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
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;
/
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 matplotlib
matplotlib.use('Agg')
import numpy
from pylab import *
import mytools
import pickle
import time
import sys
import random

random.seed(1)

proximalpoint = 400
distalpoint = 620
BACdt = 5.0
fs = 8
maxLens = [1300,1185]

lenToStart = 0.0
lenToEnd = 200.0
maxSynsPerSeg = 50
Nsyns = 1000

lensToStart = [0.0 + x*200 for x in range(0,6)]
lensToEnd = [200.0 + x*200 for x in range(0,5)]+[1300.]
maxSynsPerSegArray = [36,21,101,200,77,29] #These numbers ensure that the distribution will be more or less uniform (so that if run with different random number seeds, more or less similar result will be obtained)

if len(sys.argv) > 1:
  lenToStart = lensToStart[int(sys.argv[1])]
  lenToEnd = lensToEnd[int(sys.argv[1])]
if len(sys.argv) > 2:
  maxSynsPerSeg = maxSynsPerSegArray[int(sys.argv[2])]
elif len(sys.argv) > 1:
  maxSynsPerSeg = maxSynsPerSegArray[int(sys.argv[1])]
else:
  maxSynsPerSeg = 40

synlocsAll = []

for icell in range(0,1):
  morphology_file = "morphologies/cell"+str(icell+1)+".asc"
  biophys_file = "models/L5PCbiophys3.hoc"
  template_file = "models/L5PCtemplate.hoc"
  synlocs = []

  h("""
load_file("stdlib.hoc")
load_file("stdrun.hoc")
objref cvode
cvode = new CVode()
cvode.active(1)
load_file("import3d.hoc")
objref L5PC
load_file(\""""+biophys_file+"""\")
load_file(\""""+template_file+"""\")
L5PC = new L5PCtemplate(\""""+morphology_file+"""\")
objref st1, st2
st1 = new IClamp(0.5)
st2 = new IClamp(0.5)
L5PC.soma st1
L5PC.soma st2
objref vsoma, vdend, recSite, vdend2, isoma, cadend, casoma
vsoma = new Vector()
casoma = new Vector()
vdend = new Vector()
cadend = new Vector()
vdend2 = new Vector()
objref sl,ns,syn1,con1,isyn, tvec, syns["""+str(Nsyns)+"""]
isyn = new Vector()
tvec = new Vector()
sl = new List()
double siteVec[2]
sl = L5PC.locateSites("apic","""+str(distalpoint)+""")
maxdiam = 0
for(i=0;i<sl.count();i+=1){
  dd1 = sl.o[i].x[1]
  dd = L5PC.apic[sl.o[i].x[0]].diam(dd1)
  if (dd > maxdiam) {
    j = i
    maxdiam = dd
  }
}
siteVec[0] = sl.o[j].x[0]
siteVec[1] = sl.o[j].x[1]
print "distalpoint gCa_HVA: ", L5PC.apic[siteVec[0]].gCa_HVAbar_Ca_HVA
print "distalpoint gCa_LVA: ", L5PC.apic[siteVec[0]].gCa_LVAstbar_Ca_LVAst
access L5PC.apic[siteVec[0]]
cvode.record(&v(siteVec[1]),vdend,tvec)
cvode.record(&cai(siteVec[1]),cadend,tvec)
recSite = new IClamp(siteVec[1])
recSite.amp = 0
L5PC.apic[siteVec[0]] {
        recSite
}
L5PC.apic[siteVec[0]] {
  syn1 = new AlphaSynapse(siteVec[1])
  syn1.e = 0
  syn1.tau = 5
  syn1.onset = 10000 + """+str(BACdt)+""" 
  cvode.record(&syn1.i,isyn,tvec)
}
""")
  synsInSegs = [0]*len(h.L5PC.apic)
  for istim in range(0,Nsyns):
    myiseg = -1
    while myiseg == -1:
      x = lenToStart+(lenToEnd-lenToStart)*random.random()
      h("""sl = L5PC.locateSites("apic","""+str(x)+""")
Nsegs_x = sl.count()
""")
      iseg = random.randint(0,h.Nsegs_x-1)
      h("iseg = sl.o["+str(iseg)+"].x[0]")
      #print "iseg = sl.o["+str(iseg)+"].x[0] = "+str(int(h.iseg))
      if synsInSegs[int(h.iseg)] < maxSynsPerSeg:
        myiseg = int(h.iseg)
        break
      print "istim = "+str(istim)+", x = "+str(x)+", continue searching for iseg..."
    #time.sleep(0.08)
    synsInSegs[myiseg] = synsInSegs[myiseg] + 1
    #print "synsInSegs["+str(myiseg)+"] = "+str(synsInSegs[myiseg])
    h("""
siteVec[0] = sl.o["""+str(iseg)+"""].x[0]
siteVec[1] = sl.o["""+str(iseg)+"""].x[1]
access L5PC.apic[siteVec[0]]
L5PC.apic[siteVec[0]] {
  syns["""+str(istim)+"""] = new AlphaSynapse(siteVec[1])
  syns["""+str(istim)+"""].e = 0
  syns["""+str(istim)+"""].tau = 5
  syns["""+str(istim)+"""].onset = 10000 + """+str(BACdt)+""" 
}
""")
    synlocs.append([h.siteVec[0],h.siteVec[1]])
  
  #time.sleep(5)
  h("""
access L5PC.soma
cvode.record(&v(0.5),vsoma,tvec)
cvode.record(&cai(0.5),casoma,tvec)
sl = new List()
sl = L5PC.locateSites("apic","""+str(proximalpoint)+""")
maxdiam = 0
for(i=0;i<sl.count();i+=1){
  dd1 = sl.o[i].x[1]
  dd = L5PC.apic[sl.o[i].x[0]].diam(dd1)
  if (dd > maxdiam) {
    j = i
    maxdiam = dd
  }
}
siteVec[0] = sl.o[j].x[0]
siteVec[1] = sl.o[j].x[1]
print "proximalpoint gCa_HVA: ", L5PC.apic[siteVec[0]].gCa_HVAbar_Ca_HVA
print "proximalpoint gCa_LVA: ", L5PC.apic[siteVec[0]].gCa_LVAstbar_Ca_LVAst
access L5PC.apic[siteVec[0]]
access L5PC.soma
isoma = new Vector()
cvode.record(&st1.i,isoma,tvec)
""")

  synlocsAll.append(synlocs[:])
picklelist = [Nsyns,maxSynsPerSeg,maxLens,synlocsAll]
file = open('synlocs'+str(lenToStart)+'-'+str(lenToEnd)+'.sav', 'w')
pickle.dump(picklelist,file)
file.close()

Loading data, please wait...