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
                            
#Copied from calcupdownresponses_noisy.py
from neuron import h
import matplotlib
matplotlib.use('Agg')
import numpy
from pylab import *
import mytools
import pickle
import time
import sys
import random
from setparams import *

rdSeed = 1
ITER = 0


v0 = -80
ca0 = 0.0001
#proximalpoints = [100,100,100,200,200,200,300,300,300,400,400,400]
#distalpoints =   [600,750,900,600,750,900,600,750,900,600,750,900]
proximalpoints = [200,200]
distalpoints =   [600,900]
BACdt = 5.0
fs = 8
tstop = 3350.0
#epspdts = [0.25*x for x in range(-80,81)]
#epspdts = [0.5*x for x in range(-40,41)]
#epspdts = [4.0*x for x in range(-40,-20)]+[2.0*x for x in range(-40,-20)]+[1.0*x for x in range(-40,-20)]+[0.5*x for x in range(-40,41)]+[1.0*x for x in range(21,41)]+[2.0*x for x in range(21,41)]+[4.0*x for x in range(21,41)]
#epspdts = [2.0*x for x in range(-10,-5)]+range(-10,11)+[2.0*x for x in range(6,11)]
epspdts = [10.0*x for x in range(-10,-8)]+[4.0*x for x in range(-20,-10)]+[4.0*x for x in range(-10,-5)]+[2.0*x for x in range(-10,11)]+[4.0*x for x in range(6,11)]+[4.0*x for x in range(11,21)]+[10.0*x for x in range(9,11)]
epspdts_savetimecourses = [-40,-20,-10,-4,0,4,10,20,40]

Is_st2 = 1.32
st2coeff = 0.40      #Somatic 5ms pulse
st2coeff_down = 1.35 #Somatic 5ms pulse
st1coeff = 0.9      #Proximal apical 200ms pulse
syn1coeff = 0.5     #Synaptic epsp-like input

unpicklefile = open('apicalthresholds.sav', 'r')
unpickledlist = pickle.load(unpicklefile)
unpicklefile.close()
IsAllAll_st1 = unpickledlist[0]
dists = unpickledlist[1]

unpicklefile = open('apicalthresholds_epsp.sav', 'r')
unpickledlist = pickle.load(unpicklefile)
unpicklefile.close()
IsAllAll_syn1 = unpickledlist[0]

import mutation_stuff
MT = mutation_stuff.getMT()
defVals = mutation_stuff.getdefvals()
keyList = defVals.keys()
for idefval in range(0,len(keyList)):
  if type(defVals[keyList[idefval]]) is not list:
    defVals[keyList[idefval]] = [defVals[keyList[idefval]], defVals[keyList[idefval]]] #make the dictionary values [somatic, apical]
updatedVars = ['somatic','apical','basal'] # the possible classes of segments that defVals may apply to
whichDefVal = [0,1,0]                      # use the defVal[0] for somatic and basal segments and defVal[1] for apical segments
unpicklefile = open('scalings_cs.sav', 'r')
unpickledlist = pickle.load(unpicklefile)
unpicklefile.close()
theseCoeffsAllAll = unpickledlist[0]
theseMutValsAllAll = unpickledlist[2]

paramdicts = []
paramdicts.append({'A_gNaTa_tbar_NaTa_t': 1.0, 'S_gCa_HVAbar_Ca_HVA': 1.0})   # 1 spike per burst, control
paramdicts.append({'A_gNaTa_tbar_NaTa_t': 1.6})                               # 1-2 spikes per burst
paramdicts.append({'A_gNaTa_tbar_NaTa_t': 2.2})                               # 2-3 spikes per burst
paramdicts.append({'A_gNaTa_tbar_NaTa_t': 2.2, 'S_gCa_HVAbar_Ca_HVA': 0.9})   # 3-4 spikes per burst
paramdicts.append({'A_gNaTa_tbar_NaTa_t': 2.2, 'S_gCa_HVAbar_Ca_HVA': 0.625}) # 3-5 spikes per burst
paramdicts.append({'A_gNaTa_tbar_NaTa_t': 2.2, 'S_gCa_HVAbar_Ca_HVA': 0.5})   # 4-6 spikes per burst
paramdicts.append({'A_gNaTa_tbar_NaTa_t': 2.2, 'S_gCa_HVAbar_Ca_HVA': 0.3})   # 5-9 spikes per burst

if len(sys.argv) > 1:
  rdSeed = int(float(sys.argv[1]))

icell = 0
idist = 0

if len(sys.argv) > 2:
  idist = int(float(sys.argv[2]))

