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
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
                            
from neuron import h
import mutation_stuff
import protocol

defValsMut = mutation_stuff.getdefvals()
defVals = protocol.get_defvals()

def setparams(params):
  params_copy = params.copy()
  keys = params.keys()
  for ikey in range(0,len(keys)):
    params_copy[keys[ikey]] = params[keys[ikey]]*defVals[keys[ikey]]

  #Apply the new parameter values:                                                                                                                                                                        
  IhChanged = False
  CaHVAChanged = False
  CaLVAChanged = False
  aIh = defVals['D_aIh']
  bIh = defVals['D_bIh']
  print "setparams: params_copy = "+str(params_copy)
  for ikey in range(0,len(keys)):
    key = keys[ikey]
    section = []
    if key[0:2] == "A_" or key[0:2] == "B_" or key[0:2] == "S_" or key[0:2] == "*_":
      if key[0:2] == "A_":
        section = "apical"
      if key[0:2] == "B_":
        section = "basal"
      if key[0:2] == "S_":
        section = "somatic"
      if key[0:2] == "*_":
        section = "all"
      if len(section) > 0:
        print("forsec L5PC."+section+" "+key[2:]+" = "+str(params_copy[key]))
        h("forsec L5PC."+section+" "+key[2:]+" = "+str(params_copy[key]))
    elif key[0:2] == "D_":
      if key[2:] == "aIh":
        IhChanged = True
        aIh = params_copy[key]
      if key[2:] == "bIh":
        IhChanged = True
        bIh = params_copy[key]
      if key == "aCa_HVA":
        CaHVAChanged = True
      if key[2:] == "aCa_LVAst":
        CaLVAChanged = True
  if IhChanged:
    print("""L5PC.distribute_channels(\"apic\",\"gIhbar_Ih\",2,"""+str(aIh)+""",3.6161,0.0,"""+str(bIh)+""",0.00020000000)""")
    h("""L5PC.distribute_channels(\"apic\",\"gIhbar_Ih\",2,"""+str(aIh)+""",3.6161,0.0,"""+str(bIh)+""",0.00020000000)""")
  if CaLVAChanged:
    print("""L5PC.distribute_channels(\"apic\",\"gCa_LVAstbar_Ca_LVAst\",3,1.000000,0.01,685.000000,885.000000,"""+str(params_copy['D_aCa_LVAst'])+""")""")
    h("""L5PC.distribute_channels(\"apic\",\"gCa_LVAstbar_Ca_LVAst\",3,1.000000,0.01,685.000000,885.000000,"""+str(params_copy['D_aCa_LVAst'])+""")""")
  if CaHVAChanged:
    print("""L5PC.distribute_channels(\"apic\",\"gCa_HVAbar_Ca_HVA\",3,1.000000,0.100000,685.000000,885.000000,"""+str(params_copy['D_aCa_HVA'])+""")""")
    h("""L5PC.distribute_channels(\"apic\",\"gCa_HVAbar_Ca_HVA\",3,1.000000,0.100000,685.000000,885.000000,"""+str(params_copy['D_aCa_HVA'])+""")""")




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