Goldfish Mauthner cell (Medan et al 2017)

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Accession:189308
" ...In fish, evasion of a diving bird that breaks the water surface depends on integrating visual and auditory stimuli with very different characteristics. How do neurons process such differential sensory inputs at the dendritic level? For that we studied the Mauthner-cells (M-cells) in the goldfish startle circuit, which receive visual and auditory inputs via two separate dendrites, both accessible for in vivo recordings. We asked if electrophysiological membrane properties and dendrite morphology, studied in vivo, play a role in selective sensory processing in the M-cell. Our results show that anatomical and electrophysiological differences between the dendrites combine to produce stronger attenuation of visually evoked post synaptic potentials (PSPs) than to auditory evoked PSPs. Interestingly, our recordings showed also cross-modal dendritic interaction, as auditory evoked PSPs invade the ventral dendrite (VD) as well as the opposite, visual PSPs invade the lateral dendrite (LD). However, these interactions were asymmetrical with auditory PSPs being more prominent in the VD than visual PSPs in the LD. Modelling experiments imply that this asymmetry is caused by active conductances expressed in the proximal segments of the VD. ..."
Reference:
1 . Medan V, Mäki-Marttunen T, Sztarker J, Preuss T (2018) Differential processing in modality-specific Mauthner cell dendrites. J Physiol 596:667-689 [PubMed]
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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: Goldfish;
Cell Type(s): Mauthner cell;
Channel(s): I Sodium; I Potassium;
Gap Junctions:
Receptor(s):
Gene(s):
Transmitter(s):
Simulation Environment: NEURON; Python;
Model Concept(s): Sensory processing;
Implementer(s): Maki-Marttunen, Tuomo [tuomomm at uio.no];
Search NeuronDB for information about:  I Sodium; I Potassium; 
# Python file for running the Mauthner cell simulation
#
# Function run_model_somatic_stims runs by default the same simulation
# as mcell.hoc. Function run_model_dendritic stims runs
# another simulation, where the end of one of the dendrites
# is stimulated, and response is measured along the dendrites.
#
# 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)
#



import numpy as np
from neuron import h

def run_model_somatic_stims(params = [], stims = [[5.0, 10.0, 10.], [5.0, 10.0, 30.], [5.0, 10.0, 50.], [5.0, 10.0, 70.], [5.0, 10.0, 90.], [5.0, 10.0, 190.], [5.0, 10.0, 170.], [5.0, 10.0, 150.], [5.0, 10.0, 140.], [5.0, 10.0, 130.], [5.0, 10.0, 110.], [5.0, 10.0, 100.], [5.0, 10.0, 20.], [5.0, 10.0, -20.], [5.0, 10.0, -50.], [5.0, 10.0, -70.]], stimsR = [52.0, 72.0, 200.], recordDend = False, activesecs = ["dend[20]", "soma", "dend[21]", "dend[25]", "dend[27]", "dend[29]"], activecoeffs = [0.035, 0.035, 0.035, 0.035, 0.035, 0.0]): 

  if len(params)==0:
    params = [0.008699989010953,  4.454066447429189, 10.606380438297881, -83.389488181026280,  0.000955040259787, -52.802714891048296, -58.684517509388293,  8.103232645890719,  8.897735502857380,  0.016351981798972,  1.355604123083193, -63.065631347739128,  5.403637679068333,  0.114211915503005]

  gl=params[0]
  g1=params[1]
  g2=params[2]
  el=params[3]
  e1=55
  e2=-90
  Cm=2.5
  Ra=120
  glA=params[4]
  VoffaNa=params[5]   
  VoffaK=params[6]    
  VsloaNa=params[7]   
  VsloaK=params[8]    
  tauaNa=params[9]    
  tauaK=params[10]   
  VoffiNa=params[11]
  VsloiNa=params[12]
  tauiNa=params[13]

  t_sim = 15

  dt = 0.01

  alllengths = [28.263, 65.172, 137.513, 128.311, 39.547, 48.292, 49.275, 27.859, 36.188, 47.769, 49.665, 60.565, 69.584, 34.191, 125.280, 15.586, 72.951, 51.087, 59.876, 46.099, 32.208, 45.457, 75.944]
  allcumlengths = [25+x for x in [0.0, 28.26286, 93.43471, 230.94766, 230.94766, 270.49515, 270.49515, 93.43471, 28.26286]]+[25+x for x in [0.00000, 47.76941, 97.43476, 157.99953, 227.58324, 261.77471, 261.77471, 277.36042, 277.36042, 157.99953, 97.43476, 143.53365, 143.53365]]+[0.00]

