Parallel odor processing by mitral and middle tufted cells in the OB (Cavarretta et al 2016, 2018)

 Download zip file   Auto-launch 
Help downloading and running models
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;
/
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 split
from neuron import h
import params; nmxg = params.Nmitral_per_glom; nmt = params.Nmtufted_per_glom; nmi = params.gid_mtufted_begin
from common import pc, getmodel, nhost
import util
import all2all as a2a
h.load_file('gj_nrn.hoc')
from gidfunc import ismitral, ismtufted, mgid2glom

gj_min_g1=0
gj_max_g1=2.5
gj_min_g3=0
gj_max_g3=2.5
gj_min_g2=0
gj_max_g2=0.001

def init():
    data = {}
    for uid in range(nhost):
      data.update({ uid:(getmodel().mitrals.keys()) })
    data = a2a.all2all(data)
    mgids = []
    for _mgids in data.values(): mgids += _mgids
    mgids = set(mgids)

    # initialize source
    
    for mgid in getmodel().mitrals.keys():
        mpriden = split.mpriden(mgid)
        if not mpriden:
          continue
       
        rgj = params.ranstream(mgid, params.stream_gap_junction)

        
        mpriden.push()
        secref = h.SectionRef()
        h.pop_section()
        
        h.mk_gj_src(pc, mgid, secref)

        glomid = mgid2glom(mgid)
        
        sistergids = []

        # no longer all to all, only a chain
        if not (ismtufted(mgid) and (mgid - nmi) % nmt == (nmt - 1)):
            if ismitral(mgid) and mgid % nmxg == (nmxg - 1):
                sistergids += [glomid * nmt + nmi]
            else:
                sistergids += [mgid + 1]
            
        if not (ismitral(mgid) and mgid % nmxg == 0):
            if ismtufted(mgid) and (mgid - nmi) % nmt == 0:
                sistergids += [(glomid + 1) * nmxg - 1]
            else:
                sistergids += [mgid - 1]
            
        sistergids = mgids.intersection(range(glomid * nmxg, glomid * nmxg + nmxg) + range(glomid * nmt + nmi, glomid * nmt + nmt + nmi)).difference([ mgid ])  

        for sistermgid in sistergids:
            gap = h.Gap(mpriden(0.99))

            if ismitral(mgid) and ismitral(sistermgid):
                gap.g = rgj.uniform(gj_min_g1, gj_max_g1)
            elif  ismtufted(mgid) and ismtufted(sistermgid):
                gap.g = rgj.uniform(gj_min_g3, gj_max_g3)
            else:
                gap.g = rgj.uniform(gj_min_g2, gj_max_g2)
                

            getmodel().gj[(mgid, sistermgid)] = gap
            

    pc.barrier()

    # initialize targets
    for key, gap in getmodel().gj.items():
        mgid, sistermgid = key
        pc.target_var(gap, gap._ref_vgap, sistermgid)

    util.elapsed('Gap junctions built')



Loading data, please wait...