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 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
vis
bulbdef.py
bulbdict.py
bulbgui.py
bulbvis.py
cellreader.py
cellwriter.py
cfg27.py
dummysyns.txt
Eta.txt *
firing.py
geodist.py
geodist.txt
ggid2type.txt
gidfunc.py
glomdist.py
granules.py
granules.txt
graphmeat.py
growdef.py *
ipsc.py
ispkdata.py
Kod.txt *
misc.py
Nod.txt *
odors.py
odstim2.txt *
pad.txt *
realgloms.txt *
spikes.py
spikesreader.py
spk2gd.py
spk2weight.py
spkgraph.py
winflag.txt
                            
# synapses conductances
mc_exc_gmax = 3.25 # nS
mc_inh_gmax = 0.3 # uS

mt_exc_gmax = 3.25 # nS
mt_inh_gmax = 0.3 # uS

# 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 = [ (x, None, None) for x in range(1005, 1015) ]

# 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 = 0.
init_inh_weight = 0.

training_exc = False
training_inh = False