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

 Download zip file 
Help downloading and running models
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]
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;
/
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 *
                            
import params
from neuron import h
from neuron import numpy
import mkmitral

pc = h.ParallelContext()
nhost = int(pc.nhost())
rank = int(pc.id())

matrank = (41, 61)
domain = ((-500, 1500, 50), (-500, 2500, 50))

def f(ii):
  i = int(ii)
  density = numpy.zeros(matrank)
  for gid in range(i , params.Nmitral, nhost):
    m = mkmitral.mkmitral(gid)
    for sec in m.secdens:
      accumulate_density(sec, density, domain)
    print gid
  return density

def accumulate_density(sec, density, domain):
  sec.push()
  for i in range(int(h.n3d())):
    x,y = (h.x3d(i), h.y3d(i))
    r = (round(x, domain[0]),round(y, domain[1]))
    if not False in r:
      density[r] += 1
  h.pop_section()

def round(x, d):
  # integer toward 0 min, max, inc where min is 0, if not in interval
  #return False
  if (x < d[0] or x > d[1]):
    return False
  return int((x - d[0])/d[2])


def compute():
  for i in range(nhost):
    pc.submit(f, i)
  den = numpy.zeros(matrank)
  while(pc.working()):
    den += pc.pyret()  
  return den

if __name__ == '__main__':
  pc.runworker()
  density = compute()
  pc.done()
  print "density max = ", density.max()
  density = density * (20/density.max())

  import pickle
  pickle.dump(density, open('density.dat', 'w'))

#following works on linux if using openmpi
from mayavi.mlab import barchart,show
barchart(density)
show()


Loading data, please wait...