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: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 GLU cell; Olfactory bulb main interneuron periglomerular GABA cell; Olfactory bulb main interneuron granule MC GABA 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; Olfaction;
Implementer(s): Morse, Tom [Tom.Morse at Yale.edu];
Search NeuronDB for information about:  Olfactory bulb main mitral GLU cell; Olfactory bulb main interneuron periglomerular GABA cell; Olfactory bulb main interneuron granule MC GABA cell;
Files displayed below are from the implementation
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ShortEtAl2016
early_theta_version
event_generator
import
py
run_0
run_1
run_10
run_11
run_12
run_13
run_14
run_15
run_16
run_17
run_2
run_3
run_4
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run_6
run_7
run_8
run_9
run_test
saved_sim_makers
spike_output
stimulation
synaptic_activity
tdt2mat_data
tmp
VecStim
readme.html
readme.louise
readme.NSG
readme.specialcase.txt
ampanmda.mod *
cadecay.mod *
cadecay2.mod *
Caint.mod *
Can.mod *
CaPN.mod *
CaT.mod *
fi.mod
GradeAMPA.mod *
GradeGABA.mod *
GradNMDA.mod *
hpg.mod *
Ih.mod *
kamt.mod *
KCa.mod *
kdrmt.mod *
kfasttab.mod *
kM.mod *
KS.mod *
kslowtab.mod *
LCa.mod *
nafast.mod *
NaP.mod *
naxn.mod
Nicotin.mod *
nmdanet.mod *
OdorInput.mod *
thetastim.mod *
ThreshDetect.mod *
vecstim.mod *
batch_run_first_NSG.py
batch_runs.py
batch_runs.py20150708
batch_runs.py20150808gc_error
batch_runs_first_NSG.py
build_net.hoc
build_net_Shep.hoc
build_net_Shep_NSG.hoc
build_net_Shep_NSG20160825.hoc
build_net_SMS.hoc
build_net_theta.hoc
build_net20150312.hoc
build_pg_net.hoc
cell_properties_for_ET_from_standalone.txt
cells_volt_graphs.ses
cells_volt_graphs_pg.ses
create_arrays.py
documentation.txt
et.hoc
et_rig.ses
et_rig2.ses
Et_start.zip
granule.hoc *
graph_fncs.hoc
graph_fncs_pg.hoc
gui_stim.hoc
how_to_run_pre_init_on_mac.txt
inhib_study.eps
inhib_study.ps
init.hoc
init.py
make_lookup_table.sh
makelib.err
makelib.out
mct_cells.hoc
mitral.hoc
mosinit.hoc
nrnivmodl.out
num_of_columns.hoc
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
screenshot0.png
tdt2mat_data.hoc
temporary_file.tmp
test_matplotlib.hoc
                            
// PG cell template
// modified to add a second dendrite

begintemplate PGcell
public soma, dend, gemmshaft, gemmbody, dend1, gemmshaft1, gemmbody1
public AMPAr, spiketimes, dendspike, spikecount

create soma, dend, gemmshaft, gemmbody, dend1, gemmshaft1, gemmbody1
objref AMPAr, spiketimes, dendspike, spikecount, dendcount

proc parameter() { 

    AMPAtau		= 5.5		
    Erev		= 0		    
	shell_depth = 0.2       
		
		RM	= 20e3  	// ohm.cm2  
        EL 	= -65		
	    
		ENA =  45       
        EK  = -80	    
	    ECA = 100
		
	    gNa_Soma = 50e-3  
	    gNa_Dend = 20e-3  
	       Sh_Na =   5       
		   
	    gKdr_Soma = 20e-3  
	    gKdr_Dend = 5e-3   	
	
	    gA_Soma = 10e-3    
	    gA_Dend = 30e-3    
	       Sha_A =     0   
	       Shi_A =   -20   
		   k_tauH  = 2.5   
           sh_tauH =   0   

	    gH_Dend = 0.2e-3   
		    
	    gM_Soma = 1.0e-3   
	    gM_Dend = 0.0e-3   	   
	   
	    gKCa_Soma  = 0.0e-3   
	    gKCa_Dend  = 2.0e-3  
	   
	    gCapn_Soma = 0.0e-3		   
	    gCapn_Dend = 1.0e-3	
	    
		gCaT_Soma = 0.0e-3
	    gCaT_Dend = 3.0e-3   
	       Sha_T  =  -15     
		   Shi_T  =    0     
	       K_tauH =  1.0
		   
		gcan_Dend = 0.0e-3   
	   
}


proc celldef() {
topol()
subsets()
segments()
geometry()
biophysics($1)
}

proc topol() {											
	connect dend(0), soma(1)
	connect gemmshaft(0), dend(1)
	connect gemmbody(0), gemmshaft(1)

	connect dend1(0), soma(1)
	connect gemmshaft1(0), dend1(1)
	connect gemmbody1(0), gemmshaft1(1)
}

