Parallel odor processing by mitral and middle tufted cells in the OB (Cavarretta et al 2016, 2018)

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Accession:240116
"[...] experimental findings suggest that MC and mTC may encode parallel and complementary odor representations. We have analyzed the functional roles of these pathways by using a morphologically and physiologically realistic three-dimensional model to explore the MC and mTC microcircuits in the glomerular layer and deeper plexiform layers. [...]"
References:
1 . Cavarretta F, Burton SD, Igarashi KM, Shepherd GM, Hines ML, Migliore M (2018) Parallel odor processing by mitral and middle tufted cells in the olfactory bulb. Sci Rep 8:7625 [PubMed]
2 . Cavarretta F, Marasco A, Hines ML, Shepherd GM, Migliore M (2016) Glomerular and Mitral-Granule Cell Microcircuits Coordinate Temporal and Spatial Information Processing in the Olfactory Bulb. Front Comput Neurosci 10:67 [PubMed]
Model Information (Click on a link to find other models with that property)
Model Type: Realistic Network;
Brain Region(s)/Organism: Olfactory bulb;
Cell Type(s): Olfactory bulb main tufted middle GLU cell; Olfactory bulb main interneuron granule MC GABA cell; Olfactory bulb main interneuron granule TC GABA cell; Olfactory bulb (accessory) mitral cell; Olfactory bulb main tufted cell external; Olfactory bulb short axon cell;
Channel(s): I A; I Na,t; I_Ks; I K;
Gap Junctions: Gap junctions;
Receptor(s): AMPA; GabaA; NMDA;
Gene(s):
Transmitter(s): Glutamate; Gaba;
Simulation Environment: NEURON;
Model Concept(s): Action Potentials; Action Potential Initiation; Active Dendrites; Long-term Synaptic Plasticity; Synaptic Integration; Synchronization; Pattern Recognition; Spatio-temporal Activity Patterns; Temporal Pattern Generation; Sensory coding; Sensory processing; Olfaction;
Implementer(s): Cavarretta, Francesco [francescocavarretta at hotmail.it]; Hines, Michael [Michael.Hines at Yale.edu];
Search NeuronDB for information about:  Olfactory bulb main interneuron granule MC GABA cell; Olfactory bulb main tufted middle GLU cell; Olfactory bulb main interneuron granule TC GABA cell; GabaA; AMPA; NMDA; I Na,t; I A; I K; I_Ks; Gaba; Glutamate;
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modeldb-bulb3d
sim
ampanmda.mod
distrt.mod *
fi.mod
fi_stdp.mod *
gap.mod
Gfluct.mod
kamt.mod
kdrmt.mod
ks.mod
naxn.mod
orn.mod
ThreshDetect.mod *
all.py
all2all.py *
assembly.py
balance.py *
bindict.py
binsave.py
binspikes.py
blanes.hoc
blanes.py
blanes_exc_conn.txt
blanes6.dic
bulb3dtest.py
cancel.py
catfiles.sh
cellreader.py
cellwriter.py
cfg27.py
common.py
complexity.py *
convertdic.py
destroy_model.py
determine_connections.py
distribute.py *
dsac.py
Eta.txt *
fillgloms.py
fixnseg.hoc *
g_conn_stats.py
gapjunc.py
gen_weights.py
geodist.py
geodist.txt
getmitral.py
gidfunc.py
GJ.py
gj_nrn.hoc
Glom.py *
granule.hoc
granules.py
graphmeat.py
grow.py
growdef.py *
growout.py
job
Kod.txt *
lateral_connections.py
loadbalutil.py *
lpt.py *
mcgrow.py
MCrealSoma.py *
mgrs.py
misc.py
mitral.hoc
mkassembly.py
mkmitral.py
modeldata.py
mtgrow.py
MTrealSoma.py
MTrealSoma2.py
mtufted.hoc
multisplit_distrib.py
net_mitral_centric.py
Nod.txt *
odors.py
odorstim.py
odstim2.txt *
pad.txt *
params.py
parrun.py
pathdist.py
realgloms.txt *
runsim.py
spike2file.hoc *
spk2weight.py
split.py
subsetsim.py
test_complexity.py
txt2bin.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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