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
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
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morphologies
README.html
Ca_HVA.mod *
Ca_LVAst.mod *
CaDynamics_E2.mod *
epsp.mod *
Ih.mod *
Im.mod *
K_Pst.mod *
K_Tst.mod *
Nap_Et2.mod *
NaTa_t.mod *
ProbAMPANMDA2.mod *
ProbUDFsyn2.mod *
SK_E2.mod *
SKv3_1.mod *
calcapicalthresholds_control.py
calcapicalthresholds_epsp_control.py
calcifcurves.py
calcifcurves_comb.py
calcnspikesperburst2.py
calcsteadystate.py
calcupdown2responses.py
calcupdownresponses_noisydown.py
calcupdownresponses_noisyup.py
coding.py
coding_comb.py
coding_nonprop_comb_somaticI.py
coding_nonprop_somaticI.py
collectupdownresponses_noisy.py
control_cs.sav
controlamps_cs0.sav
controlamps_cs1.sav
controlamps_cs2.sav
controlamps_cs3.sav
controlamps_cs4.sav
controlamps_cs5.sav
controlamps_cs6.sav
drawfigcomb.py
drawnspikesperburst2.py
drawupdownresponses_noisy.py
findppicoeffs.py
findppicoeffs_comb.py
findppicoeffs_complement.py
findthresholdbasalamps_coding.py
findthresholddistalamps.py
findthresholddistalamps_coding.py
findthresholddistalamps_comb.py
mutation_stuff.py
mytools.py
protocol.py
runcontrols_cs.py
savebasalsynapselocations_coding.py
savesynapselocations.py
savesynapselocations_coding.py
scalemutations_cs.py
scalings_cs.sav
setparams.py
synlocs300.0.sav
                            
: this model is built-in to neuron with suffix epsp
: Schaefer et al. 2003

COMMENT
modified from syn2.mod
injected current with exponential rise and decay current defined by
         i = 0 for t < onset and
         i=amp*((1-exp(-(t-onset)/tau0))-(1-exp(-(t-onset)/tau1)))
          for t > onset

	compare to experimental current injection:
 	i = - amp*(1-exp(-t/t1))*(exp(-t/t2))

	-> tau1==t2   tau0 ^-1 = t1^-1 + t2^-1
ENDCOMMENT
					       
INDEPENDENT {t FROM 0 TO 1 WITH 1 (ms)}

NEURON {
	POINT_PROCESS epsp
	RANGE onset, tau0, tau1, imax, i, myv
	NONSPECIFIC_CURRENT i
}
UNITS {
	(nA) = (nanoamp)
	(mV) = (millivolt)
	(umho) = (micromho)
}

PARAMETER {
	onset=0  (ms)
	tau0=0.2 (ms)
	tau1=3.0 (ms)
	imax=0 	 (nA)
	v	 (mV)
}

ASSIGNED { i (nA)  myv (mV)}

LOCAL   a[2]
LOCAL   tpeak
LOCAL   adjust
LOCAL   amp

BREAKPOINT {
	myv = v
        i = curr(t)
}

FUNCTION myexp(x) {
	if (x < -100) {
	myexp = 0
	}else{
	myexp = exp(x)
	}
}

FUNCTION curr(x) {				
	tpeak=tau0*tau1*log(tau0/tau1)/(tau0-tau1)
	adjust=1/((1-myexp(-tpeak/tau0))-(1-myexp(-tpeak/tau1)))
	amp=adjust*imax
	if (x < onset) {
		curr = 0
	}else{
		a[0]=1-myexp(-(x-onset)/tau0)
		a[1]=1-myexp(-(x-onset)/tau1)
		curr = -amp*(a[0]-a[1])
	}
}

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