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A Novel Phase Portrait for Neuronal Excitability

Overview of attention for article published in PLOS ONE, August 2012
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Title
A Novel Phase Portrait for Neuronal Excitability
Published in
PLOS ONE, August 2012
DOI 10.1371/journal.pone.0041806
Pubmed ID
Authors

Guillaume Drion, Alessio Franci, Vincent Seutin, Rodolphe Sepulchre

Abstract

Fifty years ago, FitzHugh introduced a phase portrait that became famous for a twofold reason: it captured in a physiological way the qualitative behavior of Hodgkin-Huxley model and it revealed the power of simple dynamical models to unfold complex firing patterns. To date, in spite of the enormous progresses in qualitative and quantitative neural modeling, this phase portrait has remained a core picture of neuronal excitability. Yet, a major difference between the neurophysiology of 1961 and of 2011 is the recognition of the prominent role of calcium channels in firing mechanisms. We show that including this extra current in Hodgkin-Huxley dynamics leads to a revision of FitzHugh-Nagumo phase portrait that affects in a fundamental way the reduced modeling of neural excitability. The revisited model considerably enlarges the modeling power of the original one. In particular, it captures essential electrophysiological signatures that otherwise require non-physiological alteration or considerable complexification of the classical model. As a basic illustration, the new model is shown to highlight a core dynamical mechanism by which calcium channels control the two distinct firing modes of thalamocortical neurons.

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Mendeley readers

The data shown below were compiled from readership statistics for 54 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 3 6%
United Kingdom 2 4%
France 1 2%
Australia 1 2%
Unknown 47 87%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 15 28%
Researcher 11 20%
Professor 5 9%
Student > Bachelor 5 9%
Other 4 7%
Other 8 15%
Unknown 6 11%
Readers by discipline Count As %
Neuroscience 16 30%
Agricultural and Biological Sciences 8 15%
Physics and Astronomy 7 13%
Engineering 5 9%
Computer Science 3 6%
Other 6 11%
Unknown 9 17%