Effect of ionic diffusion on extracellular potentials (Halnes et al 2016)

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"Recorded potentials in the extracellular space (ECS) of the brain is a standard measure of population activity in neural tissue. Computational models that simulate the relationship between the ECS potential and its underlying neurophysiological processes are commonly used in the interpretation of such measurements. Standard methods, such as volume-conductor theory and current-source density theory, assume that diffusion has a negligible effect on the ECS potential, at least in the range of frequencies picked up by most recording systems. This assumption remains to be verified. We here present a hybrid simulation framework that accounts for diffusive effects on the ECS potential. ..."
1 . Halnes G, Mäki-Marttunen T, Keller D, Pettersen KH, Andreassen OA, Einevoll GT (2016) Effect of Ionic Diffusion on Extracellular Potentials in Neural Tissue. PLoS Comput Biol 12:e1005193 [PubMed]
Model Information (Click on a link to find other models with that property)
Model Type: Extracellular; Neuron or other electrically excitable cell;
Brain Region(s)/Organism:
Cell Type(s): Neocortex U1 L6 pyramidal corticalthalamic cell;
Gap Junctions:
Simulation Environment: MATLAB; NEURON;
Model Concept(s): Extracellular Fields;
Implementer(s): Halnes, Geir [geir.halnes at nmbu.no]; Maki-Marttunen, Tuomo [tuomo.maki-marttunen at tut.fi];
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# calcsumcurr_manyareagsynmediumtau_parts_fixeddt.py
# Python script for running a simulation of L5PC under random synaptic inputs. The script saves transmembrane currents that can be later used for calculating
# LFP and diffusion in the extracellular domain.
# Tuomo Maki-Marttunen, 2014-2016
# Usage:
# python calcsumcurr_manyareagsynmediumtau_parts_fixeddt.py 20 0.025 0.000042 10000 10000 2 1 200 #Run a single pyramidal cell simulation with 20 segments/compartment,
#                                                                                                 #0.025ms time step, 0.042 nS synaptic conductance, 10000 ms biological
#                                                                                                 #time, with 10000 synapses of type 2 (scattered on both apical and
#                                                                                                 #basal dendrites), random number seed 1, and performing the simulation
#                                                                                                 #in units of 200ms
# Output:
#   The script saves the amounts of each current type exiting the cell into the 13 extracellular compartments. The bordering extracellular compartments do not contain any
#   membrane segments, and thus 13 compartments are enough. The results are saved into MATLAB files
#   currsums_parts_10000areagsynsmediumtau_fixeddt_type2_amp4.2e-5_tstop10000_nseg20_dt0.025_seed1_sim0x200.mat
#   currsums_parts_10000areagsynsmediumtau_fixeddt_type2_amp4.2e-5_tstop10000_nseg20_dt0.025_seed1_sim1x200.mat
#   ...
#   currsums_parts_10000areagsynsmediumtau_fixeddt_type2_amp4.2e-5_tstop10000_nseg20_dt0.025_seed1_sim49x200.mat

from neuron import h
import numpy
import scipy.io
from pylab import *
import time
import sys
from os.path import exists

morphology_file = "morphologies/cell1.asc"
biophys_file = "models/L5PCbiophys3.hoc"
template_file = "models/L5PCtemplate.hoc"
v0 = -80
ca0 = 0.0001

synlambda = 5.0 # frequency of synaptic inputs (Hz)
syntau = 2.0    # decay time (ms)

proximalpoint = 400
distalpoint = 620
#distalpoint = 960
BACdt = 5.0
part_threshys = [100*x+68 for x in range(-1,11)]+[inf]
if len(sys.argv) > 1:
  nsegs = int(float(sys.argv[1]))
  nsegs = 20
if len(sys.argv) > 2:
  dt = float(sys.argv[2])
  dt = 0.025
if len(sys.argv) > 3:
  syngmax = float(sys.argv[3])
  syngmax = 0.000042
if len(sys.argv) > 4:
  tstop = float(sys.argv[4])
  tstop = 10000
if len(sys.argv) > 5:
  Nsynlocs = int(sys.argv[5])
  Nsynlocs = 10000
if len(sys.argv) > 6:
  synloctype = int(sys.argv[6])
  synloctype = 2 # 1: apical only, 2: apical & basal, 3: basal only
if len(sys.argv) > 7:
  myseed = int(sys.argv[7])
  myseed = 1
if len(sys.argv) > 8:
  singleSimT = float(sys.argv[8])
  singleSimT = 200

