3D olfactory bulb: operators (Migliore et al, 2015)

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Accession:168591
"... Using a 3D model of mitral and granule cell interactions supported by experimental findings, combined with a matrix-based representation of glomerular operations, we identify the mechanisms for forming one or more glomerular units in response to a given odor, how and to what extent the glomerular units interfere or interact with each other during learning, their computational role within the olfactory bulb microcircuit, and how their actions can be formalized into a theoretical framework in which the olfactory bulb can be considered to contain "odor operators" unique to each individual. ..."
Reference:
1 . Migliore M, Cavarretta F, Marasco A, Tulumello E, Hines ML, Shepherd GM (2015) Synaptic clusters function as odor operators in the olfactory bulb. Proc Natl Acad Sci U S A 112:8499-504 [PubMed]
Model Information (Click on a link to find other models with that property)
Model Type: Realistic Network;
Brain Region(s)/Organism:
Cell Type(s): Olfactory bulb main mitral GLU cell; Olfactory bulb main interneuron granule MC GABA cell;
Channel(s): I Na,t; I A; I K;
Gap Junctions:
Receptor(s): AMPA; NMDA; Gaba;
Gene(s):
Transmitter(s): Gaba; Glutamate;
Simulation Environment: NEURON; Python;
Model Concept(s): Activity Patterns; Dendritic Action Potentials; Active Dendrites; Synaptic Plasticity; Action Potentials; Synaptic Integration; Unsupervised Learning; Sensory processing; Olfaction;
Implementer(s): Migliore, Michele [Michele.Migliore at Yale.edu]; Cavarretta, Francesco [francescocavarretta at hotmail.it];
Search NeuronDB for information about:  Olfactory bulb main mitral GLU cell; Olfactory bulb main interneuron granule MC GABA cell; AMPA; NMDA; Gaba; I Na,t; I A; I K; Gaba; Glutamate;
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figure1eBulb3D
readme.html
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# 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 = 27
#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 = 1 # nS
inh_gmax = 2e-3 # 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, 6.5e-3) ]
#odor_sequence = [ 'seq.txt' ]

# segments to records
# (cell gid, section index, arc, output filename)
sec2rec = []

# sim. duration
tstop = 7050

initial_weights = '' 

# dummy syns parameters
dummy_syn_conn = ''
#dummy_syn_conn = 'dummysyn-c%d-d%d.txt' % (mean_synapse_interval, grid_dim)
dummy_syn_freq = 10.

# sniff interval
# None is random
# the number was constant
sniff_invl = 100


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