A two-layer biophysical olfactory bulb model of cholinergic neuromodulation (Li and Cleland 2013)

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Accession:149739
This is a two-layer biophysical olfactory bulb (OB) network model to study cholinergic neuromodulation. Simulations show that nicotinic receptor activation sharpens mitral cell receptive field, while muscarinic receptor activation enhances network synchrony and gamma oscillations. This general model suggests that the roles of nicotinic and muscarinic receptors in OB are both distinct and complementary to one another, together regulating the effects of ascending cholinergic inputs on olfactory bulb transformations.
Reference:
1 . Li G, Cleland TA (2013) A two-layer biophysical model of cholinergic neuromodulation in olfactory bulb. J Neurosci 33:3037-58 [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 cell; Olfactory bulb main interneuron periglomerular cell; Olfactory bulb main interneuron granule MC cell;
Channel(s): I Na,p; I L high threshold; I T low threshold; I A; I M; I h; I K,Ca; I CAN; I Sodium; I Calcium; I Potassium; I_Ks; I Cl, leak; I Ca,p;
Gap Junctions:
Receptor(s): Nicotinic; GabaA; Muscarinic; AMPA; NMDA;
Gene(s):
Transmitter(s): Acetylcholine;
Simulation Environment: NEURON; MATLAB;
Model Concept(s): Sensory processing; Sensory coding; Neuromodulation; Olfaction;
Implementer(s): Li, Guoshi [guoshi_li at med.unc.edu];
Search NeuronDB for information about:  Olfactory bulb main mitral cell; Olfactory bulb main interneuron periglomerular cell; Olfactory bulb main interneuron granule MC cell; Nicotinic; GabaA; Muscarinic; AMPA; NMDA; I Na,p; I L high threshold; I T low threshold; I A; I M; I h; I K,Ca; I CAN; I Sodium; I Calcium; I Potassium; I_Ks; I Cl, leak; I Ca,p; Acetylcholine;
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ACh_ModelDB
celldata
Connection
data
Input
SP
Readme.txt
cadecay.mod *
cadecay2.mod *
Caint.mod *
Can.mod *
CaPN.mod *
CaT.mod *
GradeAMPA.mod *
GradeGABA.mod *
GradNMDA.mod *
hpg.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 *
Background.hoc
Connect.hoc
GC_def.hoc
GC_save.hoc *
GC_Stim.hoc
Input.hoc
MC_def.hoc
MC_save.hoc
MC_Stim.hoc
mod_func.c
mosinit.hoc
OB.hoc
Parameter.hoc
PG_def.hoc
PG_save.hoc *
PG_Stim.hoc
SaveData.hoc
tabchannels.dat *
tabchannels.hoc
                            
//===============================================================================
//      PG Cell (One soma; one dend; one spine shaft and one spine body)
//===============================================================================

load_file("nrngui.hoc")
xopen("$(NEURONHOME)/lib/hoc/noload.hoc") // standard run tools
load_file("PG_def.hoc")         

tstop   = 1500
celsius = 35   

gnic_spine = 0.0e-3 // mS/cm2

objref pg
pg = new PGcell(gnic_spine)   

load_file("PG_save.hoc") 
//nrncontrolmenu()

// Current injection
T1    =  500   // 500
Dur   =  600   // 600
 

 Ic1 = -0.12  
   
 u0  = 0.0         
 u1  = 0.0   
 Wb_PG  = 0.0e-3 

  

 objref Stim1
 pg.soma Stim1 = new IClamp(0.5)
 Stim1.del = T1
 Stim1.dur = Dur		
 Stim1.amp = Ic1 

 objref Stim2
 pg.gemmbody  Stim2 = new OdorInput(0.0)
	Stim2.torn = 1000
	Stim2.r    = 100  
    Stim2.del  = 0
    Stim2.dur  = tstop
    Stim2.f0   = u0 	
	Stim2.f1   = u1
 
 
// Random background inputs!!!

Tb_ISI  = 10      // spike interval in background input
N_SPIKE = 1000    // number of spike in background input

objref PGbinput 
objref PGb
       PGb = new NetStim(.5)
       PGb.number   = N_SPIKE
       PGb.start    = 0
       PGb.interval = Tb_ISI
       PGb.noise    = 1
	
	   PGbinput = new NetCon(PGb, pg.AMPAr)
       PGbinput.threshold = 0
       PGbinput.delay     = 0
       PGbinput.weight    = Wb_PG  	 
 
  
objref g1,g2,g3,g4,g5, g6

proc fig1()  {

g1 = new Graph(0)
addplot(g1, 0)
g1.size(0,tstop,-80,50)
g1.view(0,-80,tstop,130, 0,150,500,160)
g1.addvar("Soma.V", "pg.gemmbody.v(0.5)", 3, 1, 0.8, 0.9, 2)  //1: black; 2: red; 3: blue

g4 = new Graph(0)
addplot(g4, 0)
g4.size(0,tstop,0,1)
g4.view(0,0,tstop,1, 0,900,500,150)
g4.addvar("Icat.m", "pg.dend.m_Icat", 5, 2, 0.8, 0.9, 2) 
g4.addvar("Icat.h", "pg.dend.h_Icat", 1, 2, 0.8, 0.9, 2)  

g6 = new Graph(0)
addplot(g6, 0)
g6.size(0,tstop,-80,50)
g6.view(0,-80,tstop,130, 0,500,500,160)
g6.addvar("Spine.V", "pg.gemmbody.v(0.5)", 2, 1, 0.8, 0.9, 2)  //

g5 = new Graph(0)
addplot(g5, 0)
g5.size(0,tstop,0,2)
g5.view(0,0,tstop,2, 0,450,500,130)
g5.addvar("Dend.Ca", "pg.dend.cai", 2, 2, 0.8, 0.9, 2)


g2 = new Graph(0)
addplot(g2, 0)
g2.size(0,tstop, 0,180)
g2.view(0,0,tstop,180, 0,700,500,130)
g2.addvar("Dend.ECa", "pg.dend.eca", 2, 2, 0.8, 0.9, 2)  //

/*
g3 = new Graph(0)
addplot(g3, 0)
g3.size(0,tstop,-0.0005,0.0005)
g3.view(0,-0.005,tstop,0.01, 450,200,400,150)
g3.addvar("ICaT", "pg.dend.ica_Icat", 1, 1, 0.9, 0.9, 2)  
*/

}

fig1()
run()
save_data()




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