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

xopen("tabchannels.hoc")
load_file("MC_def.hoc")         

tstop   = 6000
celsius = 35 

gnic_tuft = 0.0e-3  // mS/cm2

objref mit
mit = new Mitral(gnic_tuft)

load_file("MC_save.hoc") 
nrncontrolmenu()

// Current injection
T1  = 1500     // 
Dur = 4000
Ic1 = 0.18     //

// Noisy current injection
F0  = 0.0   // 
F1  = 0.0   // 
   

 objref Stim1, Stim2
 mit.soma Stim1 = new IClamp(0.5)
 Stim1.del = T1
 Stim1.dur = Dur		
 Stim1.amp = Ic1  
 
 mit.tuft Stim2 = new OdorInput(0.5)
 Stim2.del  = 1000
 Stim2.dur  = Dur		
 Stim2.torn = 3000
 Stim2.r    = 100
 Stim2.f0   = F0
 Stim2.f1   = F1 
 
 
// Exponential synapse
objref syn1, syn2
mit.tuft syn1 = new ExpSyn(0.5)
syn1.tau =  10
syn1.e   =  0   // -70

// Alpha synapses   
mit.dend syn2 = new AlphaSynapse(0.5)     // Tuft  
//mit.tuft syn2 = new AlphaSynapse(0.0)   // Tuft
syn2.onset = 3362    // 3255 for 0.2 nA
syn2.tau   = 3
syn2.gmax  = 0.0    // Dend: 0.003/0.01; Tuft:0.005/0.02
syn2.e     = -80	 
	 
	 
// Artifical Spiking Input
objref nc
nc = new NetStim(.5)
nc.number   = 100
nc.start    = 1000
nc.interval = 100
nc.noise    = 0

objref ns
ns = new NetCon(nc, syn1) // mit.AMPA
ns.threshold = 0
ns.delay     = 0
ns.weight    = 0e-3       // 30 uS   
 

proc advance() {
fadvance()
 
}

proc run() {
  running_ = 1
  stdinit()
  continuerun(tstop)
}

 
objref mitrate, spk
spk = new Vector()
mitrate = new Vector()

T = (tstop-T1)/1000

proc firing_rate() { local i, n
    n = 0
	if (mit.spiketimes.size() > 0) {
      spk = mit.spiketimes
	  ind = spk.indwhere(">", T1)
	  if (ind != -1) {
	   //print ind
	   n = spk.size()-ind
	 }
    }
	mitrate.append(n/T)
	mitrate.printf()
} 
  
objref g1, g2, g3, g4, g5, g6, g7, g8, g9
proc fig()  {


g3 = new Graph(0)
addplot(g3, 0)
g3.size(0,tstop,-70,60)
g3.view(0,-80,tstop,140, 0,150,400,150)
g3.addvar("Tuft.V", "mit.tuft.v(0.5)", 1, 1, 0.9, 0.9, 2)  

g4 = new Graph(0)
addplot(g4, 0)
g4.size(0,tstop,-70,60)
g4.view(0,-80,tstop,140, 0,420,400,150)
g4.addvar("Prim.V", "mit.prim.v(0.5)", 5, 1, 0.9, 0.9, 2)  

g1 = new Graph(0)
addplot(g1, 0)
g1.size(0,tstop,-70,60)
g1.view(0,-80,tstop,140, 0,680,400,150)
g1.addvar("Soma.V", "mit.soma.v(0.5)", 3, 1, 0.9, 0.9, 2)  

g2 = new Graph(0)
addplot(g2, 0)
g2.size(0,tstop,-70,60)
g2.view(0,-80,tstop,140, 0,920,400,150)
g2.addvar("Dend.V", "mit.dend.v(1.0)", 2, 1, 0.9, 0.9, 2)  //1: black; 2: red; 3: blue

/*
g9 = new Graph(0)
addplot(g9, 0)
g9.size(0,tstop,0,1)
g9.view(0,0,tstop,1, 450,200,400,150)
g9.addvar("Conductance", "syn2.i", 2, 1, 0.9, 0.9, 2)  //1: black; 2: red; 3: blue
*/



g6 = new Graph(0)
addplot(g6, 0)
g6.size(0,tstop,0,1)
g6.view(0,0,tstop,1, 0,150,400,150)
g6.addvar("Ks Activation", "mit.soma.m_IKs", 2, 1, 0.9, 0.9, 2)  
g6.addvar("Ks Inactivation", "mit.soma.h_IKs", 1, 1, 0.9, 0.9, 2)

/*
g5 = new Graph(0)
addplot(g5, 0)
g5.size(0,tstop,-2,2)
g5.view(0,-2,tstop,4, 450,200,400,150)
g5.addvar("Noisy input", "Stim2.i", 2, 1, 0.9, 0.9, 2)  //1: black; 2: red; 3: blue


g7 = new Graph(0)
addplot(g7, 0)
g7.size(0,tstop,0,0.0001)
g7.view(0,0,tstop,0.0001, 0,420,400,150)
g7.addvar("Cai", "mit.soma.cai", 2, 1, 0.9, 0.9, 2)


g8 = new Graph(0)
addplot(g8, 0)
g8.size(0,tstop,-0.0005,0.0005)
g8.view(0,-0.005,tstop,0.01, 450,200,400,150)
g8.addvar("IKCa", "mit.soma.ik_IKCa", 1, 1, 0.9, 0.9, 2)  //1: black; 2: red; 3: blue
*/

}


fig()
run()
//firing_rate()
save_data()