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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Estimation of the synaptic input firing rates and characterization of the stimulation effects in an auditory neuron
|
|---|---|
| Published in |
Frontiers in Computational Neuroscience, May 2015
|
| DOI | 10.3389/fncom.2015.00059 |
| Pubmed ID | |
| Authors |
Ryota Kobayashi, Jufang He, Petr Lansky |
| Abstract |
To understand information processing in neuronal circuits, it is important to infer how a sensory stimulus impacts on the synaptic input to a neuron. An increase in neuronal firing during the stimulation results from pure excitation or from a combination of excitation and inhibition. Here, we develop a method for estimating the rates of the excitatory and inhibitory synaptic inputs from a membrane voltage trace of a neuron. The method is based on a modified Ornstein-Uhlenbeck neuronal model, which aims to describe the stimulation effects on the synaptic input. The method is tested using a single-compartment neuron model with a realistic description of synaptic inputs, and it is applied to an intracellular voltage trace recorded from an auditory neuron in vivo. We find that the excitatory and inhibitory inputs increase during stimulation, suggesting that the acoustic stimuli are encoded by a combination of excitation and inhibition. |
X Demographics
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Switzerland | 1 | 50% |
| Unknown | 1 | 50% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 2 | 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 % |
|---|---|---|
| Unknown | 20 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 6 | 30% |
| Student > Ph. D. Student | 4 | 20% |
| Student > Postgraduate | 3 | 15% |
| Other | 2 | 10% |
| Student > Master | 2 | 10% |
| Other | 2 | 10% |
| Unknown | 1 | 5% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 5 | 25% |
| Neuroscience | 4 | 20% |
| Agricultural and Biological Sciences | 2 | 10% |
| Physics and Astronomy | 2 | 10% |
| Engineering | 2 | 10% |
| Other | 2 | 10% |
| Unknown | 3 | 15% |
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 05 June 2015.
All research outputs
#14,812,531
of 22,807,037 outputs
Outputs from Frontiers in Computational Neuroscience
#766
of 1,342 outputs
Outputs of similar age
#147,145
of 265,506 outputs
Outputs of similar age from Frontiers in Computational Neuroscience
#21
of 38 outputs
Altmetric has tracked 22,807,037 research outputs across all sources so far. This one is in the 32nd percentile – i.e., 32% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,342 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 6.2. 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 265,506 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 41st percentile – i.e., 41% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 38 others from the same source and published within six weeks on either side of this one. This one is in the 36th percentile – i.e., 36% of its contemporaries scored the same or lower than it.