Nsims = int(1.0*tstop/singleSimT+0.9999)
dtsave = 0.1

# If the simulation is already done, exit
if exists('currsums_parts_'+str(Nsynlocs)+'areagsynsmediumtau_fixeddt_type'+str(synloctype)+'_amp'+str(syngmax)+'_tstop'+str(tstop)+'_nseg'+str(nsegs)+'_dt'+str(dt)+'_seed'+str(myseed)+'_sim'+str(Nsims-1)+'x'+str(singleSimT)+'.mat'):

# Initialize the model
objref L5PC
L5PC = new L5PCtemplate(\""""+morphology_file+"""\")
dtsave = """+str(dtsave)+"""
objref st1, synlist
st1 = new IClamp(0.5)
st1.del = 700
L5PC.soma st1
synlist = new List()
objref isyn,tvec,sl
isyn = new Vector()
tvec = new Vector()
sl = new List()
double siteVec[2]
sl = L5PC.locateSites("apic","""+str(distalpoint)+""")
maxdiam = 0
  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]
access L5PC.apic[siteVec[0]]
objref vsoma, vdend, recSite, vdend2, isoma, cadend, cadend2, casoma
{vsoma = new Vector()}
{casoma = new Vector()}
{vdend = new Vector()}
{cadend = new Vector()}
{vdend2 = new Vector()}
{cadend2 = new Vector()}
access L5PC.soma
access L5PC.apic[siteVec[0]]
access L5PC.soma
{isoma = new Vector()}
forsec L5PC.all {
  nseg = """+str(nsegs)+"""
dt = """+str(dt)+"""

# Initialize the variables where the transmembrane currents will be saved
objref apicalina["""+str(109*nsegs)+"""], apicalik["""+str(109*nsegs)+"""], apicalica["""+str(109*nsegs)+"""], apicalih["""+str(109*nsegs)+"""], apicalil["""+str(109*nsegs)+"""], apicalv["""+str(109*nsegs)+"""], apicalicap["""+str(109*nsegs)+"""], apicalimemb["""+str(109*nsegs)+"""]
objref basalih["""+str(84*nsegs)+"""], basalil["""+str(84*nsegs)+"""], basalv["""+str(84*nsegs)+"""], basalicap["""+str(84*nsegs)+"""], basalimemb["""+str(84*nsegs)+"""]
objref somaticina["""+str(nsegs)+"""], somaticik["""+str(nsegs)+"""], somaticica["""+str(nsegs)+"""], somaticih["""+str(nsegs)+"""], somaticil["""+str(nsegs)+"""], somaticv["""+str(nsegs)+"""], somaticicap["""+str(nsegs)+"""], somaticimemb["""+str(nsegs)+"""]
objref axonalil["""+str(2*nsegs)+"""], axonalv["""+str(2*nsegs)+"""], axonalicap["""+str(2*nsegs)+"""], axonalimemb["""+str(2*nsegs)+"""]

# Initialize the segment areas
print "objref complete"
A_apical = [0]*109*nsegs
A_basal = [0]*84*nsegs
A_somatic = [0]*nsegs
A_axonal = [0]*2*nsegs
part_apical = [0]*109*nsegs
part_basal = [0]*84*nsegs
part_somatic = [0]*nsegs
part_axonal = [0]*2*nsegs
len_apical = [0]*109*nsegs
len_basal = [0]*84*nsegs
len_somatic = [0]*nsegs
len_axonal = [0]*2*nsegs
maxx = -inf
minx = inf
maxy = -inf
miny = inf
maxz = -inf
minz = inf