morphology_file = "morphologies/cell1.asc"
biophys_file = "models/L5PCbiophys3.hoc"
template_file = "models/L5PCtemplate_withsyns.hoc"

theseCoeffsAll = theseCoeffsAllAll[icell]

if True:
    proximalpoint = proximalpoints[idist]
    distalpoint = distalpoints[idist]
    fixedpoint = 700
    idist_proximal = dists.index(proximalpoint)
    idist_distal = dists.index(distalpoint)
    Is_syn1 = IsAllAll_syn1[icell][idist_distal]
    if type(Is_syn1) is list:
      Is_syn1 = Is_syn1[-1]
    Is_st1 = IsAllAll_st1[icell][idist_proximal]
    if type(Is_st1) is list:
      Is_st1 = Is_st1[-1]
    h("""
load_file("stdlib.hoc")
load_file("stdrun.hoc")
objref cvode
cvode = new CVode()
cvode.active(0)
dt = 0.025
//cvode.atol(0.0002)
load_file("import3d.hoc")
objref L5PC
load_file(\""""+biophys_file+"""\")
load_file(\""""+template_file+"""\")
L5PC = new L5PCtemplate(\""""+morphology_file+"""\")
access L5PC.soma
objref st1, st2
st2 = new IClamp(0.5)
L5PC.soma st2
st2.amp = """+str(st2coeff*Is_st2)+"""
st2.del = 3000
st2.dur = 5
objref vsoma, casoma, sksoma, vdend, vdend2, vdend3, cadend3, skdend3, tvec, isyn
vsoma = new Vector()
casoma = new Vector()
sksoma = new Vector()
vdend = new Vector()
vdend2 = new Vector()
vdend3 = new Vector()
cadend3 = new Vector()
skdend3 = new Vector()
objref sl,ns,syn1,con1
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
L5PC.apic[siteVec[0]] cvode.record(&v(siteVec[1]),vdend,tvec)
L5PC.apic[siteVec[0]] {
  syn1 = new epsp(siteVec[1])
  syn1.tau0 = 0.5
  syn1.tau1 = 5
  syn1.onset = 3000 + """+str(BACdt)+"""
  syn1.imax = """+str(syn1coeff*Is_syn1)+"""
  cvode.record(&syn1.i,isyn,tvec)
}
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
L5PC.apic[siteVec[0]] cvode.record(&v(siteVec[1]),vdend2,tvec)
L5PC.apic[siteVec[0]] st1 = new IClamp(siteVec[1])
st1.amp = 0
st1.del = 700
st1.dur = 2600

sl = L5PC.locateSites("apic","""+str(fixedpoint)+""")
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]
L5PC.apic[siteVec[0]] cvode.record(&v(siteVec[1]),vdend3,tvec)
L5PC.apic[siteVec[0]] cvode.record(&cai(siteVec[1]),cadend3,tvec)
L5PC.apic[siteVec[0]] cvode.record(&ik_SK_E2(siteVec[1]),skdend3,tvec)

L5PC.soma cvode.record(&v(0.5),vsoma,tvec)
L5PC.soma cvode.record(&cai(0.5),casoma,tvec)
L5PC.soma cvode.record(&ik_SK_E2(0.5),sksoma,tvec)
tstop = """+str(tstop)+"""
""")

    rateCoeff = 0.7
    #Add noisy inputs:
    h("""
NsynE = 10000 // number of excitatory synapses
NsynI = 2500 // number of inhibitory synapses
rateE = 0.72*"""+str(rateCoeff)+""" // average rate of presynaptic excitatory cells
rateI = 7 // average rate of presynaptic inhibitory cells
Econ = 0.0004 //excitatory synaptic conductance
Icon = 0.001 //inhibitory synaptic conductance
rdSeed = """+str(rdSeed)+"""
objref preTrainList
objref rds1,rds2
preTrainList = new List()
{rds1 = new Random(1000*rdSeed)}//random for presynaptic trains
{rds2 = new Random(1000*rdSeed)}//random for presynaptic trains
{rds1.negexp(1/rateE)}
{rds2.negexp(1/rateI)}
L5PC.initRand(rdSeed)
L5PC.setparameters(Econ,Icon,NsynE,NsynI)
L5PC.distributeSyn()
for(i2=0;i2<(NsynE+NsynI);i2+=1){
  {preTrainList.append(new Vector())}
  pst=0 //presynaptic spike time
  while(pst < tstop){
    if (i2<NsynE) {
      pst+= 1000*rds1.repick()
    } else {
      pst+= 1000*rds2.repick()
    }
    {preTrainList.o[preTrainList.count()-1].append(pst)}
  }
}
L5PC.setpretrains(preTrainList)
L5PC.queuePreTrains()
""")
    paramdict = paramdicts[icell]
    setparams(paramdict)

