3D model of the olfactory bulb (Migliore et al. 2014)

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Accession:151681
This entry contains a link to a full HD version of movie 1 and the NEURON code of the paper: "Distributed organization of a brain microcircuit analysed by three-dimensional modeling: the olfactory bulb" by M Migliore, F Cavarretta, ML Hines, and GM Shepherd.
Reference:
1 . Migliore M, Cavarretta F, Hines ML, Shepherd GM (2014) Distributed organization of a brain microcircuit analyzed by three-dimensional modeling: the olfactory bulb. Front Comput Neurosci 8:50 [PubMed]
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Model Information (Click on a link to find other models with that property)
Model Type: Realistic Network; Channel/Receptor; Dendrite;
Brain Region(s)/Organism: Olfactory bulb;
Cell Type(s): Olfactory bulb main mitral cell; Olfactory bulb main interneuron granule MC cell;
Channel(s): I Na,t; I A; I K;
Gap Junctions:
Receptor(s): NMDA; Glutamate; Gaba;
Gene(s):
Transmitter(s):
Simulation Environment: NEURON;
Model Concept(s): Pattern Recognition; Activity Patterns; Bursting; Temporal Pattern Generation; Oscillations; Synchronization; Active Dendrites; Detailed Neuronal Models; Synaptic Plasticity; Action Potentials; Synaptic Integration; Unsupervised Learning; Olfaction;
Implementer(s): Hines, Michael [Michael.Hines at Yale.edu]; Migliore, Michele [Michele.Migliore at Yale.edu]; Cavarretta, Francesco [francescocavarretta at hotmail.it];
Search NeuronDB for information about:  Olfactory bulb main mitral cell; Olfactory bulb main interneuron granule MC cell; NMDA; Glutamate; Gaba; I Na,t; I A; I K;
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bulb3d
readme.html
ampanmda.mod *
distrt.mod *
fi.mod *
kamt.mod *
kdrmt.mod *
naxn.mod *
ThreshDetect.mod *
all2all.py *
balance.py *
bindict.py
BulbSurf.py
colors.py *
common.py
complexity.py *
custom_params.py *
customsim.py
destroy_model.py *
determine_connections.py
distribute.py *
fig7.py
fixnseg.hoc *
getmitral.py
gidfunc.py *
glom.py
granule.hoc *
granules.py
input-odors.txt *
loadbalutil.py *
lpt.py *
mayasyn.py
mgrs.py
misc.py
mitral.hoc *
mitral_dend_density.py
mkmitral.py
modeldata.py *
multisplit_distrib.py *
net_mitral_centric.py
odordisp.py *
odors.py *
odorstim.py
params.py
parrun.py
realgloms.txt *
runsim.py
split.py *
util.py *
weightsave.py *
                            
from neuron import h
h.load_file('mitral.hoc')
import genmitral

def mkmitral(gid):
  nrn = getmitral(gid)
  
  m = h.Mitral()
  m.createsec(len(nrn.dend), len(nrn.tuft))
  m.subsets()
  m.topol(0) # need to connect secondary dendrites explicitly

  for i, d in enumerate(nrn.dend):
    
    # <<< check my changed if
    if(d.parent == nrn.soma): # <<< changed name
      m.secden[i].connect(m.soma(.5))
    else:
      m.secden[i].connect(m.secden[d.parent.index](1)) # <<< changed name
  
  m.geometry()
  m.segments() # depends on geometry
  m.geometry() # again to get the hillock stylized shape

  fillall(nrn, m)
  
  m.segments() # again to get the proper number of segments for tuft and secden
  m.soma.push()
  m.x = h.x3d(0)
  m.y = h.y3d(0)
  m.z = h.z3d(0)
  h.pop_section()
  m.memb()
  return m

def fillall(n, m):
  fillshape(n.soma, m.soma)
  fillshape(n.apic, m.priden)
  for i,s in enumerate(n.dend):
    fillshape(s, m.secden[i])
  for i,s in enumerate(n.tuft):
    fillshape(s, m.tuftden[i])
  
def fillshape(s1, s2):
    s2.push()
    h.pt3dclear()
    for x in s1.points:
      h.pt3dadd(x[0], x[1], x[2], x[3])
    h.pop_section()

if __name__ == "__main__":
  for mgid in range(635):
    print mgid
    mkmitral(mgid)