# Set the recordings for the compartments in the apical dendrite. Calculate also the membrane areas of each segment
for i in range(0,109):
  h("access L5PC.apic["+str(i)+"]")
  h("tmpvarx = x3d(0)")
  h("tmpvary = y3d(0)")
  h("tmpvarz = z3d(0)")
  h("tmpvarx2 = x3d(n3d()-1)")
  h("tmpvary2 = y3d(n3d()-1)")
  h("tmpvarz2 = z3d(n3d()-1)")
  coord1 = [h.tmpvarx,h.tmpvary,h.tmpvarz]
  coord2 = [h.tmpvarx2,h.tmpvary2,h.tmpvarz2]
  for j in range(0,nsegs):
    thispos = 0.5/nsegs+1.0/nsegs*j
    if nsegs == 1:
      thispos3d = [(x + y)/2 for x,y in zip(coord1,coord2)]
      thispos3d = [x + j*(y-x)/(nsegs-1) for x,y in zip(coord1,coord2)]
    part_apical[i*nsegs+j] = next(i for i,x in enumerate(part_threshys) if thispos3d[1] <= x)
    h("{apicalina["+str(i*nsegs+j)+"] = new Vector()}")
    h("{apicalik["+str(i*nsegs+j)+"] = new Vector()}")
    h("{apicalica["+str(i*nsegs+j)+"] = new Vector()}")
    h("{apicalih["+str(i*nsegs+j)+"] = new Vector()}")
    h("{apicalil["+str(i*nsegs+j)+"] = new Vector()}")
    h("{apicalv["+str(i*nsegs+j)+"] = new Vector()}")
    h("{apicalicap["+str(i*nsegs+j)+"] = new Vector()}")
    h("{apicalimemb["+str(i*nsegs+j)+"] = new Vector()}")
    h("L5PC.apic["+str(i)+"] insert extracellular")
    h("access L5PC.apic["+str(i)+"]")
    h("{apicalina["+str(i*nsegs+j)+"].record(&L5PC.apic["+str(i)+"].ina("+str(thispos)+    "),dtsave)}")
    h("{apicalik["+str(i*nsegs+j)+"].record(&L5PC.apic["+str(i)+"].ik("+str(thispos)+     "),dtsave)}")
    h("{apicalica["+str(i*nsegs+j)+"].record(&L5PC.apic["+str(i)+"].ica("+str(thispos)+    "),dtsave)}")
    h("{apicalil["+str(i*nsegs+j)+"].record(&L5PC.apic["+str(i)+"].i_pas("+str(thispos)+  "),dtsave)}")
    h("{apicalv["+str(i*nsegs+j)+"].record(&L5PC.apic["+str(i)+"].v("+str(thispos)+      "),dtsave)}")
    h("{apicalicap["+str(i*nsegs+j)+"].record(&L5PC.apic["+str(i)+"].i_cap("+str(thispos)+  "),dtsave)}")
    h("{apicalimemb["+str(i*nsegs+j)+"].record(&L5PC.apic["+str(i)+"].i_membrane("+str(thispos)+  "),dtsave)}")
    h("L5PC.apic["+str(i)+"].nseg = " + str(nsegs))
    h("tmpvar = area("+str(thispos)+")")
    A_apical[i*nsegs+j] = h.tmpvar
  h("tmpvar = L")
  len_apical[i] = h.tmpvar
print "apical complete"

