Layer V pyramidal cell functions and schizophrenia genetics (Mäki-Marttunen et al 2019)

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Accession:249463
Study on how GWAS-identified risk genes of shizophrenia affect excitability and integration of inputs in thick-tufted layer V pyramidal cells
Reference:
1 . Mäki-Marttunen T, Devor A, Phillips WA, Dale AM, Andreassen OA, Einevoll GT (2019) Computational modeling of genetic contributions to excitability and neural coding in layer V pyramidal cells: applications to schizophrenia pathology Front. Comput. Neurosci. 13:66
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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: Neocortex;
Cell Type(s):
Channel(s): I A; I M; I h; I K,Ca; I Calcium; I A, slow; I Na,t; I Na,p; I L high threshold; I T low threshold;
Gap Junctions:
Receptor(s): AMPA; NMDA; Gaba;
Gene(s):
Transmitter(s): Glutamate; Gaba;
Simulation Environment: NEURON; Python;
Model Concept(s): Schizophrenia; Dendritic Action Potentials; Action Potential Initiation; Synaptic Integration;
Implementer(s): Maki-Marttunen, Tuomo [tuomo.maki-marttunen at tut.fi];
Search NeuronDB for information about:  AMPA; NMDA; Gaba; I Na,p; I Na,t; I L high threshold; I T low threshold; I A; I M; I h; I K,Ca; I Calcium; I A, slow; Gaba; Glutamate;
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l5pc_scz
almog
cells
README.html
BK.mod *
ca_h.mod
ca_r.mod
cad.mod *
epsp.mod *
ih.mod *
kfast.mod
kslow.mod
na.mod
ProbAMPANMDA2.mod *
ProbUDFsyn2.mod *
SK.mod *
best.params *
calcifcurves2.py
calcifcurves2_comb_one.py
calcnspikesperburst.py
calcsteadystate.py
calcupdownresponses.py
cc_run.hoc *
coding.py
coding_comb.py
coding_nonprop_somaticI.py
coding_nonprop_somaticI_comb.py
collectifcurves2_comb_one.py
collectthresholddistalamps.py
combineppicoeffs_comb_one.py
drawfigcomb.py
drawnspikesperburst.py
findppicoeffs.py
findppicoeffs_merge.py
findppicoeffs_merge_comb_one.py
findthresholdbasalamps_coding.py
findthresholddistalamps.py
findthresholddistalamps_coding.py
findthresholddistalamps_comb.py
main.hoc *
model.hoc *
model_withsyns.hoc
mosinit.hoc *
mutation_stuff.py
myrun.hoc *
myrun_withsyns.hoc
mytools.py
params.hoc *
protocol.py
savebasalsynapselocations_coding.py
savesynapselocations_coding.py
scalemutations.py
scalings_cs.sav
setparams.py
synlocs450.0.sav
                            
COMMENT

na.mod

Sodium channel, Hodgkin-Huxley style kinetics.  

Author: Alon Korngreen, MPI, 1999

ENDCOMMENT

INDEPENDENT {t FROM 0 TO 1 WITH 1 (ms)}

NEURON {
	SUFFIX na
	USEION na READ ena WRITE ina
	RANGE m, h, gna, gbar,vshiftm,vshifth,taum_scale,tauh_scale
	RANGE minf, hinf, mtau, htau
	GLOBAL a1,a2,offmt,slomt,offm,slom
	GLOBAL i1,i2,offht,sloht,offh,sloh
	GLOBAL q10, temp, tadj, vmin, vmax
}

PARAMETER {
	gbar = 0.0   	(pS/um2)	: 0.12 mho/cm2
	vshiftm =-5	(mV)		: activation voltage shift
	vshifth =-10  (mV)		: inactivation voltage shift 
								
	a1=0.058		(ms)		: activation parameters
	a2=0.114		(ms)
	offmt=-36		(mV)
	slomt=28		(mV)
	offm=-38		(mV)
	slom=10			(mV)

	i1=0.28		(ms)		: inactivation parameters
	i2=16.7 		(ms)
	offht=-60		(mV)
	sloht=25		(mV)
	offh=-66		(mV)
	sloh=6			(mV)

	
	temp = 21	(degC)		: original temp 
	q10  = 2.3				: temperature sensitivity

	v 		(mV)
	celsius		(degC)
	vmin = -120	(mV)
	vmax = 100	(mV)
}


UNITS {
	(mA) = (milliamp)
	(mV) = (millivolt)
	(pS) = (picosiemens)
	(um) = (micron)
} 

ASSIGNED {
	ina 		(mA/cm2)
	gna		(pS/um2)
	ena		(mV)
	minf 		hinf
	mtau (ms)	htau (ms)
	tadj
}
 

STATE { m h }

INITIAL { 
	mrates(v+vshiftm)
	hrates(v+vshifth)
	m = minf
	h = hinf
}

BREAKPOINT {
        SOLVE states METHOD cnexp
        gna = gbar*m*m*m*h
	ina = (1e-4) * gna * (v - ena)
} 


DERIVATIVE states {    

	mrates(v+vshiftm)
	hrates(v+vshifth)
        m' = (minf-m)/mtau
        h' = (hinf-h)/htau

}



PROCEDURE mrates(vm) {  

	:TABLE  mtau, minf DEPEND celsius FROM vmin TO vmax WITH 199

	tadj = q10^((celsius - temp)/10)
	mtau = (a1+a2*exp(-((vm-offmt)/slomt)^2))/tadj
	minf = 1/(1+exp(-(vm-offm)/slom))
}


PROCEDURE hrates(vm) {

	:TABLE  htau, hinf  DEPEND celsius  FROM vmin TO vmax WITH 199

        tadj = q10^((celsius - temp)/10)
	htau = (i1+i2*exp(-((vm-offht)/sloht)^2))/tadj
	hinf = 1/(1+exp(-(offh-vm)/sloh))
}