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 util import *

def sort(t, g):
    # sort by gid
    gs = h.Vector()
    gs.copy(g)
    srt = gs.sortindex()
    gs.index(gs, srt)
    
    # sort the times
    ts = h.Vector()
    ts.copy(t)
    ts.index(ts, srt)
    
    # mk header
    header = []
    for i in range(int(gs.size())):
        gid = int(gs.x[i])
        
        if len(header) == 0 or gid != header[-1][0]:
            header.append([ gid, 1 ])
        else:
            header[-1][-1] += 1
            
    return ts, gs, header

def save(prefix, t, g):
    # sort
    ts, gs, header = sort(t, g)
    
    from struct import pack

    # write time
    if rank == 0:
        from neuron import h
        ft = open(prefix + '.time', 'wb')
        ft.write(pack('>f', h.t))
        ft.close()



    ngroup = nhost / max(nhost / 64, 1)
    for rg, rp in group_serialize(ngroup):
        
        fname_data = '%s.sbg.%04d' % (prefix, rg)
        fname_header = '%s.sbgh.%04d' % (prefix, rg)
        fname_dict = '%s.dic.%02d' % (prefix, rg)
        
        if rp:
            fd = open(fname_data, 'ab')
            fh = open(fname_header, 'ab')
            fdic = open(fname_dict, 'ab')
        else:
            fd = open(fname_data, 'wb')
            fh = open(fname_header, 'wb')
            fdic = open(fname_dict, 'wb')

        # output data
        for i in range(int(ts.size())):
            fd.write(pack('>f', ts.x[i]))

        # output header
        for gid, h in header:
            fh.write(pack('>LL', gid, h))

        # output dictionary
        for syn in getmodel().mgrss.values():
            if syn.md:
                fdic.write(pack('>LLHfLf', syn.md_gid, syn.mgid, syn.isec, syn.xm, syn.ggid, syn.xg))
            
        fd.close()
        fh.close()
        fdic.close()
#        binsave_calls += 1
                    
    

    

    

    

    

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