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
                            
import pathdist
import bindict
import gidfunc
import params
import fileinput
wexc_mc = []
wexc_mt = []
winh_mc = []
winh_mt = []
for l in fileinput.input('../weightse1.25i0.4i0.4kdrmt0.3ks0.005.txt'):
  tk = l.split()
  wexc_mc += [float(tk[1])]
  winh_mc += [float(tk[2])]
  wexc_mt += [float(tk[3])]
  winh_mt += [float(tk[4])]



for perc in [0,50,100]:


  bindict.load('fullbulb%d-v4.dic'%perc)


  for w_base in [0]:

    for glomid in [2,10,17,33,126,99,113,5,77,105,47,98,16,8,27]:
      
      filename = 'w%dcontrol%dp%de1.25i0.04i0.04' % (w_base, glomid, perc)

      gloms = set([ 37, glomid ])

      def issynapse(gid): return not (gidfunc.ismitral(gid) or gidfunc.ismtufted(gid) or gidfunc.isgranule(gid))

      def glom2mgid(glomid):
        for i in range(glomid * params.Nmitral_per_glom, (glomid + 1) * params.Nmitral_per_glom):
          yield i + params.gid_mitral_begin
        for i in range(glomid * params.Nmtufted_per_glom, (glomid + 1) * params.Nmtufted_per_glom):
          yield i + params.gid_mtufted_begin


      with open(filename+'.weight.dat', 'w') as fo:
        for _glomid in gloms:
          for mgid in glom2mgid(_glomid):
            for gid in bindict.mgid_dict[mgid]:
              if issynapse(gid):
                ii = int(pathdist.pd(gid)/20)
                if gidfunc.ismitral(mgid):
                  wexc = wexc_mc[ii]
                  winh = winh_mc[ii]
                else:
                  wexc = wexc_mt[ii]
                  winh = winh_mt[ii]


                
                fo.write('%d %d 0\n' % (gid, wexc))
                fo.write('%d %d 0\n' % (gid-1, winh))
      print w_base, glomid, perc

    
    

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