    styles = ['g-','g-','g-','g-','g-','g-','g-','g-','g-']
    #cols = ['#00aaaa','#11cc44','#55ee00','#bbaa00','#ee6600','#ff0000', '#aa00aa','#772277','#333333']
    cols = ['#666666','#012345','#aa00aa','#bbaa00','#ee6600','#ff0000', '#00aaaa','#772277','#00cc00']
    coeffCoeffs = [[0.25,0],[0.125,0],[0.5,0],[0.5,1.0/3],[0.5,2.0/3],[0.5,1.0],[-0.25,0],[-0.125,0],[-0.5,0]]
    lw = 0.5

    mutcounter = -1

    for igene in range(0,len(MT)):

     for imut in range(0,len(MT[igene])): 

      nVals = len(MT[igene][imut])*[0]
      thesemutvars = []
      theseCoeffs = theseCoeffsAll[igene][imut]
      for imutvar in range(0,len(MT[igene][imut])):
        thesemutvars.append(MT[igene][imut][imutvar][0])
        if type(MT[igene][imut][imutvar][1]) is int or type(MT[igene][imut][imutvar][1]) is float:
          MT[igene][imut][imutvar][1] = [MT[igene][imut][imutvar][1]]
        nVals[imutvar] = len(MT[igene][imut][imutvar][1])
      cumprodnVals = cumprod(nVals)
      allmutvars = cumprodnVals[len(MT[igene][imut])-1]*[thesemutvars]
      allmutvals = []
      for iallmutval in range(0,cumprodnVals[len(MT[igene][imut])-1]):
        allmutvals.append([0]*len(thesemutvars))
      for iallmutval in range(0,cumprodnVals[len(MT[igene][imut])-1]):
        for imutvar in range(0,len(MT[igene][imut])):
          if imutvar==0:
            allmutvals[iallmutval][imutvar] = MT[igene][imut][imutvar][1][iallmutval%nVals[imutvar]]
          else:
            allmutvals[iallmutval][imutvar] = MT[igene][imut][imutvar][1][(iallmutval/cumprodnVals[imutvar-1])%nVals[imutvar]]
    
      for iallmutval in range(0,cumprodnVals[len(MT[igene][imut])-1]):
        iters = [0,2,5,6,8,-1]
        #iters = [-1]
        for iiter in range(0,len(iters)):
          iter = iters[iiter]
          if iter==5:
            continue
          if iter == -1 and (igene > 0 or imut > 0 or iallmutval > 0):
            continue # do the control only once!
          mutcounter = mutcounter + 1                                                                                                                                                               
          if len(sys.argv) > 3 and mutcounter != int(sys.argv[3]):
            continue
          CasomaupThisIter = []
          SKsomaupThisIter = []
          VdendupThisIter = []
          Vdend2upThisIter = []
          Vdend3upThisIter = []
          Cadend3upThisIter = []
          SKdend3upThisIter = []
          spikesupThisIter = []
          Vdend3tcupThisIter = []
          Cadend3tcupThisIter = []
          SKdend3tcupThisIter = []
          timecoursedataThisIter = []

          if iter >= 0:
            thisCoeff = coeffCoeffs[iter][0]*theseCoeffs[iallmutval] + coeffCoeffs[iter][1]*(1.0 - 0.5*theseCoeffs[iallmutval])
          else:
            thisCoeff = 0
          print "iter="+str(iter)+", thisCoeff="+str(thisCoeff)
        
