3D model of the olfactory bulb (Migliore et al. 2014)

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Accession:151681
This entry contains a link to a full HD version of movie 1 and the NEURON code of the paper: "Distributed organization of a brain microcircuit analysed by three-dimensional modeling: the olfactory bulb" by M Migliore, F Cavarretta, ML Hines, and GM Shepherd.
Reference:
1 . Migliore M, Cavarretta F, Hines ML, Shepherd GM (2014) Distributed organization of a brain microcircuit analyzed by three-dimensional modeling: the olfactory bulb. Front Comput Neurosci 8:50 [PubMed]
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Model Information (Click on a link to find other models with that property)
Model Type: Realistic Network; Channel/Receptor; Dendrite;
Brain Region(s)/Organism: Olfactory bulb;
Cell Type(s): Olfactory bulb main mitral cell; Olfactory bulb main interneuron granule MC cell;
Channel(s): I Na,t; I A; I K;
Gap Junctions:
Receptor(s): NMDA; Glutamate; Gaba;
Gene(s):
Transmitter(s):
Simulation Environment: NEURON;
Model Concept(s): Pattern Recognition; Activity Patterns; Bursting; Temporal Pattern Generation; Oscillations; Synchronization; Active Dendrites; Detailed Neuronal Models; Synaptic Plasticity; Action Potentials; Synaptic Integration; Unsupervised Learning; Olfaction;
Implementer(s): Hines, Michael [Michael.Hines at Yale.edu]; Migliore, Michele [Michele.Migliore at Yale.edu]; Cavarretta, Francesco [francescocavarretta at hotmail.it];
Search NeuronDB for information about:  Olfactory bulb main mitral cell; Olfactory bulb main interneuron granule MC cell; NMDA; Glutamate; Gaba; I Na,t; I A; I K;
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bulb3d
readme.html
ampanmda.mod *
distrt.mod *
fi.mod *
kamt.mod *
kdrmt.mod *
naxn.mod *
ThreshDetect.mod *
all2all.py *
balance.py *
bindict.py
BulbSurf.py
colors.py *
common.py
complexity.py *
custom_params.py *
customsim.py
destroy_model.py *
determine_connections.py
distribute.py *
fig7.py
fixnseg.hoc *
getmitral.py
gidfunc.py *
glom.py
granule.hoc *
granules.py
input-odors.txt *
loadbalutil.py *
lpt.py *
mayasyn.py
mgrs.py
misc.py
mitral.hoc *
mitral_dend_density.py
mkmitral.py
modeldata.py *
multisplit_distrib.py *
net_mitral_centric.py
odordisp.py *
odors.py *
odorstim.py
params.py
parrun.py
realgloms.txt *
runsim.py
split.py *
util.py *
weightsave.py *
                            
# granules parameters
# This value changes the number of granules that will be generated
# the GCL volume is approximately 1910088333.383 (um^3)
# so the number of granules filling the GCL is volume/(grid_dim^3)

grid_dim = 25 # 122166 granules
# grid_dim = 49 # 16013 granules


# synapses interval on the mitral's dendrites
mean_synapse_interval = 10.

# synapses conductances
exc_gmax = 0.1 # nS
inh_gmax = 0.005 # uS

# sniffs parameters
# random ranges for syn weights
ods_wl = .7
ods_wh = 1.3

# random sniff frequency range
ods_freql = 2
ods_freqh = 10

# 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 = [ ('Mint', 50, 20000, 4e-3) ]
# sim. duration
tstop = 1000.

initial_weights = '' 

# sniff interval
# None is random
# the number was constant
sniff_invl = None