On stochastic diff. eq. models for ion channel noise in Hodgkin-Huxley neurons (Goldwyn et al. 2010)

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Accession:128502
" ... We analyze three SDE models that have been proposed as approximations to the Markov chain model: one that describes the states of the ion channels and two that describe the states of the ion channel subunits. We show that the former channel-based approach can capture the distribution of channel noise and its effect on spiking in a Hodgkin-Huxley neuron model to a degree not previously demonstrated, but the latter two subunit-based approaches cannot. ..."
Reference:
1 . Goldwyn JH, Imennov NS, Famulare M, Shea-Brown E (2011) Stochastic differential equation models for ion channel noise in Hodgkin-Huxley neurons. Phys Rev E Stat Nonlin Soft Matter Phys 83:041908 [PubMed]
Model Information (Click on a link to find other models with that property)
Model Type: Neuron or other electrically excitable cell; Channel/Receptor;
Brain Region(s)/Organism:
Cell Type(s): Squid axon;
Channel(s): I Sodium; I Potassium;
Gap Junctions:
Receptor(s):
Gene(s):
Transmitter(s):
Simulation Environment: FORTRAN;
Model Concept(s): Ion Channel Kinetics; Action Potentials; Methods; Noise Sensitivity;
Implementer(s): Goldwyn, Joshua [jhgoldwyn at gmail.com];
Search NeuronDB for information about:  I Sodium; I Potassium;
#!/bin/bash

# EXAMPLE 1:
# Run all models for a constant input (strength of DC input is 7 micro
# amp / cm^2)
# Write out first 30 interspike intervals (in ms)

END=6
for ((i=0;i<=END;i++)); do
    echo running method $i
    ./HH_run $i 100 1E7 0.01 30 7. 0. 0. 0. 0 2 123 > ${i}_output.txt
done

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