  dendxs = [allcumlengths[i]+alllengths[i]/2 for i in range(len(alllengths))]
  for i in range(0,7):
    dendxs.append(100)
  axondiams = [54,54,54]
  h.load_file("neurmorph_activedend.hoc")
  h("access soma")
  h("soma.cm="+str(Cm))
  h("soma.Ra="+str(Ra))
  h("soma.e_pas="+str(el))
  h("soma.g_pas="+str(gl))
  for i in range(0,30):
    h("dend["+str(i)+"].cm="+str(Cm))
    h("dend["+str(i)+"].Ra="+str(Ra))
    h("dend["+str(i)+"].e_pas="+str(el))
    h("dend["+str(i)+"].g_pas="+str(gl))
  h("axonhillock.cm="+str(Cm))
  h("axonhillock.Ra="+str(Ra))
  h("axonhillock.e_pas="+str(el))
  h("axonhillock.g_pas="+str(glA))
  h("axonhillock.g_I1="+str(g1))
  h("axonhillock.g_I2="+str(g2))
  h("axonhillock.E_I1="+str(e1))
  h("axonhillock.E_I2="+str(e2))
  h("axonhillock.Voffa_I1="+str(VoffaNa))
  h("axonhillock.Voffa_I2="+str(VoffaK))
  h("axonhillock.Vsloa_I1="+str(VsloaNa))
  h("axonhillock.Vsloa_I2="+str(VsloaK))
  h("axonhillock.taua_I1="+str(tauaNa))
  h("axonhillock.taua_I2="+str(tauaK))
  h("axonhillock.Voffi_I1="+str(VoffiNa))
  h("axonhillock.Vsloi_I1="+str(VsloiNa))
  h("axonhillock.taui_I1="+str(tauiNa))
  for i in range(0,3):
    h("axon["+str(i)+"].cm="+str(Cm))
    h("axon["+str(i)+"].Ra="+str(Ra))
    h("axon["+str(i)+"].diam="+str(axondiams[i]))  
    h("axon["+str(i)+"].e_pas="+str(el))
    h("axon["+str(i)+"].g_pas="+str(glA))
  for i in range(0,len(activesecs)):
    h(activesecs[i]+".g_I1="+str(activecoeffs[i]*g1))
    h(activesecs[i]+".g_I2="+str(activecoeffs[i]*g2))
    h(activesecs[i]+".E_I1="+str(e1))
    h(activesecs[i]+".E_I2="+str(e2))
    h(activesecs[i]+".Voffa_I1="+str(VoffaNa))
    h(activesecs[i]+".Voffa_I2="+str(VoffaK))
    h(activesecs[i]+".Vsloa_I1="+str(VsloaNa))
    h(activesecs[i]+".Vsloa_I2="+str(VsloaK))
    h(activesecs[i]+".taua_I1="+str(tauaNa))
    h(activesecs[i]+".taua_I2="+str(tauaK))
    h(activesecs[i]+".Voffi_I1="+str(VoffiNa))
    h(activesecs[i]+".Vsloi_I1="+str(VsloiNa))
    h(activesecs[i]+".taui_I1="+str(tauiNa))
  h("forall nseg=20")

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

  h("""
	v_init = """ + str(el) + """
	tstop = """ + str(t_sim) + """
        dt = """ + str(dt) + """

        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))
  """)
  dists_rec = []
  reclocs_branch = []
  if recordDend:
    h("distance()")
    reclocs_seg =    [-1, 17, 0,  4  ,10, 16,   16,  16,   16, 16,   16,  16,   16,21,  21, 21,  21,25,  25, 25, 25, 25, 27,  27, 27,  27,29,  29, 29,  29, 0]
    reclocs_x =      [0.5,0.5,0.5,0.5,0.5,0.125,0.25,0.375,0.5,0.625,0.75,0.875,1, 0.25,0.5,0.75,1, 0.25,0.5,0.5,0.75,1, 0.25,0.5,0.75,1, 0.25,0.5,0.75,1, 0.5]
    reclocs_branch = [-1, 0,  0,  0,  0,  0,    0,   0,    0,  0,    0,   0,    0, 1,   1,  1,   1, 1,   1,  1,  1,  1,  1,   1,  1,   1, 1,   1,  1,   1, -2]
    h("""
objref recs
recs = new List()
""")
    for irec in range(0,len(reclocs_seg)):
      h("{recs.append(new Vector())}")
      if reclocs_seg[irec]==-1:
        h("{recs.o["+str(irec)+"].record(&soma.v("+str(reclocs_x[irec])+"))}")
        dists_rec.append(h.distance(reclocs_x[irec],sec=h.soma))
      elif reclocs_seg[irec]==-2:
        h("{recs.o["+str(irec)+"].record(&axonhillock.v("+str(reclocs_x[irec])+"))}")
        dists_rec.append(h.distance(reclocs_x[irec],sec=h.axonhillock))
      else:
        h("{recs.o["+str(irec)+"].record(&dend["+str(reclocs_seg[irec])+"].v("+str(reclocs_x[irec])+"))}")
        dists_rec.append(h.distance(reclocs_x[irec],sec=h.dend[reclocs_seg[irec]]))


