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;
load_file("Cell.hoc")

proc init() {
finitialize(-55)
/*
if (cvode.active()) {
cvode.re_init()
} else {fcurrent()
}
frecord_init()
*/
}

// A series of homogenous bursts; recording [Ca^2+]i to play into the Bennett model.
// The bursts within this file occur every 10seconds, giving ~10 spikes (average firing frequency of 1Hz).

SPNcells[0].soma.gkbar_cagk=0

access SPNcells[0].soma
SPNcells[0].soma.gkabar_borgka=0.02

// The D-current was switched off for this simulation, and the Ca-activated K current was ammended; d1_cagk=1.6. We are using Cell.hoc, which already has these ammendments made within.

steps_per_ms=1
dt=1
tstop=20e3

objectvar stim[2]
stim = new IClamp(0.5)
stim.amp=5
stim.dur=1
stim.del=10e3

// Need to open "injected current amplitudes" from the -55mV voltage-clamp protocol clamp.hoc.

objectvar ClampVector, ClampFile
ClampVector=new Vector()
ClampFile=new File()
ClampFile.ropen("ClampFiddyFive.dat")
ClampVector.scanf(ClampFile)

stim[1]=new IClamp(0.5)
stim[1].amp=ClampVector.x[0]
stim[1].dur=20e3
stim[1].del=0

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))

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 j=1, nruns-1 {
print j, gkabar_borgka

SPNcells[0].soma.gkabar_borgka=0.02 - j*0.0001

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

stim[1].amp=ClampVector.x[j]

run()

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

}

// Save matrix of recorded vectors to .dat dataset.

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

Loading data, please wait...