputs
Outputs of similar age from Frontiers in Computational Neuroscience
#41
of 49 outputs
Altmetric has tracked 25,374,917 research outputs across all sources so far. This one is in the 1st percentile – i.e., 1% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,463 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 7.0. This one is in the 1st percentile – i.e., 1% 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 281,105 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 49 others from the same source and published within six weeks on either side of this one. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.