  Vrecs = np.empty((np.shape(stims)[0]+1,), dtype=np.object)
  times = np.empty((np.shape(stims)[0]+1,), dtype=np.object)
  VrecsDend = []
  for istim in range(0,np.shape(stims)[0]):
    h("stims[0].del = "+str(stims[istim][0]))
    h("stims[0].dur = "+str(stims[istim][1]-stims[istim][0]))
    h("stims[0].amp = "+str(stims[istim][2]))
    h.init()
    h.run()

    Vrecs[istim] = np.array(h.vrec)
    times[istim] = np.array(h.time)
    if recordDend:
      VrecsDend.append(np.array(h.recs))

  h("stims[0].amp = 0")
  h("objref stimsR[50]")
  for i in range(0,50):
    h("soma stimsR["+str(i)+"] = new IClamp(0.5)")
  for i in range(0,50):
    h("stimsR["+str(i)+"].del = "+str(stimsR[0] + (stimsR[1]-stimsR[0])/50.0*i))
    h("stimsR["+str(i)+"].dur = "+str((stimsR[1]-stimsR[0])/50.0))
    h("stimsR["+str(i)+"].amp = "+str(stimsR[2]/50.0*i))
  t_sim = 72
  h("tstop = " + str(t_sim))
  h.init()
  h.run()

  Vrecs[np.shape(stims)[0]] = np.array(h.vrec)
  times[np.shape(stims)[0]] = np.array(h.time)

  return [times, Vrecs, VrecsDend, dists_rec, reclocs_branch]



def run_model_dendritic_stims(params = [], stimloc = 'lateral', stim_onset = 5, stim_dur=0.1, stim_amps = [10,20,40,60,80,100,120,150,200], idendstims = [16, 29], xdendstims = [1.0, 1.0], t_sim=15, activesecs = ["dend[20]", "soma", "dend[21]", "dend[25]", "dend[27]", "dend[29]"], activecoeffs = [0.035, 0.035, 0.035, 0.035, 0.035, 0.0]): 

  if len(params)==0:
    params = [0.008700000039526154, 20.999999990461987, 15.28930140049916, -83.40000793442388, 0.0003000000045918104, -56.70000000361548, -67.49999990345302, 8.10000002060277, 9.570002271582146,
              0.017999999846640063, 1.399997381068154, -64.00000048114761, 6.060000000757244, 0.20999225221952442]

  gl=params[0]
  g1=params[1]
  g2=params[2]
  el=params[3]
  e1=55
  e2=-90
  Cm=2.5
  Ra=120
  glA=params[4]
  VoffaNa=params[5]   
  VoffaK=params[6]    
  VsloaNa=params[7]   
  VsloaK=params[8]    
  tauaNa=params[9]    
  tauaK=params[10]   
  VoffiNa=params[11]
  VsloiNa=params[12]
  tauiNa=params[13]

  axondiams = [54,54,54]

