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;
// Determines dependency of IA on prepulse duration
// cf. Figure 2(B) in Whyment et al. 2011
// Created Linford Briant October 2013

load_file("Cell.hoc")

tstop=5000
steps_per_ms=1
dt=1

celcius=20

access SPNcells[0].soma
SPNcells[0].soma.gbar_na3=0
SPNcells[0].axon.gbar_na3=0

objectvar stim
stim=new VClamp(0.5)
stim.amp[0]=0
stim.dur[0]=3000
stim.dur[1]=0
stim.amp[1]=-90
stim.amp[2]=0
stim.dur[2]=2000

proc stimDUR() {
stim.dur[1]=$1
}

// Following sets up all recording vectors, the time vector, and the database matrices:

nstim=21

objectvar IArec[nstim], grec[nstim], vrec[nstim], EKrec[nstim]
for i=0, nstim-1 IArec[i]=new Vector()
for i=0, nstim-1 grec[i]=new Vector()
for i=0, nstim-1 vrec[i]=new Vector()
for i=0, nstim-1 EKrec[i]=new Vector()

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

// Sets up vector which is the A-current rather than the A-conductance:

objectvar DataM, DataN, DataK, M, N, K
M=new Matrix(vrec[0].size(),22)
N=new Matrix(vrec[0].size(),22)
K=new Matrix(vrec[0].size(),22)

N.setcol(1,grec[0])
K.setcol(1,vrec[0])

EKrec[0]=vrec[0].sub(ek)
IArec[0]=grec[0].mul(EKrec[0])

M.setcol(1,IArec[0])

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

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

// Now that matrices and protocol have been set up, we can run the main procudure:

for i=1, nstim-1 {

print i

stimDUR(0+i*5)

// This will do 21 simulations, with stimDUR() between 5ms (the initial) and 100ms in 5ms increments.

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

run()

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

// Need to set columns for these two matrices before IA else they get overwritten.

EKrec[i]=vrec[i].sub(ek)
IArec[i]=grec[i].mul(EKrec[i])

M.setcol(i+1,IArec[i])

}

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

DataK=new File()
DataK.wopen("PrepulseAmplitude_V.dat")
K.fprint(DataK, " %g")
DataK.close("PrepulseAmplitude_V.dat")

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

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