Sympathetic Preganglionic Neurone (Briant et al. 2014)

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Accession:151482
A model of a sympathetic preganglionic neurone of muscle vasoconstrictor-type.
Reference:
1 . Briant LJ, Stalbovskiy AO, Nolan MF, Champneys AR, Pickering AE (2014) Increased intrinsic excitability of muscle vasoconstrictor preganglionic neurons may contribute to the elevated sympathetic activity in hypertensive rats. J Neurophysiol 112:2756-78 [PubMed]
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): Spinal cord lumbar motor neuron alpha ACh cell; Spinal cord sympathetic preganglionic neuron;
Channel(s): I Na,t; I L high threshold; I N; I A; I K; I K,Ca; I_AHP;
Gap Junctions:
Receptor(s):
Gene(s):
Transmitter(s):
Simulation Environment: NEURON; MATLAB;
Model Concept(s): Action Potential Initiation; Activity Patterns; Bursting; Ion Channel Kinetics; Temporal Pattern Generation; Parameter Fitting; Action Potentials; Parameter sensitivity;
Implementer(s):
Search NeuronDB for information about:  Spinal cord lumbar motor neuron alpha ACh cell; I Na,t; I L high threshold; I N; I A; I K; I K,Ca; I_AHP;
// Simulations that record the membrane potential and A-conductance for sinusoidal varying ISI NetStim, with varying levels of maximal A-conductance density.

load_file("Cell.hoc")

tstop=6000

proc init() {
finitialize(-50)
/*
if (cvode.active()) {
cvode.re_init()
} else {fcurrent()
}
frecord_init()
*/
}

steps_per_ms=1
dt=1

celcius=20

access SPNcells[0].soma

// Spiking currents need to be switched off
SPNcells[0].soma.gbar_na3=0
SPNcells[0].axon.gbar_na3=0

// These current vectors make sure that the cell is kept at -50mV and stepped down to -100mV, irrespective of the value of the parameter (GKABAR)

objectvar ClampVector[2], ClampFile[2]
ClampVector[0]=new Vector()
ClampFile[0]=new File()
ClampFile[0].ropen("ClampHundred.dat")
ClampVector[0].scanf(ClampFile[0])

ClampVector[1]=new Vector()
ClampFile[1]=new File()
ClampFile[1].ropen("ClampFiddy.dat")
ClampVector[1].scanf(ClampFile[1])

nstim=3
objectvar stim[nstim]
for i=0, nstim-1 stim[i]=new IClamp(0.5)
stim[0].amp=ClampVector[1].x[0]
stim[0].dur=3000
stim[0].del=0
stim[1].amp=ClampVector[0].x[0]
stim[1].dur=1000
stim[1].del=3000
stim[2].amp=ClampVector[1].x[0]
stim[2].dur=2000
stim[2].del=4000

nruns=201
objectvar grec[nruns], vrec[nruns]
for i=0, nruns-1 grec[i]=new Vector()
for i=0, nruns-1 vrec[i]=new Vector()

grec[0].record(&SPNcells[0].soma.gka_borgka(0.5))
vrec[0].record(&SPNcells[0].soma.v(0.5))

// first simulation for gkabar = 20mS/cm2
gkabar_borgka=0.02
run()

objectvar DataM, DataN, M, N
M=new Matrix(vrec[0].size(),202)
N=new Matrix(grec[0].size(),202)
M.setcol(1,vrec[0])
N.setcol(1,grec[0])

objectvar vdt
vdt = new Vector()
vdt.resize(vrec[0].size())
vdt.indgen(dt)

M.setcol(0,vdt)
N.setcol(0,vdt)

for i=1, nruns-1 {
print i
grec[i].record(&SPNcells[0].soma.gka_borgka(0.5))
vrec[i].record(&SPNcells[0].soma.v(0.5))

stim[0].amp=ClampVector[1].x[i]
stim[1].amp=ClampVector[0].x[i]
stim[2].amp=ClampVector[1].x[i]

print gkabar_borgka

gkabar_borgka=0.02-(0.0001*i)

run()

N.setcol(i+1,grec[i])
M.setcol(i+1,vrec[i])

}

DataN=new File()
DataN.wopen("GKABAR_Inflection.dat")
N.fprint(DataN, " %g")
DataN.close("GKABAR_Inflection.dat")

DataM=new File()
DataM.wopen("GKABAR_Inflection.dat")
M.fprint(DataM, " %g")
DataM.close("GKABAR_Inflection.dat")

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