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3D olfactory bulb: operators (Migliore et al, 2015)

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Accession:168591
"... Using a 3D model of mitral and granule cell interactions supported by experimental findings, combined with a matrix-based representation of glomerular operations, we identify the mechanisms for forming one or more glomerular units in response to a given odor, how and to what extent the glomerular units interfere or interact with each other during learning, their computational role within the olfactory bulb microcircuit, and how their actions can be formalized into a theoretical framework in which the olfactory bulb can be considered to contain "odor operators" unique to each individual. ..."
Reference:
1 . Migliore M, Cavarretta F, Marasco A, Tulumello E, Hines ML, Shepherd GM (2015) Synaptic clusters function as odor operators in the olfactory bulb. Proc Natl Acad Sci U S A 112:8499-504 [PubMed]
Model Information (Click on a link to find other models with that property)
Model Type: Realistic Network;
Brain Region(s)/Organism:
Cell Type(s): Olfactory bulb main mitral GLU cell; Olfactory bulb main interneuron granule MC GABA cell;
Channel(s): I Na,t; I A; I K;
Gap Junctions:
Receptor(s): AMPA; NMDA; Gaba;
Gene(s):
Transmitter(s): Gaba; Glutamate;
Simulation Environment: NEURON; Python;
Model Concept(s): Activity Patterns; Dendritic Action Potentials; Active Dendrites; Synaptic Plasticity; Action Potentials; Synaptic Integration; Unsupervised Learning; Sensory processing; Olfaction;
Implementer(s): Migliore, Michele [Michele.Migliore at Yale.edu]; Cavarretta, Francesco [francescocavarretta at hotmail.it];
Search NeuronDB for information about:  Olfactory bulb main mitral GLU cell; Olfactory bulb main interneuron granule MC GABA cell; AMPA; NMDA; Gaba; I Na,t; I A; I K; Gaba; Glutamate;
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figure1eBulb3D
readme.html
ampanmda.mod *
distrt.mod *
fi.mod *
fi_stdp.mod *
kamt.mod *
kdrmt.mod *
naxn.mod *
ThreshDetect.mod *
.hg_archival.txt
all2all.py *
balance.py *
bindict.py
binsave.py
binspikes.py
BulbSurf.py
catfiles.sh
colors.py *
common.py
complexity.py *
custom_params.py *
customsim.py
destroy_model.py *
determine_connections.py
distribute.py *
falsegloms.txt
fixnseg.hoc *
g37e1i002.py
gidfunc.py *
Glom.py *
granule.hoc *
granules.py
grow.py
input-odors.txt *
loadbalutil.py *
lpt.py *
m2g_connections.py
mayasyn.py
mgrs.py
misc.py
mitral.hoc *
mkdict.py
mkmitral.py
modeldata.py *
multisplit_distrib.py *
net_mitral_centric.py
odordisp.py *
odors.py *
odorstim.py
params.py
parrun.py
realgloms.txt *
realSoma.py *
runsim.py
spike2file.hoc *
split.py *
util.py *
vrecord.py
weightsave.py *
                            
from neuron import h
h.load_file('mitral.hoc')
from grow import *

def mkmitral(gid):
  nrn = genMitral(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__":
  cells = []
  x = h.startsw()
  # note 259 has tertiary branches
  for i in range(10):
    print "mid=",i
    cells.append(mkmitral(i))
  print "wall time ", h.startsw() - x, " seconds"
  h.load_file('select.hoc')
  

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