          mutText = ""
          for imutvar in range(0,len(MT[igene][imut])):
            if imutvar > 0 and imutvar%2==0:
              mutText = mutText+"\n"
            mutvars = allmutvars[iallmutval][imutvar]
            mutvals = allmutvals[iallmutval][imutvar]
            if type(mutvars) is str:
              mutvars = [mutvars]
            mutText = mutText + str(mutvars) + ": "
            for kmutvar in range(0,len(mutvars)):
              mutvar = mutvars[kmutvar]
              if mutvar.find('offm') > -1 or mutvar.find('offh') > -1 or mutvar.find('ehcn') > -1:
                newVal =  [x+mutvals*thisCoeff for x in defVals[mutvar]]
                if mutvals >= 0 and kmutvar==0:
                  mutText = mutText + "+" + str(mutvals) +" mV"
                elif kmutvar==0:
                  mutText = mutText  + str(mutvals) +" mV"
              else:
                newVal = [x*(mutvals**thisCoeff) for x in defVals[mutvar]]
                if kmutvar==0:
                  mutText = mutText + "*" + str(mutvals)
              if kmutvar < len(mutvars)-1:
                mutText = mutText + ", "
              if mutvar.find('_Ih') > -1:
                updateThese = [1,1,1]
              elif mutvar.find('_Ca_HVA') > -1 or mutvar.find('_Ca_LVAst') > -1 or mutvar.find('_SKv3.1') > -1 or mutvar.find('_Ca_HVA') > -1 or mutvar.find('_SK_E2') > -1 or mutvar.find('_NaTa_t') > -1 or mutvar.find('_CaDynamics_E2') > -1:
                updateThese = [1,1,0]
              elif mutvar.find('_K_Pst') > -1 or mutvar.find('_K_Tst') > -1 or mutvar.find('_Nap_Et2') > -1:
                updateThese = [1,0,0]
              elif mutvar.find('_Im') > -1:
                updateThese = [0,1,0]
              else:
                print "Error: str=" + str(mutvar)
                updatedThese = [0,0,0]
              for iupdated in range(0,3):
                if updateThese[iupdated]:
                  print """forsec L5PC."""+str(updatedVars[iupdated])+""" {
"""+mutvar+""" = """+str(newVal[whichDefVal[iupdated]])+"""
}"""
                  h("""forsec L5PC."""+str(updatedVars[iupdated])+""" {
"""+mutvar+""" = """+str(newVal[whichDefVal[iupdated]])+"""
}""")
          print mutText
          thisCa = h.L5PC.soma[0].minCai_CaDynamics_E2

          timeStart = time.time()
          for idt in range(0,len(epspdts)):
              iup = 1
              print "st1.amp = 0"
              print "st2.amp = "+str((st2coeff*(1-iup) + st2coeff_down*iup)*Is_st2)
              print "syn1.imax = "+str(syn1coeff*Is_syn1)
              h("st1.amp = 0")
              h("st2.amp = "+str((st2coeff*(1-iup) + st2coeff_down*iup)*Is_st2))
              h("syn1.imax = "+str(syn1coeff*Is_syn1))
              h("""
cai0_ca_ion = """+str(thisCa)+"""
v_init = """+str(v0)+"""
syn1.onset = """+str(3000+epspdts[idt])+"""
""")
              timeStart_dt = time.time()
              h.init()
              h.run()
              print "Simulation done in "+str(time.time()-timeStart_dt)+" seconds"
              times=np.array(h.tvec)
              Vsoma=np.array(h.vsoma)
              Casoma=np.array(h.casoma)
              SKsoma=np.array(h.sksoma)
              Vdend=np.array(h.vdend)
              Vdend2=np.array(h.vdend2)
              Vdend3=np.array(h.vdend3)
              Cadend3=np.array(h.cadend3)
              SKdend3=np.array(h.skdend3)
              spikes = mytools.spike_times(times,Vsoma,-35,-45)

              CasomaupThisIter.append(max(Casoma))
              SKsomaupThisIter.append(max(SKsoma))
              VdendupThisIter.append(max(Vdend))
              Vdend2upThisIter.append(max(Vdend2))
              Vdend3upThisIter.append(max(Vdend3))
              Cadend3upThisIter.append(max(Cadend3))
              SKdend3upThisIter.append(max(SKdend3))
              spikesupThisIter.append(spikes[:])

              times_tcshort = [2900+x for x in range(0,301)]
              Vdend3tcupThisIter.append(mytools.interpolate(times,Vdend3,times_tcshort))
              Cadend3tcupThisIter.append(mytools.interpolate(times,Cadend3,times_tcshort))
              SKdend3tcupThisIter.append(mytools.interpolate(times,SKdend3,times_tcshort))

              if epspdts[idt] in epspdts_savetimecourses:
                times_tc = [2680+x for x in range(0,641)]
                Vsoma_tc = mytools.interpolate(times,Vsoma,times_tc)
                Casoma_tc = mytools.interpolate(times,Casoma,times_tc)
                SKsoma_tc = mytools.interpolate(times,SKsoma,times_tc)
                Vdend_tc = mytools.interpolate(times,Vdend,times_tc)
                Vdend2_tc = mytools.interpolate(times,Vdend2,times_tc)
                Vdend3_tc = mytools.interpolate(times,Vdend3,times_tc)
                Cadend3_tc = mytools.interpolate(times,Cadend3,times_tc)
                SKdend3_tc = mytools.interpolate(times,SKdend3,times_tc)
                picklelist = [times_tc[:],Vsoma_tc[:],Casoma_tc[:],SKsoma_tc[:],Vdend_tc[:],Vdend2_tc[:],Vdend3_tc[:],Cadend3_tc[:],SKdend3_tc[:],epspdts_savetimecourses]
                timecoursedataThisIter.append(picklelist[:])
                file = open('updownresponsetimecourse_noisydown_cs'+str(icell)+'_dist'+str(proximalpoints[idist])+'_'+str(distalpoints[idist])+'_syn1coeff'+str(syn1coeff)+'_'+str(igene)+'_'+str(imut)+'_'+str(iallmutval)+'_'+str(iter)+'_'+str(rateCoeff)+'_seed'+str(rdSeed)+'.sav', 'w')
                pickle.dump(picklelist,file)
                file.close()
          print "All dts done in "+str((time.time()-timeStart_dt)/3600)+" hours"