# Set the recordings for the compartments in the basal dendrite. Calculate also the membrane areas of each segment
for i in range(0,84):
  h("access L5PC.dend["+str(i)+"]")
  h("tmpvarx = x3d(0)")
  h("tmpvary = y3d(0)")
  h("tmpvarz = z3d(0)")
  h("tmpvarx2 = x3d(n3d()-1)")
  h("tmpvary2 = y3d(n3d()-1)")
  h("tmpvarz2 = z3d(n3d()-1)")
  coord1 = [h.tmpvarx,h.tmpvary,h.tmpvarz]
  coord2 = [h.tmpvarx2,h.tmpvary2,h.tmpvarz2]
  for j in range(0,nsegs):
    thispos = 0.5/nsegs+1.0/nsegs*j
    if nsegs == 1:
      thispos3d = [(x + y)/2 for x,y in zip(coord1,coord2)]
      thispos3d = [x + j*(y-x)/(nsegs-1) for x,y in zip(coord1,coord2)]
    part_basal[i*nsegs+j] = next(i for i,x in enumerate(part_threshys) if thispos3d[1] <= x)
    h("{basalih["+str(i*nsegs+j)+"] = new Vector()}")
    h("{basalil["+str(i*nsegs+j)+"] = new Vector()}")
    h("{basalv["+str(i*nsegs+j)+"] = new Vector()}")
    h("{basalicap["+str(i*nsegs+j)+"] = new Vector()}")
    h("{basalimemb["+str(i*nsegs+j)+"] = new Vector()}")
    h("L5PC.dend["+str(i)+"] insert extracellular")
    h("access L5PC.dend["+str(i)+"]")
    h("{basalil["+str(i*nsegs+j)+"].record(&L5PC.dend["+str(i)+"].i_pas("+str(thispos)+  "),dtsave)}")
    h("{basalv["+str(i*nsegs+j)+"].record(&L5PC.dend["+str(i)+"].v("+str(thispos)+      "),dtsave)}")
    h("{basalicap["+str(i*nsegs+j)+"].record(&L5PC.dend["+str(i)+"].i_cap("+str(thispos)+  "),dtsave)}")
    h("{basalimemb["+str(i*nsegs+j)+"].record(&L5PC.dend["+str(i)+"].i_membrane("+str(thispos)+  "),dtsave)}")
    h("L5PC.dend["+str(i)+"].nseg = " + str(nsegs))
    h("tmpvar = area("+str(thispos)+")")
    A_basal[i*nsegs+j] = h.tmpvar
  h("tmpvar = L")
  len_basal[i] = h.tmpvar
print "basal complete"

# Set the recordings for the compartments in the soma. Calculate also the membrane area
for i in range(0,1):
  h("access L5PC.soma["+str(i)+"]")
  h("tmpvarx = x3d(0)")
  h("tmpvary = y3d(0)")
  h("tmpvarz = z3d(0)")
  h("tmpvarx2 = x3d(n3d()-1)")
  h("tmpvary2 = y3d(n3d()-1)")
  h("tmpvarz2 = z3d(n3d()-1)")
  coord1 = [h.tmpvarx,h.tmpvary,h.tmpvarz]
  coord2 = [h.tmpvarx2,h.tmpvary2,h.tmpvarz2]
  for j in range(0,nsegs):
    thispos = 0.5/nsegs+1.0/nsegs*j
    if nsegs == 1:
      thispos3d = [(x + y)/2 for x,y in zip(coord1,coord2)]
      thispos3d = [x + j*(y-x)/(nsegs-1) for x,y in zip(coord1,coord2)]
    part_somatic[i*nsegs+j] = next(i for i,x in enumerate(part_threshys) if thispos3d[1] <= x)
    h("{somaticina["+str(i*nsegs+j)+"] = new Vector()}")
    h("{somaticik["+str(i*nsegs+j)+"] = new Vector()}")
    h("{somaticica["+str(i*nsegs+j)+"] = new Vector()}")
    h("{somaticih["+str(i*nsegs+j)+"] = new Vector()}")
    h("{somaticil["+str(i*nsegs+j)+"] = new Vector()}")
    h("{somaticv["+str(i*nsegs+j)+"] = new Vector()}")
    h("{somaticicap["+str(i*nsegs+j)+"] = new Vector()}")
    h("{somaticimemb["+str(i*nsegs+j)+"] = new Vector()}")
    h("L5PC.soma["+str(i)+"] insert extracellular")
    h("access L5PC.soma["+str(i)+"]")
    h("{somaticina["+str(i*nsegs+j)+"].record(&L5PC.soma["+str(i)+"].ina("+str(thispos)+    "),dtsave)}")
    h("{somaticik["+str(i*nsegs+j)+"].record(&L5PC.soma["+str(i)+"].ik("+str(thispos)+     "),dtsave)}")
    h("{somaticica["+str(i*nsegs+j)+"].record(&L5PC.soma["+str(i)+"].ica("+str(thispos)+    "),dtsave)}")
    h("{somaticil["+str(i*nsegs+j)+"].record(&L5PC.soma["+str(i)+"].i_pas("+str(thispos)+  "),dtsave)}")
    h("{somaticv["+str(i*nsegs+j)+"].record(&L5PC.soma["+str(i)+"].v("+str(thispos)+      "),dtsave)}")
    h("{somaticicap["+str(i*nsegs+j)+"].record(&L5PC.soma["+str(i)+"].i_cap("+str(thispos)+  "),dtsave)}")
    h("{somaticimemb["+str(i*nsegs+j)+"].record(&L5PC.soma["+str(i)+"].i_membrane("+str(thispos)+  "),dtsave)}")
    h("L5PC.soma["+str(i)+"].nseg = " + str(nsegs))
    h("tmpvar = area("+str(thispos)+")")
    A_somatic[i*nsegs+j] = h.tmpvar
    if j == nsegs/2:
      somapos3d = thispos3d[:]
  h("tmpvar = L")
  len_somatic[i] = h.tmpvar
print "somatic complete"

