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 *
                            
'''
input: a map with destination ranks as keys, the values are pickleable objects.
output: a map with source ranks as keys, each value is an object.
Transfer the map value (pickleable python object) to the proper destination rank,
A value associated with the destination rank will appear on the
destination rank as a value associated with the source rank.
Alternatively, if the input is a list of nhost objects (some can be None),
then so is the return value.
'''

from common import *
import util

ptime = False

def all2all(data, size=0):
  enter = h.startsw()
  r = _all2all(data, size)
  if ptime and rank == 0: print 'all2all elapsed time = %g'% (h.startsw()-enter)
  return  r

def _all2all(data, size=0):
  if nhost == 1:
    if size == -1:
      return (0, 0)
    return data
  if type(data) is list:
    return pc.py_alltoall(data, size)
  elif type(data) is dict:
    d = []
    for i in range(nhost):
      d.append(None)
    for i in data:
      d[i] = data[i]
    d = pc.py_alltoall(d, size)
    if size == -1:
      return d
    z = {}
    for i,x in enumerate(d):
      if x != None:
        z.update({i : x})
    return z
  raise ValueError

if __name__ == '__main__':
  d = []
  for i in range(nhost):
    d.append(i+10)
  sizes = all2all(d, -1)
  d = all2all(d)
  for r in util.serialize():
    print rank, sizes, d