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 *
h.load_file("spike2file.hoc")


idvec = h.Vector()
idvec.buffer_size(5000000)
spikevec = h.Vector()
spikevec.buffer_size(5000000)

n_spkout_files = max(nhost/64, 1) # each file contains spikes from 64 ranks
n_spkout_sort = min(n_spkout_files*8, nhost) #each file serializes from 8 ranks
# so each sorting rank gathers spikes from nhost/n_spkout_sort ranks
checkpoint_interval = 100000
clean_weights_active = False
clean_weights_interval = 10500.0

from weightsave import weight_reset as clean_weights, weight_file

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

  inittime = h.startsw()
  cvode.active(0)
#  if rank == 0: print 'cvode active=', cvode.active()
  h.stdinit()
  inittime = h.startsw() - inittime
  if rank == 0:
    if clean_weights_active:
      print 'weights reset active at %g ms' % clean_weights_interval
    else:
      print 'weights reset not active'
    print 'init time = %g'%inittime

  tnext_clean = clean_weights_interval
  while h.t < tstop:
    told = h.t
    tnext = h.t + checkpoint_interval

    if tnext > tstop:
      tnext = tstop

    #if clean_weights_active:
      #while tnext_clean < tnext:
        #pc.psolve(tnext_clean)
        #clean_weights()
        #tnext_clean += clean_weights_interval
   
    pc.psolve(tnext)
    
    

#    if rank == 0:
#      print 'sim. checkpoint at %g' % h.t
    
    if h.t == told:
      if rank == 0:
        print "psolve did not advance time from t=%.20g to tnext=%.20g\n"%(h.t, tnext)
      break
#    weight_file(params.filename+('.%d'%isaved))   
    # save spikes and dictionary in a binary format to
    # make them more comprimibles
    import binsave
    binsave.save(params.filename, spikevec, idvec)
    
#    h.spike2file(params.filename, spikevec, idvec, n_spkout_sort, n_spkout_files)
  
  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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