# Set the recordings for the compartments in the axon initial segment. Calculate also the membrane areas of each segment
for i in range(0,2):
  for j in range(0,nsegs):
    thispos = 0.5/nsegs+1.0/nsegs*j
    part_axonal[i*nsegs+j] = next(i for i,x in enumerate(part_threshys) if somapos3d[1] <= x)
    h("{axonalil["+str(i*nsegs+j)+"] = new Vector()}")
    h("{axonalv["+str(i*nsegs+j)+"] = new Vector()}")
    h("{axonalicap["+str(i*nsegs+j)+"] = new Vector()}")
    h("{axonalimemb["+str(i*nsegs+j)+"] = new Vector()}")
    h("L5PC.axon["+str(i)+"] insert extracellular")
    h("access L5PC.axon["+str(i)+"]")
    h("{axonalv["+str(i*nsegs+j)+"].record(&L5PC.axon["+str(i)+"].v("+str(thispos)+    "),dtsave)}")
    h("{axonalicap["+str(i*nsegs+j)+"].record(&L5PC.axon["+str(i)+"].i_cap("+str(thispos)+    "),dtsave)}")
    h("{axonalimemb["+str(i*nsegs+j)+"].record(&L5PC.axon["+str(i)+"].i_membrane("+str(thispos)+    "),dtsave)}")
    h("L5PC.axon["+str(i)+"].nseg = " + str(nsegs))
    h("tmpvar = area("+str(thispos)+")")
    A_axonal[i*nsegs+j] = h.tmpvar
  h("tmpvar = L")
  len_axonal[i] = h.tmpvar
print "axonal complete"

synbranch = [0]*Nsynlocs
syniseg = [0]*Nsynlocs
synx = [0.0]*Nsynlocs

# Calculate the probability of synapse being found in the basal dendrite.
if synloctype == 1:
  basalprob = 0.0
if synloctype == 2:
  basalprob = sum(A_basal)/(sum(A_basal) + sum(A_apical))
if synloctype == 3:
  basalprob = 1.0
print "Basal area: "+str(sum(A_basal))
print "Apical area: "+str(sum(A_apical))
print "basalprob = "+str(basalprob)

# Calculate the probabilities for the synapse being in each segment
ps_basal = [1.0*x/sum(A_basal) for x in A_basal]
cumps_basal = cumsum(ps_basal)
ps_apical = [1.0*x/sum(A_apical) for x in A_apical]
cumps_apical = cumsum(ps_basal)

# Draw the random numbers, one to decide which branch, one to decide which segment, and one to determine the distance x from 0-end
rs_branch = rand(Nsynlocs)
rs_seg = rand(Nsynlocs)
rs_x = rand(Nsynlocs)

ts_syn = []
seg_syn = [-1]*Nsynlocs
seg_syn_accurate = [-1]*Nsynlocs
part_syn = [-1]*Nsynlocs

