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

k_slow.mod

voltage gated potassium channel, Hodgkin-Huxley style kinetics.  

Kinetics were fit to data from recordings of nucleated patches derived 
from pyramidal neurons. Data recordings and fits from Alon Korngreen 

Author: Alon Korngreen,  MPImF Cell Physiology, 1998,
alon@mpimf-heidelberg.mpg.de

last updated 31/7/2002 by AK

ENDCOMMENT

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

NEURON {
	SUFFIX kslow
	USEION k READ ek WRITE ik
	RANGE  a, b, b1,gkslow, gbar
	RANGE  ainf, taua, binf, taub,taub1
	GLOBAL a0, a1, a2, offma, sloma, offmb, slomb
	GLOBAL b0, b11, b2, offht1, sloht2, offht2
	GLOBAL bb0,bb1,bb2, offht3, offht4, sloht3, sloht4
	GLOBAL offh, sloh
	GLOBAL q10, temp, tadj, vmin, vmax, vshift
}

PARAMETER {
	gbar = 0   	(pS/um2)	: 
	vshift = 0	(mV)		: voltage shift (affects all)
								
	offh = -58	(mV)		: v 1/2 for inact (b) 
	sloh   = 11  (mV)		: inact slope
		
	a0   = 192.3076923 (ms mV)		: parameters for alpha and beta for activation
	a1   = 51.5995872 (ms)
	a2   = 188.6792453 (ms)	
	offma   = 11.1	(mV)
	sloma   = 13.1	(mV)
	offmb   = -1.27	(mV)
	slomb   = 71    (mV)
	
	b0   = 360	(ms)			: fast inact tau (taub) (ms) 
	b11   = 1010	(ms)	
	b2   = 23.7     (ms/mV)
	offht1   = -54      (mV)
	offht2   = -75	(mV)	
	sloht2   = 48	(mV)	

	bb0 = 2350	(ms)			: Slow inactivation tau (taub1)
	bb1 = 1380	(ms)
	bb2 = -210  (ms)
	offht3 = 0	(mV)
	offht4 = 0	(mV)
	sloht3 = 89.4454383 (mV)
	sloht4 = 32.67973856 (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 {
	ik 		(mA/cm2)
	gkslow		(pS/um2)
	ek		(mV)
	ainf 		
	binf
	taua (ms)	
	taub (ms)
	taub1 (ms)	
	tadj
}
 

STATE {a b b1}

INITIAL { 
	rates(v+vshift)
	a = ainf
	b = binf 
	b1= binf
}

BREAKPOINT {
        SOLVE states METHOD cnexp
        gkslow = gbar*a*a*(0.5*b+0.5*b1)
	  ik = (1e-4) * gkslow * (v - ek)
} 

LOCAL aexp, bexp,b1exp, z 

DERIVATIVE states {   		
        rates(v+vshift) 	
        a'  = (ainf-a)/taua
        b'  = (binf-b)/taub
	b1' = (binf-b1)/taub1
}


PROCEDURE rates(vm) {  

	LOCAL alpha, beta
	:	TABLE  taua, ainf, binf, taub, taub1  DEPEND celsius FROM vmin TO vmax WITH 199
	tadj = q10^((celsius - temp)/10)
	
	alpha=tadj/a0*(vm-offma)/(1-exp(-(vm-offma)/sloma))
	beta=tadj/a1*exp(-(vm-offmb)/slomb)-1/a2

	taua=1/(alpha+beta)
	ainf = alpha/(alpha+beta)
	
	taub = b0 + (b11+b2*(vm-offht1))*exp(-(vm-offht2)*(vm-offht2)/(sloht2*sloht2))
    	taub1=bb0+bb1*exp(-(vm-offht3)/sloht3)+bb2*exp(-(vm-offht4)/sloht4)
	binf = 1/(1+exp((vm-offh)/sloh))
}