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A biophysical observation model for field potentials of networks of leaky integrate-and-fire neurons

Overview of attention for article published in Frontiers in Computational Neuroscience, January 2013
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Title
A biophysical observation model for field potentials of networks of leaky integrate-and-fire neurons
Published in
Frontiers in Computational Neuroscience, January 2013
DOI 10.3389/fncom.2012.00100
Pubmed ID
Authors

Peter beim Graben, Serafim Rodrigues

Abstract

We present a biophysical approach for the coupling of neural network activity as resulting from proper dipole currents of cortical pyramidal neurons to the electric field in extracellular fluid. Starting from a reduced three-compartment model of a single pyramidal neuron, we derive an observation model for dendritic dipole currents in extracellular space and thereby for the dendritic field potential (DFP) that contributes to the local field potential (LFP) of a neural population. This work aligns and satisfies the widespread dipole assumption that is motivated by the "open-field" configuration of the DFP around cortical pyramidal cells. Our reduced three-compartment scheme allows to derive networks of leaky integrate-and-fire (LIF) models, which facilitates comparison with existing neural network and observation models. In particular, by means of numerical simulations we compare our approach with an ad hoc model by Mazzoni et al. (2008), and conclude that our biophysically motivated approach yields substantial improvement.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 68 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Netherlands 2 3%
Germany 1 1%
Belarus 1 1%
Unknown 64 94%

Demographic breakdown

Readers by professional status Count As %
Researcher 16 24%
Student > Ph. D. Student 14 21%
Student > Master 6 9%
Professor > Associate Professor 5 7%
Professor 5 7%
Other 9 13%
Unknown 13 19%
Readers by discipline Count As %
Agricultural and Biological Sciences 14 21%
Engineering 10 15%
Neuroscience 10 15%
Physics and Astronomy 10 15%
Computer Science 3 4%
Other 5 7%
Unknown 16 24%
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 04 January 2013.
All research outputs
#17,675,320
of 22,691,736 outputs
Outputs from Frontiers in Computational Neuroscience
#958
of 1,336 outputs
Outputs of similar age
#210,111
of 280,671 outputs
Outputs of similar age from Frontiers in Computational Neuroscience
#82
of 131 outputs
Altmetric has tracked 22,691,736 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 1,336 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 21st percentile – i.e., 21% 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 280,671 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 22nd percentile – i.e., 22% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 131 others from the same source and published within six weeks on either side of this one. This one is in the 30th percentile – i.e., 30% of its contemporaries scored the same or lower than it.