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Dissecting estimation of conductances in subthreshold regimes

Overview of attention for article published in Journal of Computational Neuroscience, October 2015
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Title
Dissecting estimation of conductances in subthreshold regimes
Published in
Journal of Computational Neuroscience, October 2015
DOI 10.1007/s10827-015-0576-2
Pubmed ID
Authors

Catalina Vich, Antoni Guillamon

Abstract

We study the influence of subthreshold activity in the estimation of synaptic conductances. It is known that differences between actual conductances and the estimated ones using linear regression methods can be huge in spiking regimes, so caution has been taken to remove spiking activity from experimental data before proceeding to linear estimation. However, not much attention has been paid to the influence of ionic currents active in the non-spiking regime where such linear methods are still profusely used. In this paper, we use conductance-based models to test this influence using several representative mechanisms to induce ionic subthreshold activity. In all the cases, we show that the currents activated during subthreshold activity can lead to significant errors when estimating synaptic conductance linearly. Thus, our results add a new warning message when extracting conductance traces from intracellular recordings and the conclusions concerning neuronal activity that can be drawn from them. Additionally, we present, as a proof of concept, an alternative method that takes into account the main nonlinear effects of specific ionic subthreshold currents. This method, based on the quadratization of the subthreshold dynamics, allows us to reduce the relative errors of the estimated conductances by more than one order of magnitude. In experimental conditions, under appropriate fitting to canonical models, it could be useful to obtain better estimations as well even under the presence of noise.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 13 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Spain 1 8%
Uruguay 1 8%
Unknown 11 85%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 5 38%
Professor > Associate Professor 2 15%
Student > Master 2 15%
Student > Bachelor 1 8%
Librarian 1 8%
Other 1 8%
Unknown 1 8%
Readers by discipline Count As %
Engineering 4 31%
Agricultural and Biological Sciences 2 15%
Mathematics 2 15%
Physics and Astronomy 2 15%
Neuroscience 1 8%
Other 0 0%
Unknown 2 15%
Attention Score in Context

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 29 February 2016.
All research outputs
#17,774,664
of 22,829,683 outputs
Outputs from Journal of Computational Neuroscience
#214
of 307 outputs
Outputs of similar age
#185,757
of 275,910 outputs
Outputs of similar age from Journal of Computational Neuroscience
#2
of 6 outputs
Altmetric has tracked 22,829,683 research outputs across all sources so far. This one is in the 19th percentile – i.e., 19% of other outputs scored the same or lower than it.
So far Altmetric has tracked 307 research outputs from this source. They receive a mean Attention Score of 3.5. This one is in the 27th percentile – i.e., 27% of its peers scored the same or lower than it.
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We're also able to compare this research output to 6 others from the same source and published within six weeks on either side of this one. This one has scored higher than 4 of them.