Squid axon (Hodgkin, Huxley 1952) used in (Chen et al 2010) (R language)

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Accession:152897
"... Previous work showed that magnetic electrical field-induced antinoceptive action is mediated by activation of capsaicin-sensitive sensory afferents. In this study, a modified Hodgkin-Huxley model, in which TRP-like current (I-TRP) was incorporated, was implemented to predict the firing behavior of action potentials (APs), as the model neuron was exposed to sinusoidal changes in externally-applied voltage. ... Our simulation results suggest that modulation of TRP-like channels functionally expressed in small-diameter peripheral sensory neurons should be an important mechanism through which it can contribute to the firing pattern of APs."
Reference:
1 . HODGKIN AL, HUXLEY AF (1952) A quantitative description of membrane current and its application to conduction and excitation in nerve. J Physiol 117:500-44 [PubMed]
2 . Chen BS, Lo YC, Lius YC, Wu SN (2010) Effects of transient receptor potential-like current on the firing pattern of action potentials in the Hodgkin-Huxley neuron during exposure to sinusoidal external voltage. Chin J Physiol 53:423-9 [PubMed]
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Model Information (Click on a link to find other models with that property)
Model Type: Axon;
Brain Region(s)/Organism:
Cell Type(s): Squid axon;
Channel(s): I Na,t; I K;
Gap Junctions:
Receptor(s):
Gene(s):
Transmitter(s):
Simulation Environment: R;
Model Concept(s): Action Potentials;
Implementer(s): Wu, Sheng-Nan [snwu at mail.ncku.edu.tw]; Huang, Chin-Wei; Chen, Bing-Shuo;
Search NeuronDB for information about:  I Na,t; I K;
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HH-Test-R
readme.html
CJP-HH-TRP.pdf
HH-test.jpg
HH-test.R
                            
# Hodgkin-Huxley kinetics
library(deSolve)

LVmod0D <- function(Time, State, Pars) {
 with(as.list(c(State, Pars)), {
if(Time>=5&&Time<=5.5){Amp}else{Amp<-0}
taum<-1/(0.1*(V+40)/(1-exp(-(V+40)/10))+4*exp(-(V+65)/18))
minf<-(0.1*(V+40)/(1-exp(-(V+40)/10)))*taum
tauh<-1/(0.07*exp(-(V+65)/20)+(1/(1+exp(-(V+35)/10))))
hinf<-0.07*exp(-(V+65)/20)*tauh
taun<-1/(0.01*(V+55)/(1-exp(-(V+55)/10))+0.125*exp(-(V+65)/80))
ninf<-(0.01*(V+55)/(1-exp(-(V+55)/10)))*taun

dV<-(-gna*(m^3)*h*(V-50)-gk*(n^4)*(V-(-77))-gl*(V-(-54.4))+Amp)/Cm
dm<--(m-minf)/taum
dh<--(h-hinf)/tauh
dn<--(n-ninf)/taun

return(list(c(dV, dm,dh,dn)))
 })
 }
pars<-c(gna=120,gk=36,gl=0.3, Cm=1, Amp=20)

yini<-c(V=-65, m=0.052, h=0.596, n=0.317)

times <- seq(0, 20, by = 0.1)

print(system.time(
out <- ode(func = LVmod0D, y = yini, parms = pars, times = times)))

## the model output is plotted, using R function matplot
## http://www.jstatsoft.org/v33/i09/paper
matplot(out[,"time"], out[,2:3], type = "l", xlab = "time", ylab = "Membrane potential",
main = "Hodgkin-Huxley model", lwd = 2, col="blue")
# legend("topright", c("Membrane potential", "m"), col = 1:2, lty = 1:2)