          #Print the parameters and their default values:
          for idefval in range(0,len(defVals.keys())):
            thisdefval = defVals.keys()[idefval]
            if thisdefval.find('_Im') > -1:
              h('print "L5PC.apic[0].'+thisdefval+' = ", L5PC.apic[0].'+thisdefval+', "Default = ", '+str(defVals[thisdefval][1]))
              #) #+" (def="+str(defVals[thisdefval])+")"
            else:
              h('print "L5PC.soma[0].'+thisdefval+' = ", L5PC.soma[0].'+thisdefval+', "Default = ", '+str(defVals[thisdefval][0]))
              #h('print L5PC.soma[0]."+thisdefval) #+" (def="+str(defVals[thisdefval])+")"                       

          #Restore default values:
          for imutvar in range(0,len(MT[igene][imut])):
            mutvars = allmutvars[iallmutval][imutvar]
            mutvals = allmutvals[iallmutval][imutvar]
            if type(mutvars) is str:
              mutvars = [mutvars]
            for kmutvar in range(0,len(mutvars)):
              mutvar = mutvars[kmutvar]
              newVal = defVals[mutvar]
              if mutvar.find('_Ih') > -1:
                updateThese = [1,1,1]
              elif mutvar.find('_Ca_HVA') > -1 or mutvar.find('_Ca_LVAst') > -1 or mutvar.find('_SKv3.1') > -1 or mutvar.find('_Ca_HVA') > -1 or mutvar.find('_SK_E2') > -1 or mutvar.find('_NaTa_t') > -1 or mutvar.find('_CaDynamics_E2') > -1:
                updateThese = [1,1,0]
              elif mutvar.find('_K_Pst') > -1 or mutvar.find('_K_Tst') > -1 or mutvar.find('_Nap_Et2') > -1:
                updateThese = [1,0,0]
              elif mutvar.find('_Im') > -1:
                updateThese = [0,1,0]
              else:
                print "Error: str=" + str(mutvar)
                updatedThese = [0,0,0]
              for iupdated in range(0,3):
                if updateThese[iupdated]:
                  print """forsec L5PC."""+str(updatedVars[iupdated])+""" {
"""+mutvar+""" = """+str(newVal[whichDefVal[iupdated]])+"""
}"""
                  h("""forsec L5PC."""+str(updatedVars[iupdated])+""" {
"""+mutvar+""" = """+str(newVal[whichDefVal[iupdated]])+"""
}""")

          picklelist = [theseCoeffsAllAll,timecoursedataThisIter,epspdts_savetimecourses,MT]
          file = open('updownresponsetimecourse_noisydown_cs'+str(icell)+'_dist'+str(proximalpoints[idist])+'_'+str(distalpoints[idist])+'_syn1coeff'+str(syn1coeff)+'_'+str(igene)+'_'+str(imut)+'_'+str(iallmutval)+'_'+str(iter)+'_'+str(rateCoeff)+'_seed'+str(rdSeed)+'.sav', 'w')
          pickle.dump(picklelist,file)
          file.close()
          picklelist = [theseCoeffsAllAll,CasomaupThisIter,SKsomaupThisIter,VdendupThisIter,Vdend2upThisIter,Vdend3upThisIter,Cadend3upThisIter,SKdend3upThisIter,
                        spikesupThisIter,Vdend3tcupThisIter,Cadend3tcupThisIter,SKdend3tcupThisIter,epspdts,MT]
          file = open('updownresponse_noisydown_cs'+str(icell)+'_dist'+str(proximalpoints[idist])+'_'+str(distalpoints[idist])+'_syn1coeff'+str(syn1coeff)+'_'+str(igene)+'_'+str(imut)+'_'+str(iallmutval)+'_'+str(iter)+'_'+str(rateCoeff)+'_seed'+str(rdSeed)+'_tmp.sav', 'w')
          pickle.dump(picklelist,file)
          file.close()


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