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 column_eval import column_eval, preprocess, axial_score, radial_score
from column_affecting_score import affecting_score as cas
import column_affecting_score as casm
casm.cas = {}
fi=open('imp.txt','r')
imp={}
line=fi.readline()
while line:
  tk=line.split()
  imp[int(tk[0])]=float(tk[1])
  line=fi.readline()
fi.close()

gli = [ (74, 100), (84, 52), (21, 102), (33, 124), \
        (106, 59), (89, 111), (113, 78), \
        (108, 87), (105, 18), (112, 7), (25, 29), \
        (94, 22), (86, 117), (49, 116), (107, 83) ]


#data = preprocess('g37e1i002step3.weight.dat')
#print radial_score(data, 37), axial_score(data, 37)

data = preprocess('g37cc030s2.weight.dat')
ce1 = column_eval(data, 37)[0][1]

#print radial_score(data, 37), axial_score(data, 37)
  
from math import log
index = 0
fo = open('output1.txt', 'w')
for i in range(1, 5):
  jmax=4
  if i==2:
    jmax -= 1
  
  for j in range(0, jmax):

    gl1, gl2 = gli[index]
    data = preprocess('odpart%d_%d.weight.dat' % (i, j))

    #print radial_score(data, 37), axial_score(data, 37)
    
    ce = column_eval(data, 37)[0][1]
    ri = (imp[gl1] + imp[gl2])/2 #)-imp[37])/imp[37]
    x = cas(37,gl1,gl2) #log(cas(37,gl1,gl2)/((cas(gl1,37,gl2)+cas(gl2,37,gl1))/2.0))
    print i, j, gl1, gl2, x, ri, ce
    fo.write('%d %d %g %g %g\n'%(gl1, gl2, x, ri, ce[0]))
    index += 1
fo.close()

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