// create subsets	
objref pg_all, pgdendgemm, spine
proc subsets() {
	// gemmules (body & shaft)
        spine = new SectionList()	 
	forsec "gemm" spine.append()
	
	// dendrites & gemmules (body & shaft)
	pgdendgemm = new SectionList()
	forsec "dend" pgdendgemm.append()
	forsec "gemm" pgdendgemm.append()	

	// all pg sections
	pg_all = new SectionList()
	forsec pgdendgemm pg_all.append()
	soma pg_all.append()
} 
proc segments() {
        forall {nseg=1}
	// dend1.nseg = 1 // use to study effect of nseg later if desired
}
proc geometry() {				
	soma { L=8  diam=8 }			
	forsec "dend" { L=100 diam=1 }     
	forsec spine { L=1 diam=1 }
	define_shape()					
}

proc biophysics() {
    
    parameter()
	spiketimes = new Vector()
	dendspike  = new Vector()
	
	spike_threshold = -10  
	
	forsec pg_all { 		// insert passive current everywhere
		Ra = 80        
		cm = 1.2        
		
		insert pas
		  g_pas = 1/RM 	
		  e_pas = EL		
	}
	
	soma {	
	
	insert nax
        gbar_nax  = gNa_Soma
        sh_nax  = Sh_Na	
	insert kdrmt
	    gbar_kdrmt = gKdr_Soma  
        q10_kdrmt  = 3		
	insert kamt
        gbar_kamt = gA_Soma    
		sha_kamt  = Sha_A 
		shi_kamt  = Shi_A
		k_tauH_kamt = k_tauH
		sh_tauH_kamt = sh_tauH
    insert kM
        gkbar_kM  = gM_Soma     	
   	
	insert Icapn
        gbar_Icapn = gCapn_Soma		
	insert Ikca
        gkbar_Ikca = gKCa_Soma
		
	insert Icat
	    gbar_Icat = gCaT_Soma 
		sha_Icat  = Sha_T
		shi_Icat  = Shi_T
	insert cad2     
        depth_cad2  = shell_depth
			
	ena = ENA
	ek  = EK
    eca = ECA
	
	spikecount  = new APCount(0.5)
    spikecount.thresh = spike_threshold
    spikecount.record(spiketimes)
	}
	
	forsec pgdendgemm {		
	insert nax
        gbar_nax = gNa_Dend	
		sh_nax  = Sh_Na
    insert kdrmt
	    gbar_kdrmt = gKdr_Dend   
        q10_kdrmt  = 3		
	insert kamt
        gbar_kamt  = gA_Dend     
		sha_kamt = Sha_A 
		shi_kamt = Shi_A
		k_tauH_kamt = k_tauH
		sh_tauH_kamt = sh_tauH		
	insert kM
        gkbar_kM  = gM_Dend   
	
	insert hpg 			
	  eh_hpg = 0 
	  ghbar_hpg = gH_Dend
	  
	insert Ican
	    gbar_Ican = gcan_Dend 
	insert Ikca
        gkbar_Ikca = gKCa_Dend 
	insert Icapn
        gbar_Icapn = gCapn_Dend	
 	    
	insert Icat
	    gbar_Icat = gCaT_Dend 
		sha_Icat  = Sha_T
		shi_Icat  = Shi_T
		k_tauH_Icat = K_tauH
	insert cad2    
        depth_cad2 = shell_depth		
	
	ena = ENA
	ek  = EK
	eca = ECA
	}
	
	forsec spine {	
	  insert Inic
	  enic_Inic = 3.2
	  gbar_Inic = $1     
	}
// this synapse ignored by Shaina and Tom's use of the model	
   gemmbody{
	AMPAr = new ExpSyn(0.5)
    AMPAr.tau = AMPAtau
    AMPAr.e 	= Erev
	  
	dendcount = new APCount(0.5)
    dendcount.thresh = spike_threshold
    dendcount.record(dendspike)
    }
}

proc init() {
  celldef($1)
}

endtemplate PGcell

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