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. [...]"
Reference:
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]
Citations  Citation Browser
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 cell; Olfactory bulb main interneuron granule MC cell; Olfactory bulb main interneuron granule TC 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 cell; Olfactory bulb main tufted middle cell; Olfactory bulb main interneuron granule TC 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
                            


# synapses conductances
mc_exc_gmax = 1.25  # nS
mc_inh_gmax = 0.018 # uS

mt_exc_gmax = 1.25  # nS
mt_inh_gmax = 0.018 # uS

mt2bc_exc_gmax=0.25e-4
bc2gc_inh_gmax=0.5

# stream sniff
# you must change this to change the stimulation sequence
# of tuft weights or sniffs activation times
stream_ods_shift = 1

# odorsequence
# for each odor you must add a tuple in this manner
# the possible odors name are
# 'Apple', 'Banana', 'Basil', 'Black_Pepper',
# 'Cheese', 'Chocolate', 'Cinnamon',
# 'Cloves', 'Coffee', 'Garlic', 'Ginger',
# 'Lemongrass', 'Kiwi', 'Mint', 'Onion',
# 'Oregano', 'Pear', 'Pineapple'

# ('Mint', t init, t duration, rel. conc.), (...), (...)
odor_sequence = [ ]

# segments to records
# (cell gid, section index, arc, output filename)
sec2rec = []
#sec2rec += [  (x, None, None) for x in (range(185,190)+range(1005, 1015)) ]
#sec2rec += [  (x, None, None) for x in (range(160,165)+range(955, 965)) ]
#sec2rec += [  (x, None, None) for x in (range(390,395)+range(1415, 1425)) ]

# sim. duration
tstop = 5050

initial_weights = '' 

# dummy syns parameters
dummy_syn_conn = ''#dummysyns.txt'
ndummy_syn = 46

# sniff interval
# None is random
# the number was constant
sniff_invl_min = 350
sniff_invl_max = 350

init_exc_weight = 75/2
init_inh_weight = 50/2

training_exc = False
training_inh = False

glom2blanes = []