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 params
import MCrealSoma as realSoma

somaAxisUp = [ params.bulbAxis[i]-600 for i in range(3) ]
somaAxisDw = [ params.bulbAxis[i]-700 for i in range(3) ]
            

from math import pi, exp
# specific grow parameters

GROW_MAX_ITERATIONS = 2000
GROW_MAX_ATTEMPTS = 100

##### ATTENTION: parameters may change in the future

## apical params
APIC_DIAM = 4 # modified from Francesco's value
APIC_LEN_MAX = 550.-150

## tuft params
N_MIN_TUFT = 5
N_MAX_TUFT = 5
TUFT_DIAM = 0.8
TUFT_MIN_LEN = 40
TUFT_MAX_LEN = 80

# dendrites paramaters
#N_MIN_DEND = 4
#N_MAX_DEND = 6
#DEND_DIAM = 2

# dendrites definition
N_MIN_DEND = 4
N_MAX_DEND = 9
N_MEAN_DEND = 5

# dendrites max length, normal distribution
DEND_LEN_MU = 837.97
DEND_LEN_VAR = 283.04 ** 2
DEND_LEN_MIN = 50.
DEND_LEN_MAX = 1800.

# dendrites bifurcation parameters, exponential distribution
branch_len_mean = [ 85.32, 226.61, 226.61, 226.61, 226.61 ]
branch_len_min = [ 2.95, 0.5, 0.5, 0.5, 0.5 ]
branch_len_max = [ 325.0, 825.0,  825.0,  825.0,  825.0 ]
branch_prob = [ 0.75, 0.63, 0.42, 0.28, 0.06 ]


def gen_dend_diam(dist): return 0.9 + 2.6 * exp(-0.0013 * dist) #value's estimated with a fitting

## random walk, noise
GRW_WALK_LEN = 20.
BIFURC_PHI_MU = 0.5
BIFURC_PHI_MIN = pi / 24.
BIFURC_PHI_MAX = pi / 5.
NS_PHI_B = 0.16
NS_PHI_MIN = -6.26
NS_PHI_MAX = 6.26
NS_THETA_B = 0.1407
NS_THETA_MIN = -1.56
NS_THETA_MAX = 1.18

GROW_RESISTANCE = 1.5

# glomerulus radius

GLOM_DIST = 10. #50.

init_theta = pi / 3

diam_min_dend = 2.25
diam_max_dend = 2.75
diam_tape_dend = 0.0013

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