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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Generalized Laminar Population Analysis (gLPA) for Interpretation of Multielectrode Data from Cortex
|
|---|---|
| Published in |
Frontiers in Neuroinformatics, January 2016
|
| DOI | 10.3389/fninf.2016.00001 |
| Pubmed ID | |
| Authors |
Helena T. Głąbska, Eivind Norheim, Anna Devor, Anders M. Dale, Gaute T. Einevoll, Daniel K. Wójcik |
| Abstract |
Laminar population analysis (LPA) is a method for analysis of electrical data recorded by linear multielectrodes passing through all lamina of cortex. Like principal components analysis (PCA) and independent components analysis (ICA), LPA offers a way to decompose the data into contributions from separate cortical populations. However, instead of using purely mathematical assumptions in the decomposition, LPA is based on physiological constraints, i.e., that the observed LFP (low-frequency part of signal) is driven by action-potential firing as observed in the MUA (multi-unit activity; high-frequency part of the signal). In the presently developed generalized laminar population analysis (gLPA) the set of basis functions accounting for the LFP data is extended compared to the original LPA, thus allowing for a better fit of the model to experimental data. This enhances the risk for overfitting, however, and we therefore tested various versions of gLPA on virtual LFP data in which we knew the ground truth. These synthetic data were generated by biophysical forward-modeling of electrical signals from network activity in the comprehensive, and well-known, thalamocortical network model developed by Traub and coworkers. The results for the Traub model imply that while the laminar components extracted by the original LPA method overall are in fair agreement with the ground-truth laminar components, the results may be improved by use of gLPA method with two (gLPA-2) or even three (gLPA-3) postsynaptic LFP kernels per laminar population. |
X Demographics
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Switzerland | 1 | 33% |
| Unknown | 2 | 67% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 3 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 38 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Hungary | 1 | 3% |
| United States | 1 | 3% |
| Belarus | 1 | 3% |
| Unknown | 35 | 92% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 14 | 37% |
| Student > Master | 6 | 16% |
| Researcher | 5 | 13% |
| Professor | 3 | 8% |
| Other | 2 | 5% |
| Other | 4 | 11% |
| Unknown | 4 | 11% |
| Readers by discipline | Count | As % |
|---|---|---|
| Neuroscience | 12 | 32% |
| Agricultural and Biological Sciences | 7 | 18% |
| Engineering | 5 | 13% |
| Computer Science | 3 | 8% |
| Physics and Astronomy | 2 | 5% |
| Other | 4 | 11% |
| Unknown | 5 | 13% |
Attention Score in Context
This research output has an Altmetric Attention Score of 3. 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 03 February 2016.
All research outputs
#13,220,363
of 22,840,638 outputs
Outputs from Frontiers in Neuroinformatics
#421
of 749 outputs
Outputs of similar age
#186,111
of 396,493 outputs
Outputs of similar age from Frontiers in Neuroinformatics
#7
of 11 outputs
Altmetric has tracked 22,840,638 research outputs across all sources so far. This one is in the 41st percentile – i.e., 41% of other outputs scored the same or lower than it.
So far Altmetric has tracked 749 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 8.3. This one is in the 42nd percentile – i.e., 42% 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 396,493 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 52% of its contemporaries.
We're also able to compare this research output to 11 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.