ModelDB: Simple and accurate Diffusion Approximation algorithm for stochastic ion channels

Simple and accurate Diffusion Approximation algorithm for stochastic ion channels

Accession: 141272

" ... We derived the (Stochastic Differential Equations) SDE explicitly for any given ion channel kinetic scheme. The resulting generic equations were surprisingly simple and interpretable – allowing an easy, transparent and efficient (Diffusion Approximation) DA implementation, avoiding unnecessary approximations. The algorithm was tested in a voltage clamp simulation and in two different current clamp simulations, yielding the same results as (Markov Chains) MC modeling. Also, the simulation efficiency of this DA method demonstrated considerable superiority over MC methods, except when short time steps or low channel numbers were used."
Reference: Orio P, Soudry D (2012) Simple, fast and accurate implementation of the diffusion approximation algorithm for stochastic ion channels with multiple States PLoS One 7(5):e36670 [PubMed]

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Model Information (Click on a link to find other models with that property)

Model Type:	Neuron or other electrically excitable cell;
Brain Region(s)/Organism:
Cell Type(s):
Channel(s):	I Na,t; I K;
Gap Junctions:
Receptor(s):
Gene(s):
Transmitter(s):
Simulation Environment:	Neuron; MATLAB; SciLab;
Model Concept(s):	Action Potentials;
Implementer(s):	Orio, Patricio [patricio.orio at uv.cl];

Search NeuronDB for information about: I Na,t; I K;

Model files

Help downloading and running models

Files to reproduce the figures from:

Orio P, Soudry D (2012) Simple, fast and accurate implementation of
the diffusion approximation algorithm for stochastic ion channels with
multiple States PLoS One 7(5):e36670

Vclamp folder:
--------------

Scilab files to generate voltage clamp simulations with the K channel
from H&H model. Figures 2, 3 and 10A

- StochHH_K2 MC Vclamp.sci : MC modeling, uncoupled particles (2A)
- StochHH_K2 DA Vclamp.sci : DA algorithm, uncoupled particles (2B)
- StochHH_K2 DAss Vclamp.sci : DA with steady state app, uncoupled
  particles (3A)
- StochHH_K2 Lss Vclamp.sci : Linaro et al. algorithm (10A)
- StochHH_K5 MC Vclamp.sci : MC modeling, coupled particles (2C)
- StochHH_K5 DA Vclamp.sci : DA algorithm, coupled particles (2D)
- StochHH_K5 DAGss Vclamp.sci : Goldwyn DA algorithm, s.s app (10A)
- StochHH_K5 DAss Vclamp.sci : DA with steady state app, coupled
  particles (3B)
- StochHH_K5 DAG Vclamp.sci : Goldwyn DA algorithm, NO s.s app

Rb model folder:
----------------

Scilab/Matlab files to generate simulations of the Rubinstein's
mammalian Ranvier node model.

Figures 4-6 and 10B.

When run, each script will generate an output file with 4 columns:
Stimulus amplitude, Firing Efficiency, Mean Firing Time and FT
variance. The name of the file will indicate algorithm, number of
channels, dt, and real time elapsed.

- detRb 2vs8.sci: This will compare the deterministic version of the
  model with coupled and uncoupled particles (no output).
- StochRb2 MC multi.sci: MC modeling, uncoupled particles
- StochRb2 DA multi.sci: DA algorithm, uncoupled particles
- StochRb2 DAss multi.sci: DA with steady state app, uncoupled
  particles
- StochRb2 Lss multi.sci: Linaro et al. algorithm
- StochRb8 MC multi.sci: MC modeling, coupled particles
- StochRb8 DA multi.sci: DA algorithm, coupled particles
- StochRb8 DAGss multi.sci: Goldwyn DA algorithm, s.s. app.
- StochRb8 DAss multi.sci: DA with steady state app, coupled particles
- StochRb8 DAG multi.sci: Goldwyn DA algorithm, NO s.s. app.

HH model folder:
----------------

Neuron files to run the stochastic H&H models (figure 7-9).  Compile
the supplied .mod files and run mosinit.hoc with nrngui. A prompt
window will appear to chose the simulation algorithm and the type of
simulation.  If the default configuration of a DA model with coupled
particles is selected (click accept), and run is pressed, the
simulation should produce a graph similar to Figure 5 top right:


 
Some Matlab files are also provided.

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