# For each synapse, determine to which extracellular compartment it outputs the currents, and randomize the synapse activation times
# To do: Might become faster if the set of AlphaSynapses at a single synaptic location is replaced by a single point process that
# is activated at the time instants drawn here.
for isyn in range(0,Nsynlocs):
  if rs_branch[isyn] <= basalprob:
    synbranch[isyn] = 1
  if synbranch[isyn] == 0:
    seg_syn_accurate[isyn] = next((i for i,x in enumerate(cumps_apical) if x > rs_seg[isyn]))
    seg_syn[isyn] = seg_syn_accurate[isyn]/nsegs
    segnum = seg_syn_accurate[isyn] % nsegs
    h("access L5PC.apic["+str(seg_syn[isyn])+"]")
    mystr = "L5PC.apic["+str(seg_syn[isyn])+"]"
    part_syn[isyn] = part_apical[seg_syn[isyn]*nsegs+segnum]
    seg_syn_accurate[isyn] = next((i for i,x in enumerate(cumps_basal) if x > rs_seg[isyn]))
    seg_syn[isyn] = seg_syn_accurate[isyn]/nsegs
    segnum = seg_syn_accurate[isyn] % nsegs
    h("access L5PC.dend["+str(seg_syn[isyn])+"]")
    mystr = "L5PC.dend["+str(seg_syn[isyn])+"]"
    part_syn[isyn] = part_basal[seg_syn[isyn]*nsegs+segnum]
  ts = []
  t = 0
  secx = 1.0*segnum/nsegs+1.0/nsegs*rs_x[isyn]
  while t < tstop:
    t = t - 1000.0/synlambda*log(1-rand())
    h("{synlist.append(new AlphaSynapse("+str(secx)+"))}")
    h("syni = synlist.count()-1")
    h("synlist.o[syni].tau = "+str(syntau))
    h("synlist.o[syni].gmax = "+str(syngmax))
    h("synlist.o[syni].e = 0")
    h("synlist.o[syni].onset = "+str(t))

Nsyns = h.syni+1

tstop = """+str(tstop)+"""
v_init = """+str(v0)+"""
cai0_ca_ion = """+str(ca0)+"""
st1.amp = 0
st1.dur = 0

print "Initializing..."
print "Init complete"
tfin = 0

# Repeat the simulation of singleSimT milliseconds Nsims times. Save each simulation to a MATLAB file.
for isim in range(0,Nsims):
  for i in range(0,109*nsegs):
  for i in range(0,84*nsegs):
  for i in range(0,nsegs):
  for i in range(0,2*nsegs):
  tfin = tfin+singleSimT
  print "Starting run "+str(isim)+" until "+str(tfin)+" ms"
  print "Run "+str(isim)+" complete, tfin = "+str(tfin)

  times=np.array([tfin-singleSimT+dtsave*i for i in range(0,len(Vsoma))])

