↓ Skip to main content

The What and Where of Adding Channel Noise to the Hodgkin-Huxley Equations

Overview of attention for article published in PLoS Computational Biology, November 2011
Altmetric Badge

Mentioned by

twitter
1 X user

Citations

dimensions_citation
155 Dimensions

Readers on

mendeley
187 Mendeley
citeulike
6 CiteULike
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Title
The What and Where of Adding Channel Noise to the Hodgkin-Huxley Equations
Published in
PLoS Computational Biology, November 2011
DOI 10.1371/journal.pcbi.1002247
Pubmed ID
Authors

Joshua H. Goldwyn, Eric Shea-Brown

Abstract

Conductance-based equations for electrically active cells form one of the most widely studied mathematical frameworks in computational biology. This framework, as expressed through a set of differential equations by Hodgkin and Huxley, synthesizes the impact of ionic currents on a cell's voltage--and the highly nonlinear impact of that voltage back on the currents themselves--into the rapid push and pull of the action potential. Later studies confirmed that these cellular dynamics are orchestrated by individual ion channels, whose conformational changes regulate the conductance of each ionic current. Thus, kinetic equations familiar from physical chemistry are the natural setting for describing conductances; for small-to-moderate numbers of channels, these will predict fluctuations in conductances and stochasticity in the resulting action potentials. At first glance, the kinetic equations provide a far more complex (and higher-dimensional) description than the original Hodgkin-Huxley equations or their counterparts. This has prompted more than a decade of efforts to capture channel fluctuations with noise terms added to the equations of Hodgkin-Huxley type. Many of these approaches, while intuitively appealing, produce quantitative errors when compared to kinetic equations; others, as only very recently demonstrated, are both accurate and relatively simple. We review what works, what doesn't, and why, seeking to build a bridge to well-established results for the deterministic equations of Hodgkin-Huxley type as well as to more modern models of ion channel dynamics. As such, we hope that this review will speed emerging studies of how channel noise modulates electrophysiological dynamics and function. We supply user-friendly MATLAB simulation code of these stochastic versions of the Hodgkin-Huxley equations on the ModelDB website (accession number 138950) and http://www.amath.washington.edu/~etsb/tutorials.html.

X Demographics

X Demographics

The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 187 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 6 3%
United Kingdom 4 2%
Brazil 1 <1%
Israel 1 <1%
India 1 <1%
Chile 1 <1%
Canada 1 <1%
France 1 <1%
Argentina 1 <1%
Other 1 <1%
Unknown 169 90%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 47 25%
Researcher 42 22%
Student > Bachelor 21 11%
Student > Master 17 9%
Professor 11 6%
Other 34 18%
Unknown 15 8%
Readers by discipline Count As %
Agricultural and Biological Sciences 48 26%
Engineering 33 18%
Neuroscience 23 12%
Physics and Astronomy 18 10%
Mathematics 12 6%
Other 31 17%
Unknown 22 12%
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 18 November 2011.
All research outputs
#22,938,588
of 25,576,801 outputs
Outputs from PLoS Computational Biology
#8,612
of 9,003 outputs
Outputs of similar age
#225,138
of 245,298 outputs
Outputs of similar age from PLoS Computational Biology
#131
of 141 outputs
Altmetric has tracked 25,576,801 research outputs across all sources so far. This one is in the 1st percentile – i.e., 1% of other outputs scored the same or lower than it.
So far Altmetric has tracked 9,003 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 20.4. This one is in the 1st percentile – i.e., 1% 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 245,298 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 141 others from the same source and published within six weeks on either side of this one. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.