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
                            
TITLE K-fast channel from Korngreen and Sakmann 2000
: M.Migliore June 2001

NEURON {
	SUFFIX iA
	USEION k READ ek WRITE ik
        RANGE gbar,ninf,linf,taul,taun,g
        GLOBAL tq,qq, q10,vmin, vmax,tadj,temp
}


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

}

PARAMETER {
	v (mV)
	celsius		(degC)
	Tscale = 10	(degC)
	gbar=0.0 (pS/um2)
        offn=-47   (mV)
        offl=-66   (mV)
        slon=29   (mV)
        slol=10   (mV)
	qq=5
	tq=-55
	ek      (mV)
	vmin = -120	(mV)
	vmax = 100	(mV)
        temp = 21       (degC)          : original temp
        q10  = 2.3

        offmt = -71 (mV)
        slomt = 59 (mV)
        taummin = 0.34 (ms)
        taumdiff = 0.92 (ms)
        offht = -73 (mV)
        sloht = 23 (mV)
        tauhmin = 8 (ms)
        tauhdiff = 49 (ms)
	
}



STATE {
	n
        l
}

ASSIGNED {
	ik (mA/cm2)
        g  (pS/um2)
        ninf
        linf      
        taul  (ms)
        taun   (ms)
	tadj
}

INITIAL {
	rates(v)
	n=ninf
	l=linf
}


BREAKPOINT {
	SOLVE states METHOD cnexp
	g = gbar*n^4*l
	ik = (1e-4)*g*(v-ek)
}


DERIVATIVE states {     : exact when v held constant; integrates over dt step
        rates(v)
        n' = (ninf - n)/taun
        l' =  (linf - l)/taul
}

PROCEDURE rates(v (mV)) { :callable from hoc

        tadj= q10^((celsius-temp)/Tscale)
        ninf = 1/(1 + exp(-(v-offn)/slon))
        linf = 1/(1 + exp((v-offl)/slol))
        taun = (taummin+taumdiff*exp(-((offmt-v)/slomt)^2))/tadj
        taul = (tauhmin+tauhdiff*exp(-((offht-v)/sloht)^2))/tadj
}