  # Here, we calculate the extracellular compartment -wise currents of each species. We will need the previously saved data on the areas of each dendritic compartment and the 
  # extracellular compartment to which each dendritic compartment belongs to (in addition to the transmembrane currents saved during the simulation) in order to do this.
  ina_all = [[]]*len(part_threshys)
  ik_all = [[]]*len(part_threshys)
  ica_all = [[]]*len(part_threshys)
  ih_all = [[]]*len(part_threshys)
  il_all = [[]]*len(part_threshys)
  v_all = [[]]*len(part_threshys)
  icap_all = [[]]*len(part_threshys)
  imemb_all = [[]]*len(part_threshys)
  A_tot_all = [[]]*len(part_threshys)
  for ipart in range(0,len(part_threshys)): # Loop through the extracellular compartments
    ina = [0.0]*len(times)
    ik = [0.0]*len(times)
    ica = [0.0]*len(times)
    ih = [0.0]*len(times)
    il = [0.0]*len(times)
    v = [0.0]*len(times)
    icap = [0.0]*len(times)
    imemb = [0.0]*len(times)
    A_tot = sum([x for x,y in zip(A_apical,part_apical) if y==ipart]) + sum([x for x,y in zip(A_basal,part_basal) if y==ipart]) + sum([x for x,y in zip(A_somatic,part_somatic) if y==ipart]) + sum([x for x,y in zip(A_axonal,part_axonal) if y==ipart])
    for i in range(0,109*nsegs): # Loop through apical dendritic compartments
      if part_apical[i]==ipart:
        ina = [x+y for x,y in zip(ina,[A_apical[i]*x for x in np.array(h.apicalina[i])])]
        ik = [x+y for x,y in zip(ik,[A_apical[i]*x for x in np.array(h.apicalik[i])])]
        ica = [x+y for x,y in zip(ica,[A_apical[i]*x for x in np.array(h.apicalica[i])])]
        ih = [x+y for x,y in zip(ih,[A_apical[i]*x for x in np.array(h.apicalih[i])])]
        il = [x+y for x,y in zip(il,[A_apical[i]*x for x in np.array(h.apicalil[i])])]
        v = [x+y for x,y in zip(v,[A_apical[i]*x for x in np.array(h.apicalv[i])])]
        icap = [x+y for x,y in zip(icap,[A_apical[i]*x for x in np.array(h.apicalicap[i])])]
        imemb = [x+y for x,y in zip(imemb,[A_apical[i]*x for x in np.array(h.apicalimemb[i])])]
    for i in range(0,84*nsegs): # Loop through basal dendritic compartments
      if part_basal[i]==ipart:
        ih = [x+y for x,y in zip(ih,[A_basal[i]*x for x in np.array(h.basalih[i])])]
        il = [x+y for x,y in zip(il,[A_basal[i]*x for x in np.array(h.basalil[i])])]
        v = [x+y for x,y in zip(v,[A_basal[i]*x for x in np.array(h.basalv[i])])]
        icap = [x+y for x,y in zip(icap,[A_basal[i]*x for x in np.array(h.basalicap[i])])]
        imemb = [x+y for x,y in zip(imemb,[A_basal[i]*x for x in np.array(h.basalimemb[i])])]
    for i in range(0,nsegs): # Loop through somatic compartment
      if part_somatic[i]==ipart:
        ina = [x+y for x,y in zip(ina,[A_somatic[i]*x for x in np.array(h.somaticina[i])])]
        ik = [x+y for x,y in zip(ik,[A_somatic[i]*x for x in np.array(h.somaticik[i])])]
        ica = [x+y for x,y in zip(ica,[A_somatic[i]*x for x in np.array(h.somaticica[i])])]
        ih = [x+y for x,y in zip(ih,[A_somatic[i]*x for x in np.array(h.somaticih[i])])]
        il = [x+y for x,y in zip(il,[A_somatic[i]*x for x in np.array(h.somaticil[i])])]
        v = [x+y for x,y in zip(v,[A_somatic[i]*x for x in np.array(h.somaticv[i])])]
        icap = [x+y for x,y in zip(icap,[A_somatic[i]*x for x in np.array(h.somaticicap[i])])]
        imemb = [x+y for x,y in zip(imemb,[A_somatic[i]*x for x in np.array(h.somaticimemb[i])])]
    for i in range(0,2*nsegs): # Loop through axonal compartments
      if part_axonal[i]==ipart:
        il = [x+y for x,y in zip(il,[A_axonal[i]*x for x in np.array(h.axonalil[i])])]
        v = [x+y for x,y in zip(v,[A_axonal[i]*x for x in np.array(h.axonalv[i])])]
        icap = [x+y for x,y in zip(icap,[A_axonal[i]*x for x in np.array(h.axonalicap[i])])]
        imemb = [x+y for x,y in zip(imemb,[A_axonal[i]*x for x in np.array(h.axonalimemb[i])])]
    ina_all[ipart] = [x/100.0 for x in ina[:]]
    ik_all[ipart] = [x/100.0 for x in ik[:]]
    ica_all[ipart] = [x/100.0 for x in ica[:]]
    ih_all[ipart] = [x/100.0 for x in ih[:]]
    il_all[ipart] = [x/100.0 for x in il[:]]
    v_all[ipart] = v[:]
    icap_all[ipart] = [x/100.0 for x in icap[:]]
    imemb_all[ipart] = [x/100.0 for x in imemb[:]]
    A_tot_all[ipart] = A_tot

  dict = {'ina': numpy.array(ina_all), 'ik': numpy.array(ik_all),
          'ica': numpy.array(ica_all), 'ih': numpy.array(ih_all),
          'il': numpy.array(il_all), 'VtimesA': numpy.array(v_all), 'imemb': numpy.array(imemb_all), 'Vsoma': numpy.array(Vsoma),
          'icap': numpy.array(icap_all), 'times': numpy.array(times), 'A': numpy.array(A_tot_all),
          'ts_syn': numpy.array(ts_syn), 'part_syn': numpy.array([1+x for x in part_syn])}
  scipy.io.savemat('currsums_parts_'+str(Nsynlocs)+'areagsynsmediumtau_fixeddt_type'+str(synloctype)+'_amp'+str(syngmax)+'_tstop'+str(tstop)+'_nseg'+str(nsegs)+'_dt'+str(dt)+'_seed'+str(myseed)+'_sim'+str(isim)+'x'+str(singleSimT)+'.mat', dict)

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