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 common import *
import sys

def serialize():
  ''' Execute body of for loop for each rank in order. Hopefully
      printing will also be segregated and in order
  '''
  for r in range(nhost):
    pc.barrier()
    if r == rank:
      yield r
      sys.stdout.flush()
  pc.barrier()

def group_serialize(ngroup=nhost):
  ''' Execute body of for loop for ngroups of contiguous ranks. 
      The ranks in each group is sequentially executed. Within each barrier,
      one rank in every group is executed in parallel.
  '''
  for r in range(ngroup):
    pc.barrier()
    if r == rank%ngroup:
      yield (rank/ngroup, r)
      sys.stdout.flush()
  pc.barrier()

def finish():
  ''' proper way to quit '''
  if nhost > 0:
    pc.runworker()
    pc.done()
    print 'total elapsed time ', h.startsw()-startsw
    h.quit()
    
elapsedtime=h.startsw()
def elapsed(message):
  ''' Rank 0 prints message and walltime elapsed since
      previous call to this function.
  '''
  global elapsedtime
  if rank == 0:
    print "%s elapsedtime %g"% (message, h.startsw() - elapsedtime)
  elapsedtime = h.startsw()


def progress(pinvl, swlast):
  sw = h.startsw()
  print "t=%g wall interval %g"% (h.t, sw-swlast)
  h.cvode.event(h.t+pinvl, (progress, (pinvl , sw)))

def show_progress(invl):
  global fih
  if rank == 0:
    fih = h.FInitializeHandler(2, (progress, (invl, h.startsw())))

if __name__ == '__main__':
  h.tstop = 1000
  show_progress(200)
  h.run()

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