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Evidence for frequency-dependent extracellular impedance from the transfer function between extracellular and intracellular potentials

Overview of attention for article published in Journal of Computational Neuroscience, June 2010
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Title
Evidence for frequency-dependent extracellular impedance from the transfer function between extracellular and intracellular potentials
Published in
Journal of Computational Neuroscience, June 2010
DOI 10.1007/s10827-010-0250-7
Pubmed ID
Authors

Claude Bédard, Serafim Rodrigues, Noah Roy, Diego Contreras, Alain Destexhe

Abstract

We examine the properties of the transfer function F(T)=V(m)/V(LFP) between the intracellular membrane potential (V(m)) and the local field potential (V(LFP)) in cerebral cortex. We first show theoretically that, in the subthreshold regime, the frequency dependence of the extracellular medium and that of the membrane potential have a clear incidence on F(T). The calculation of F(T) from experiments and the matching with theoretical expressions is possible for desynchronized states where individual current sources can be considered as independent. Using a mean-field approximation, we obtain a method to estimate the impedance of the extracellular medium without injecting currents. We examine the transfer function for bipolar (differential) LFPs and compare to simultaneous recordings of V(m) and V(LFP) during desynchronized states in rat barrel cortex in vivo. The experimentally derived F(T) matches the one derived theoretically, only if one assumes that the impedance of the extracellular medium is frequency-dependent, and varies as 1/√ω (Warburg impedance) for frequencies between 3 and 500 Hz. This constitutes indirect evidence that the extracellular medium is non-resistive, which has many possible consequences for modeling LFPs.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 137 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 4 3%
Switzerland 3 2%
France 3 2%
Japan 3 2%
Canada 2 1%
United Kingdom 1 <1%
India 1 <1%
Germany 1 <1%
Malta 1 <1%
Other 3 2%
Unknown 115 84%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 40 29%
Researcher 37 27%
Professor > Associate Professor 14 10%
Professor 11 8%
Student > Master 11 8%
Other 19 14%
Unknown 5 4%
Readers by discipline Count As %
Agricultural and Biological Sciences 41 30%
Neuroscience 26 19%
Engineering 25 18%
Physics and Astronomy 12 9%
Medicine and Dentistry 9 7%
Other 16 12%
Unknown 8 6%
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 06 March 2017.
All research outputs
#17,660,193
of 22,669,724 outputs
Outputs from Journal of Computational Neuroscience
#213
of 306 outputs
Outputs of similar age
#83,667
of 93,871 outputs
Outputs of similar age from Journal of Computational Neuroscience
#2
of 4 outputs
Altmetric has tracked 22,669,724 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 306 research outputs from this source. They receive a mean Attention Score of 3.4. This one is in the 27th percentile – i.e., 27% of its peers scored the same or lower than it.
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