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]
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 GLU cell; Olfactory bulb main interneuron granule MC GABA 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 GLU cell; Olfactory bulb main interneuron granule MC GABA 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 *
                            
from common import *

# so each sorting rank gathers spikes from nhost/n_spkout_sort ranks
checkpoint_interval = 50000.

def prun(tstop):
  cvode = h.CVode()
  cvode.cache_efficient(1)
  #pc.spike_compress(0,0,1)
  pc.setup_transfer()
  mindelay = pc.set_maxstep(10)
  if rank == 0: print 'mindelay = %g'%mindelay
  runtime = h.startsw()
  exchtime = pc.wait_time()

  inittime = h.startsw()
  h.stdinit()
  inittime = h.startsw() - inittime
  if rank == 0: print 'init time = %g'%inittime
  
  while h.t < tstop:
    told = h.t
    tnext = h.t + checkpoint_interval
    if tnext > tstop:
      tnext = tstop
    pc.psolve(tnext)
    if h.t == told:
      if rank == 0:
        print "psolve did not advance time from t=%.20g to tnext=%.20g\n"%(h.t, tnext)
      break

  runtime = h.startsw() - runtime
  comptime = pc.step_time()
  splittime = pc.vtransfer_time(1)
  gaptime = pc.vtransfer_time()
  exchtime = pc.wait_time() - exchtime
  if rank == 0: print 'runtime = %g'% runtime
  printperf([comptime, exchtime, splittime, gaptime])

def printperf(p):
  avgp = []
  maxp = []
  header = ['comp','spk','split','gap']
  for i in p:
    avgp.append(pc.allreduce(i, 1)/nhost)
    maxp.append(pc.allreduce(i, 2))
  if rank > 0:
    return
  b = avgp[0]/maxp[0]
  print 'Load Balance = %g'% b
  print '\n     ',
  for i in header: print '%12s'%i,
  print '\n avg ',
  for i in avgp: print '%12.2f'%i,
  print '\n max ',
  for i in maxp: print '%12.2f'%i,
  print ''
 
if __name__ == '__main__':
  import common
  import util
  common.nmitral = 1
  common.ncell = 2
  import net_mitral_centric as nmc
  nmc.build_net_roundrobin(getmodel())
  pc.spike_record(-1, spikevec, idvec)
  from odorstim import OdorStim
  from odors import odors
  ods = OdorStim(odors['Apple'])
  ods.setup(nmc.mitrals, 10., 20., 100.)
  prun(200.)
  util.finish()

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