  dt = 0.01

  h.load_file("neurmorph_activedend.hoc")
  h("access soma")
  h("distance()")
  h("soma.cm="+str(Cm))
  h("soma.Ra="+str(Ra))
  h("soma.e_pas="+str(el))
  h("soma.g_pas="+str(gl))
  for i in range(0,30):
    h("dend["+str(i)+"].cm="+str(Cm))
    h("dend["+str(i)+"].Ra="+str(Ra))
    h("dend["+str(i)+"].e_pas="+str(el))
    h("dend["+str(i)+"].g_pas="+str(gl))
  h("axonhillock.cm="+str(Cm))
  h("axonhillock.Ra="+str(Ra))
  h("axonhillock.e_pas="+str(el))
  h("axonhillock.g_pas="+str(glA))
  h("axonhillock.g_I1="+str(g1))
  h("axonhillock.g_I2="+str(g2))
  h("axonhillock.E_I1="+str(e1))
  h("axonhillock.E_I2="+str(e2))
  h("axonhillock.Voffa_I1="+str(VoffaNa))
  h("axonhillock.Voffa_I2="+str(VoffaK))
  h("axonhillock.Vsloa_I1="+str(VsloaNa))
  h("axonhillock.Vsloa_I2="+str(VsloaK))
  h("axonhillock.taua_I1="+str(tauaNa))
  h("axonhillock.taua_I2="+str(tauaK))
  h("axonhillock.Voffi_I1="+str(VoffiNa))
  h("axonhillock.Vsloi_I1="+str(VsloiNa))
  h("axonhillock.taui_I1="+str(tauiNa))
  for i in range(0,3):
    h("axon["+str(i)+"].cm="+str(Cm))
    h("axon["+str(i)+"].Ra="+str(Ra))
    h("axon["+str(i)+"].diam="+str(axondiams[i]))  
    h("axon["+str(i)+"].e_pas="+str(el))
    h("axon["+str(i)+"].g_pas="+str(glA))
  for i in range(0,len(activesecs)):
    h(activesecs[i]+".g_I1="+str(activecoeffs[i]*g1))
    h(activesecs[i]+".g_I2="+str(activecoeffs[i]*g2))
    h(activesecs[i]+".E_I1="+str(e1))
    h(activesecs[i]+".E_I2="+str(e2))
    h(activesecs[i]+".Voffa_I1="+str(VoffaNa))
    h(activesecs[i]+".Voffa_I2="+str(VoffaK))
    h(activesecs[i]+".Vsloa_I1="+str(VsloaNa))
    h(activesecs[i]+".Vsloa_I2="+str(VsloaK))
    h(activesecs[i]+".taua_I1="+str(tauaNa))
    h(activesecs[i]+".taua_I2="+str(tauaK))
    h(activesecs[i]+".Voffi_I1="+str(VoffiNa))
    h(activesecs[i]+".Vsloi_I1="+str(VsloiNa))
    h(activesecs[i]+".taui_I1="+str(tauiNa))

  h("forall nseg=20")

  h("objref stims[2]")
  h("dend["+str(idendstims[0])+"] stims[0] = new IClamp("+str(xdendstims[0])+")")
  h("dend["+str(idendstims[1])+"] stims[1] = new IClamp("+str(xdendstims[1])+")")

  reclocs_seg =    [-1, 17, 0,  4  ,10, 16,   16,  16,   16, 16,   16,  16,   16,21,  21, 21,  21,25,  25, 25, 25, 25, 27,  27, 27,  27,29,  29, 29,  29, 0]
  reclocs_x =      [0.5,0.5,0.5,0.5,0.5,0.125,0.25,0.375,0.5,0.625,0.75,0.875,1, 0.25,0.5,0.75,1, 0.25,0.5,0.5,0.75,1, 0.25,0.5,0.75,1, 0.25,0.5,0.75,1, 0.5]
  reclocs_branch = [-1, 0,  0,  0,  0,  0,    0,   0,    0,  0,    0,   0,    0, 1,   1,  1,   1, 1,   1,  1,  1,  1,  1,   1,  1,   1, 1,   1,  1,   1, -2]

  h("""
	v_init = """ + str(el) + """
	tstop = """ + str(t_sim) + """
        dt = """ + str(dt) + """

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

	time = new Vector()
        time.record(&t)
        recs = new List()
""")

  dists_rec = []
  for irec in range(0,len(reclocs_seg)):
    h("{recs.append(new Vector())}")
    if reclocs_seg[irec]==-1:
      h("{recs.o["+str(irec)+"].record(&soma.v("+str(reclocs_x[irec])+"))}")
      dists_rec.append(h.distance(reclocs_x[irec],sec=h.soma))
    elif reclocs_seg[irec]==-2:
      h("{recs.o["+str(irec)+"].record(&axonhillock.v("+str(reclocs_x[irec])+"))}")
      dists_rec.append(h.distance(reclocs_x[irec],sec=h.axonhillock))
    else:
      h("{recs.o["+str(irec)+"].record(&dend["+str(reclocs_seg[irec])+"].v("+str(reclocs_x[irec])+"))}")
      dists_rec.append(h.distance(reclocs_x[irec],sec=h.dend[reclocs_seg[irec]]))
  dists_stim = []
  for istim in range(0,2):
    dists_stim.append(h.distance(xdendstims[istim],sec=h.dend[idendstims[istim]]))

  Vrecs = np.empty((len(stim_amps),), dtype=np.object)
  times = np.empty((len(stim_amps),), dtype=np.object)
  if stimloc=="lateral":
    istimloc = 0
  elif stimloc=="ventral":
    istimloc = 1
  else:
    print "Unknown stimulus location!"

  for istim in range(0,len(stim_amps)):
    h("stims["+str(istimloc)+"].del = "+str(stim_onset))
    h("stims["+str(istimloc)+"].dur = "+str(stim_dur))
    h("stims["+str(istimloc)+"].amp = "+str(stim_amps[istim]))
    h.init()
    h.run()

    Vrecs[istim] = np.array(h.recs)
    times[istim] = np.array(h.time)

  return [times, Vrecs, dists_rec, reclocs_branch, dists_stim]