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 [tuomomm at uio.no];
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
hay
models
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
                            
: SK-type calcium-activated potassium current
: Reference : Kohler et al. 1996

NEURON {
       SUFFIX SK_E2
       USEION k READ ek WRITE ik
       USEION ca READ cai
       RANGE gSK_E2bar, gSK_E2, ik, offc, zTau, sloc
}

UNITS {
      (mV) = (millivolt)
      (mA) = (milliamp)
      (mM) = (milli/liter)
}

PARAMETER {
          v            (mV)
          gSK_E2bar = .000001 (mho/cm2)
          zTau = 1              (ms)
          ek           (mV)
          cai          (mM)
          offc = 0.00043 (mM)
	  sloc = 4.8
}

ASSIGNED {
         zInf
         ik            (mA/cm2)
         gSK_E2	       (S/cm2)
}

STATE {
      z   FROM 0 TO 1
}

BREAKPOINT {
           SOLVE states METHOD cnexp
           gSK_E2  = gSK_E2bar * z
           ik   =  gSK_E2 * (v - ek)
}

DERIVATIVE states {
        rates(cai)
        z' = (zInf - z) / zTau
}

PROCEDURE rates(ca(mM)) {
          if(ca < 1e-7){
	              ca = ca + 1e-07
          }
          zInf = 1/(1 + (offc / ca)^sloc)
}

INITIAL {
        rates(cai)
        z = zInf
}