189308/mcell.hoc at master · ModelDBRepository/189308 · GitHub

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// HOC file for running the Mauthner cell simulation
// Saves the data to .dat files (tab-delimited ascii),
// and to memory (square pulse responses can be retrieved
// from Python as h.timeList and h.vrecList, and the ramp
// response as h.time and h.vrec)
//
// HH formalism according to Buhry et al. 2013: "Global
// parameter estimation of an Hodgkin-Huxley formalism
// using membrane voltage recordings: Application to
// neuro-mimetic analog integrated circuits", Neurocomputing
// 81 (2012) 75-85
//
// Parameters obtained by hand-fitting and dimension-by-
// dimension local optimization, see an example in runfit.py
//
// Tuomo Maki-Marttunen, 2013-2017 (CC-BY 4.0)
//

load_file("neurmorph.hoc")

gl=0.008700000039526
g1=20.999999990461987
g2=15.289301400499159
el=-83.400007934423883
e1=55
e2=-90
Cm0=2.5
Ra0=120
glA=0.000300000004592
RaA=120
RaAH=120
VoffaNa=-56.700000003615479
VoffaK=-67.499999903453016
VsloaNa=8.100000020602771
VsloaK=9.570002271582146
tauaNa=0.017999999846640
tauaK=1.399997381068154
VoffiNa=-64.000000481147609
VsloiNa=6.060000000757244
tauiNa=0.209992252219524

t_sim = 15
dt = 0.01
axondiam = 54

soma.cm=Cm0
soma.Ra=Ra0
soma.e_pas=el
soma.g_pas=gl
for i=0,29 {
  dend[i].cm=Cm0
  dend[i].Ra=Ra0
  dend[i].e_pas=el
  dend[i].g_pas=gl
}
axonhillock.cm=Cm0
axonhillock.Ra=RaAH
axonhillock.e_pas=el
axonhillock.g_pas=glA
axonhillock.g_I1=g1
axonhillock.g_I2=g2
axonhillock.E_I1=e1
axonhillock.E_I2=e2
axonhillock.Voffa_I1=VoffaNa
axonhillock.Voffa_I2=VoffaK
axonhillock.Vsloa_I1=VsloaNa
axonhillock.Vsloa_I2=VsloaK
axonhillock.taua_I1=tauaNa
axonhillock.taua_I2=tauaK
axonhillock.Voffi_I1=VoffiNa
axonhillock.Vsloi_I1=VsloiNa
axonhillock.taui_I1=tauiNa
for i = 0,2 {
  axon[i].cm=Cm0
  axon[i].Ra=RaA
  axon[i].diam=axondiam
  axon[i].e_pas=el
  axon[i].g_pas=glA
}

forall nseg=20

objref stims[1]
soma stims[0] = new IClamp(0.5)

v_init = el
tstop = t_sim

cvode_active(1)
cvode.atol(0.00005)
objref time, vrec

time = new Vector()
time.record(&t)
vrec = new Vector()
vrec.record(&dend[1].v(0.5))

double stimAmps[17]
stimAmps[0] = 10
stimAmps[1] = 30
stimAmps[2] = 50
stimAmps[3] = 70
stimAmps[4] = 90
stimAmps[5] = 190
stimAmps[6] = 170
stimAmps[7] = 150
stimAmps[8] = 140
stimAmps[9] = 130
stimAmps[10] = 110
stimAmps[11] = 100
stimAmps[12] = 20
stimAmps[13] = -20
stimAmps[14] = -50
stimAmps[15] = -70
stimAmpR = 200


objref myFile
strdef fileName
objref timeList, vrecList
timeList = new List()
vrecList = new List()

for istim=0,15 {
  stims[0].del = 5.0
  stims[0].dur = 5.0
  stims[0].amp = stimAmps[istim]
  init()
  run()

  myFile = new File()
  sprint(fileName,"run%i.dat",istim)
  myFile.wopen(fileName,istim)
  for i=0,time.size()-1 {
    myFile.printf("%g %g\n", time.x(i), vrec.x(i))
  }
  myFile.close()
  timeList.append(new Vector())
  vrecList.append(new Vector())
  for i=0,time.size()-1 {
    timeList.o[timeList.count()-1].append(time.x[i])
    vrecList.o[vrecList.count()-1].append(vrec.x[i])
  }
}

stims[0].amp = 0
objref stimsR[50]
for i = 0,49 {
  soma stimsR[i] = new IClamp(0.5)
}
for i = 0,49 {
  stimsR[i].del = 52 + 20/50.0*i
  stimsR[i].dur = 20/50.0
  stimsR[i].amp = 200/50.0*i
}
t_sim = 72
tstop = t_sim
init()
run()

myFile = new File()
myFile.wopen("runR.dat")
for i=0,time.size()-1 {
  myFile.printf("%g %g\n", time.x(i), vrec.x(i))
}
myFile.close()

timeList.append(time)
vrecList.append(vrec)