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 *
                            
from common import *
import params
import util
import parrun
import weightsave
import vrecord as vr
import net_mitral_centric as nmc

def build_part_model(gloms, mitrals, dicfile=''):

  model = getmodel()
  model.clear()

  # gids
  gids = set()
  for glomid in gloms:
    gids.update(range(glomid * params.Nmitral_per_glom, (glomid+1) * params.Nmitral_per_glom))
  gids.update(mitrals)
  
  # distribute
  nmc.build_net_round_robin(model, gids, dicfile)

  build_model()
  
def build_complete_model(dicfile=''):
  build_part_model(range(params.Ngloms), [], dicfile)

def build_model():
  import distribute
  import multisplit_distrib
  multisplit_distrib.multisplit_distrib(distribute.getmodel())

  # set initial weights
  if len(params.initial_weights) > 0:
    weightsave.weight_load(params.initial_weights)


  # print sections
  nc = h.List("NetCon")
  nc = int(pc.allreduce(nc.count(),1))
  if rank == 0: print "NetCon count = ", nc
  nseg = 0
  for sec in h.allsec():
    nseg += sec.nseg
  nseg = int(pc.allreduce(nseg, 1))
  if rank == 0: print "Total # compartments = ", nseg

  pc.spike_record(-1, parrun.spikevec, parrun.idvec)
  util.show_progress(200)

  from odorstim import OdorSequence
  odseq = OdorSequence(params.odor_sequence)

  # record
  for rec in params.sec2rec:
    vr.record(*rec)

  if rank == 0: print 'total setup time ', h.startsw()-startsw

h("proc setdt(){}")
h.dt = 1./64. + 1./128.

def run():
  
  parrun.prun(params.tstop)
  weightsave.weight_file(params.filename + '.weight.dat')
  vr.out()
  util.finish()