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]
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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 cell; Olfactory bulb main interneuron granule MC 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 cell; Olfactory bulb main interneuron granule MC 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 *
                            
'''
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