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

 Download zip file   Auto-launch 
Help downloading and running models
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
Citations  Citation Browser
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;
/
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
                            
TITLE hyperpolarization-activated current (H-current) 

COMMENT
Based on Williams and Stuart J. Neurophysiol 83:3177,2000
ENDCOMMENT

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

NEURON {
	SUFFIX iH
	USEION h READ eh WRITE ih VALENCE 1
	RANGE gbar, h_inf, tau, ih  
	GLOBAL t0,t1,off, slo, offt1, offt2, slot1, slot2
	GLOBAL q10, temp, tadj, vmin,vmax
	
}

UNITS {
	(mA) 	= (milliamp)
	(mV) 	= (millivolt)
	(molar)	= (1/liter)
	(mM) 	= (millimolar)
	(pS) = (picosiemens)
	(um) = (micron)

}

PARAMETER {
	v		(mV)
	
	celsius		(degC)
	eh	   (mV)     
	gbar	= 0.0	(pS/um2)

	off = -91       (mV)   		: V1/2 of activation	
	slo=6		(mV)	 	: slope of activation
	
	t0 = 2542.5883549	(ms) 	: parameters for time constant of activation    
	t1 = 11.40250855	(ms)     
	offt1 = 0		(mV)
	offt2 = 0		(mV)
	slot1 = 40.1606426		(mV)     
	slot2 = 16.1290323		(mV)
			
	temp = 21	(degC)		: original temp 
	q10  = 2.3			: temperature sensitivity
	vmin = -120 (mV)
	vmax = 100 (mV)     
	
}


ASSIGNED {
	ih		(mA/cm2)
        h_inf
        tau        (ms)
	tadj
	
}

STATE { h }


INITIAL {
	rates(v)
      	h = h_inf
}

BREAKPOINT { 
	SOLVE states METHOD cnexp
      	ih = (1e-4) * gbar * h * (v-eh)
}


DERIVATIVE states  { 

	rates(v) 
	h' = (h_inf-h)/tau  
}


PROCEDURE rates( v (mV)) {

	tadj= q10^((celsius-22)/10)
	h_inf = 1/(1+exp((v-off)/slo))	
        tau = 1/(tadj*(exp(-(v-offt1)/slot1)/t0+exp((v-offt2)/slot2)/t1))
}