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]
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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 periglomerular GABA cell; Olfactory bulb main interneuron granule MC GABA 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 GLU cell; Olfactory bulb main interneuron periglomerular GABA cell; Olfactory bulb main interneuron granule MC GABA 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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Readme.txt
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cadecay2.mod *
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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
                            
//======================================================================
//     Provide random background spike inputs to the network
//======================================================================

Tb_Start = 0    // Start time of background inputs

Tb_ISI  = 10   // Average spike interval in background input; F = 100 Hz
N_SPIKE = 1000 // Maximal number of spikes in one background input

Thresh = 0

// Synaptic weights of background inputs
Wb_MC  = 1.0e-3  // 
Wb_PG  = 0.5e-3  // 
Wb_GC  = 0.3e-3  //

objref MCbinput[nMit], PGbinput[nPG], GCbinput[nGran]  
objref MCb[nMit], PGb[nPG], GCb[nGran]
objref SP[nMit], WSP[nPG]


//=============================================================  
//             Spike-trigered background inputs
//=============================================================  
// For MCs
    for i = 0, nMit-1 {
	   SP[i] =  new Vector()
	
       MCb[i] = new NetStim(.5)
       MCb[i].number   = N_SPIKE
       MCb[i].start    = Tb_Start
       MCb[i].interval = Tb_ISI
       MCb[i].noise    = 1
	   MCb[i].seed(netseed)
	   
	   MCbinput[i] = new NetCon(MCb[i], mit[i].AMPA)
       MCbinput[i].threshold = Thresh
       MCbinput[i].delay     = 0
       MCbinput[i].weight    = Wb_MC  	
	   MCbinput[i].record(SP[i])
	       
  }	 
    

  // For PGs
   for i = 0, nPG-1 {
       WSP[i] =  new Vector()        
	   
       PGb[i] = new NetStim(.5)
       PGb[i].number   = N_SPIKE
       PGb[i].start    = Tb_Start
       PGb[i].interval = Tb_ISI
       PGb[i].noise    = 1
	   PGb[i].seed(netseed)
	
	   PGbinput[i] = new NetCon(PGb[i], pg[i].AMPAr)
       PGbinput[i].threshold = Thresh
       PGbinput[i].delay     = 0
       PGbinput[i].weight    = Wb_PG  	
	   PGbinput[i].record(WSP[i])
	   	   
    }	
	

  // For GCs
if (NTCE==0) {  
   for i = 0, nGran-1 {
       GCb[i] = new NetStim(.5)
       GCb[i].number   = N_SPIKE
       GCb[i].start    = Tb_Start
       GCb[i].interval = Tb_ISI
       GCb[i].noise    = 1
	   GCb[i].seed(netseed)
	
	   GCbinput[i] = new NetCon(GCb[i], gran[i].AMPAr)
       GCbinput[i].threshold = Thresh
       GCbinput[i].delay     = 0
       GCbinput[i].weight    = Wb_GC    	
    }	
}