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Can pathway-specific LFPs be obtained in cytoarchitectonically complex structures?

Overview of attention for article published in Frontiers in Systems Neuroscience, May 2014
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Title
Can pathway-specific LFPs be obtained in cytoarchitectonically complex structures?
Published in
Frontiers in Systems Neuroscience, May 2014
DOI 10.3389/fnsys.2014.00066
Pubmed ID
Authors

Julia Makarova, Tania Ortuño, Alejandra Korovaichuk, Javier Cudeiro, Valeri A. Makarov, Casto Rivadulla, Oscar Herreras

Abstract

Deciphering how the brain encodes the continuous flow of information contained in natural stimuli requires understanding the spontaneous activity of functional assemblies in multiple neuronal populations. A promising integrative approach that combines multisite recordings of local field potentials (LFP) with an independent component analysis (ICA) enables continuous readouts of population specific activities of functionally different neuron groups to be obtained. We previously used this technique successfully in the hippocampus, a single-layer neuronal structure. Here we provide numerical evidence that the cytoarchitectonic complexity of other brain structures does not compromise the value of the ICA-separated LFP components, given that spatial sampling of LFP is representative. The spatial distribution of an LFP component may be quite complex due to folded and multilayered structure of the neuronal aggregate. Nevertheless, the time course of each LFP component is still a reliable postsynaptic convolution of spikes fired by a homogeneous afferent population. This claim is supported by preliminary experimental data obtained in the lateral geniculate nucleus of the awake monkey.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 41 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 1 2%
Netherlands 1 2%
France 1 2%
Germany 1 2%
Unknown 37 90%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 14 34%
Researcher 11 27%
Student > Bachelor 2 5%
Professor 2 5%
Student > Master 2 5%
Other 6 15%
Unknown 4 10%
Readers by discipline Count As %
Neuroscience 14 34%
Agricultural and Biological Sciences 9 22%
Engineering 4 10%
Computer Science 2 5%
Biochemistry, Genetics and Molecular Biology 2 5%
Other 3 7%
Unknown 7 17%