Mitral cell activity gating by respiration and inhibition in an olfactory bulb NN (Short et al 2016)

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Accession:183300
To explore interactions between respiration, inhibition, and olfaction, experiments using light to active channel rhodopsin in sensory neurons expressing Olfactory Marker Protein were performed in mice and modeled in silico. This archive contains NEURON models that were run on parallel computers to explore the interactions between varying strengths of respiratory activity and olfactory sensory neuron input and the roles of periglomerular, granule, and external tufted cells in shaping mitral cell responses.
Reference:
1 . Short SM, Morse TM, McTavish TS, Shepherd GM, Verhagen JV (2016) Respiration gates sensory input responses in the Mitral Cell layer of the Olfactory Bulb PLOS ONE 11(12):e0168356 [PubMed]
Model Information (Click on a link to find other models with that property)
Model Type: Realistic Network; Neuron or other electrically excitable cell;
Brain Region(s)/Organism: Olfactory bulb;
Cell Type(s): Olfactory bulb main mitral cell; Olfactory bulb main interneuron periglomerular cell; Olfactory bulb main interneuron granule MC cell; Olfactory bulb main tufted cell external;
Channel(s):
Gap Junctions:
Receptor(s):
Gene(s):
Transmitter(s):
Simulation Environment: NEURON;
Model Concept(s): Activity Patterns; Sensory processing; Sensory coding; Bursting; Oscillations;
Implementer(s): Morse, Tom [Tom.Morse at Yale.edu];
Search NeuronDB for information about:  Olfactory bulb main mitral cell; Olfactory bulb main interneuron periglomerular cell; Olfactory bulb main interneuron granule MC cell;
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ShortEtAl2016
early_theta_version
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batch_run_first_NSG.py
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build_net_Shep_NSG20160825.hoc
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build_net20150312.hoc
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cell_properties_for_ET_from_standalone.txt
cells_volt_graphs.ses
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Et_start.zip
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make_lookup_table.sh
makelib.err
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mitral.hoc
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PG_def.hoc
pre_init.py
pre_init_first_NSG.py
pre_init_no_changes_in_weights.py
roberts_python_help.txt
run_on_serial.hoc
runcntrl.ses
sample_gc1_v_graph.ses
sample_mitral_pg_space_plots.ses
screenshot.png
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tdt2mat_data.hoc
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test_matplotlib.hoc
                            
TITLE Mechanism  internal calcium concentration cai

NEURON {
	SUFFIX Cacon
	USEION ca READ ica, cai WRITE cai
	GLOBAL tauca, A, camin
}

UNITS { 
 (mA) = (milliamp) 
 (mV) = (millivolt) 
 (molar) = (1/liter) 
 (mM) = (millimolar)
 (uM) = (micromolar) 
} 

PARAMETER {  
 dt (ms) 
 tauca = 800 (ms) 
: A = 1.03e-7 (mM cm2 / ms mA) : same as in paper 
 A = 0.2  :0.103
 camin = 1e-8 (mM)  : arbitrary

} 

STATE {
	cai		(mM) 
}

INITIAL {
	cai = camin
}

ASSIGNED { 
 ica (mA/cm2)    
} 

BREAKPOINT {
	SOLVE state METHOD cnexp
}

DERIVATIVE state {
	 cai' = -A*ica - (cai-camin)/tauca 
}

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