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 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 cell; I Na,t; I L high threshold; I N; I A; I K; I K,Ca; I_AHP;
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SPN_ModelDB
hoc_code
MATLAB_code
README.txt
borgka.mod
borgkdr.mod
cadifus2.mod
cagk.mod *
cal2.mod *
can2.mod
gap.mod
gapcalcium.mod
kadist.mod *
kahp.mod *
kaprox.mod *
na3.mod
ActivationProtocol_GKA.dat
ActivationProtocol_IA.dat
ActivationProtocol_V.dat
Cell.hoc
ClampFiddy.dat
ClampFiddy_vhalfm.dat
ClampFiddy_ZetaK.dat
ClampFiddy_ZetaM.dat
ClampHundred.dat
ClampHundred_vhalfm.dat
ClampHundred_ZetaK.dat
ClampHundred_ZetaM.dat
CurrentMagnitude_GKA.dat
CurrentMagnitude_IA.dat
CurrentMagnitude_V.dat
EPSCs_Filtered.txt
InactivationProtocol_GKA.dat
InactivationProtocol_IA.dat
InactivationProtocol_V.dat
init.hoc
mosinit.hoc *
                            
// Model Topology and Biophysics
// Creates cell template and adds membrane mechanisms
// Created: March 2011, Linford Briant

load_file("nrngui.hoc")

tstop=2000
celsius=20

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

begintemplate SPNcell
public soma, axon, mprox, mdend, ldend
create soma, axon, mprox, mdend[1], ldend[2]
public nclist
objectvar nclist
proc init() {ndendm=2
ndendl=3
create soma, axon, mprox, mdend[ndendm], ldend[ndendl]
connect mprox(0), soma(1)
connect axon(0), soma(1)
for i=0, ndendl-1 connect ldend[i](0), soma(0)
for i=0, ndendm-1 connect mdend[i](0), mprox(1)
nclist=new List()
}
endtemplate SPNcell

// nSPNcells=7 instances set-up in case we want to study postganglionic gain

nSPNcells=7
ndendm=2
ndendl=3

objectvar SPNcells[nSPNcells]
for i=0, nSPNcells-1 {SPNcells[i]=new SPNcell()}

for i=0, nSPNcells-1 SPNcells[i].soma {nseg=1 L=23 Ra=120
        insert capacitance {cm=1}
        insert pas {g_pas=8e-06 e_pas=-60}
        insert na3 {gbar_na3=0.05 ar2_na3=1 ena=55}
        insert na_ion {ena=55}
        insert borgkdr {gkdrbar_borgkdr=0.01}
        insert borgka {gkabar_borgka=0.012 vhalfn_borgka=-45 a0n_borgka=0.04 zetak_borgka=-5 a0l_borgka=0.023 gmn_borgka=0.45 zetan_borgka=-4 zetal_borgka=2 vhalfl_borgka=-67 zetam_borgka=4}
        insert cadifus2 {}
        insert morphology {diam=14}
        insert cal {gcalbar_cal=0.001}
        insert can {gcanbar_can=0.001}
        insert kahp {gkahpbar_kahp=0.0001}
        insert cagk {gkbar_cagk=0.005 d1_cagk=1.6}
        insert ca_ion {}
        insert k_ion {ek=-90}
}

for i=0, nSPNcells-1 SPNcells[i].axon {nseg=10  L=500  Ra=120
        /* First segment only */
		insert ca_ion {}
        insert capacitance {cm=1}
        insert morphology {diam=0.5}
        insert na3 {gbar_na3=0.1 ena=55 ar2_na3=1}
        insert na_ion {ena=55}
        insert borgkdr {gkdrbar_borgkdr=0.001}
        insert k_ion {ek=-90}
        insert pas {g_pas=0.0002 e_pas=-70}
		insert cal {gcalbar_cal(1)=0.001
		gcalbar_cal(0)=0}
        insert can {gcanbar_can(1)=0.008
		gcanbar_can(0)=0
		cons_can=0.5}
}

for i=0, nSPNcells-1 SPNcells[i].mprox {
nseg=5  L=25  Ra=120
insert capacitance {cm=1}
insert morphology {diam=5}
insert pas {g_pas=8e-06 e_pas=-60}
insert na3 {gbar_na3=0 ar2_na3=1}
insert na_ion {ena=55}
insert borgkdr {gkdrbar_borgkdr=0}
insert k_ion {ek=-90}
}

for i=0, nSPNcells-1 for j=0, ndendm-1 SPNcells[i].mdend[j] { 
nseg=20  L=600  Ra=120
insert capacitance {cm=1}
insert morphology {diam=2}
insert pas {g_pas=8e-06 e_pas=-60}
insert na3 {gbar_na3=0 ar2_na3=1}
insert na_ion {ena=55}
insert borgkdr {gkdrbar_borgkdr=0}
insert k_ion {ek=-90}
}

for i=0, nSPNcells-1 for j=0, ndendl-1 SPNcells[i].ldend[j] {
nseg=10  L=200  Ra=120
insert capacitance {cm=1}
insert morphology {diam=2}
insert pas {g_pas=8e-06 e_pas=-60}
insert na3 {gbar_na3=0 ar2_na3=1}
insert na_ion {ena=55}
insert borgkdr {gkdrbar_borgkdr=0}
insert borgka {gkabar_borgka=0}
insert k_ion {ek=-90}
}

// The following procedure displays the topology and segment properties for the first instance of SPNcells template, for inspection.

proc SPNcellsCheck() {
topology()
access SPNcells[0].soma
psection()
access SPNcells[0].mdend[0]
psection()
access SPNcells[0].ldend[0]
psection()
access SPNcells[0].mprox[0]
psection()
access SPNcells[0].axon
psection()
}

// Parameter fitting for _cagk

proc d1()  {
for i=0, nSPNcells-1 SPNcells[i].soma {d1_cagk=$1}
}
d1(1.6)
SPNcell[0].soma.gkbar_cagk=0

// Parameter fitting for _na3

Rd_na3=0.03
Rg_na3=0.03
SPNcells[0].soma.pert_na3=-10
SPNcells[0].axon.pert_na3=-10

// Setting passive properties of membrane in all sections

proc passive() {
g_passive=$1
e_passive=$2
SPNcells[0].mdend[0].g_pas=g_passive
SPNcells[0].mdend[1].g_pas=g_passive
SPNcells[0].ldend[0].g_pas=g_passive
SPNcells[0].ldend[1].g_pas=g_passive
SPNcells[0].ldend[2].g_pas=g_passive
SPNcells[0].mprox.g_pas=g_passive
SPNcells[0].soma.g_pas=g_passive
SPNcells[0].mdend[0].e_pas=e_passive
SPNcells[0].mdend[1].e_pas=e_passive
SPNcells[0].ldend[0].e_pas=e_passive
SPNcells[0].ldend[1].e_pas=e_passive
SPNcells[0].ldend[2].e_pas=e_passive
SPNcells[0].mprox.e_pas=e_passive
SPNcells[0].soma.e_pas=e_passive
}

passive(1.8e-05,-40)

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