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Attention Score in Context
| Title |
Determine Neuronal Tuning Curves by Exploring Optimum Firing Rate Distribution for Information Efficiency
|
|---|---|
| Published in |
Frontiers in Computational Neuroscience, February 2017
|
| DOI | 10.3389/fncom.2017.00010 |
| Pubmed ID | |
| Authors |
Fang Han, Zhijie Wang, Hong Fan |
| Abstract |
This paper proposed a new method to determine the neuronal tuning curves for maximum information efficiency by computing the optimum firing rate distribution. Firstly, we proposed a general definition for the information efficiency, which is relevant to mutual information and neuronal energy consumption. The energy consumption is composed of two parts: neuronal basic energy consumption and neuronal spike emission energy consumption. A parameter to model the relative importance of energy consumption is introduced in the definition of the information efficiency. Then, we designed a combination of exponential functions to describe the optimum firing rate distribution based on the analysis of the dependency of the mutual information and the energy consumption on the shape of the functions of the firing rate distributions. Furthermore, we developed a rapid algorithm to search the parameter values of the optimum firing rate distribution function. Finally, we found with the rapid algorithm that a combination of two different exponential functions with two free parameters can describe the optimum firing rate distribution accurately. We also found that if the energy consumption is relatively unimportant (important) compared to the mutual information or the neuronal basic energy consumption is relatively large (small), the curve of the optimum firing rate distribution will be relatively flat (steep), and the corresponding optimum tuning curve exhibits a form of sigmoid if the stimuli distribution is normal. |
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 15 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 15 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 3 | 20% |
| Student > Ph. D. Student | 2 | 13% |
| Student > Bachelor | 1 | 7% |
| Student > Master | 1 | 7% |
| Student > Postgraduate | 1 | 7% |
| Other | 0 | 0% |
| Unknown | 7 | 47% |
| Readers by discipline | Count | As % |
|---|---|---|
| Neuroscience | 5 | 33% |
| Agricultural and Biological Sciences | 1 | 7% |
| Physics and Astronomy | 1 | 7% |
| Engineering | 1 | 7% |
| Unknown | 7 | 47% |
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 24 February 2017.
All research outputs
#20,406,219
of 22,955,959 outputs
Outputs from Frontiers in Computational Neuroscience
#1,161
of 1,347 outputs
Outputs of similar age
#270,767
of 310,778 outputs
Outputs of similar age from Frontiers in Computational Neuroscience
#19
of 23 outputs
Altmetric has tracked 22,955,959 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 1,347 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 6.1. 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 310,778 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 23 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.