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 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
                            
//=============================================
//            Granule Cells with Spines
//=============================================

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

v_init  = -70 
tstop   = 2500
celsius = 35

MUSCAR = 0  // 1: Muscarinic effect on; 0: OFF

objref Gran
Gran = new Granule(MUSCAR)

load_file("GC_save.hoc")

objref stim1, stim2, stim3, stim4
Gran.soma stim1 = new IClamp(0.5)
Gran.soma stim2 = new IClamp(0.5)
Gran.soma stim3 = new IClamp(0.5)
Gran.soma stim4 = new IClamp(0.5)

stim1.del = 0       // 500
stim1.dur = tstop
stim1.amp = 0.0     //  0.0102/0.01 for ADP; 0.0105 for AHP  | 0.0106/0.116 for CCH

stim2.del = 500
stim2.dur = 50     // 
stim2.amp = 0.0    //  

stim3.del = 1000
stim3.dur = 600    // 500 for ADP
stim3.amp = 0.03   // 0.103/0.115 for ADP; 0.118 for AHP; 0.1 for CCH

stim4.del = 1800
stim4.dur = 50
stim4.amp = -0.0    // 


objref g1,g2,g3,g4,g5,g6,g7

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", "Gran.soma.v(0.5)", 3, 1, 0.8, 0.9, 2)  //1: black; 2: red; 3: blue

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("Dend.V", "Gran.dend.v(0.5)", 2, 1, 0.8, 0.9, 2)  


g2 = new Graph(0)
addplot(g2, 0)
g2.size(0,tstop,0,0.5)
g2.view(0,0,tstop,0.5, 0,700,500,130)
g2.addvar("Dend.Ca", "Gran.dend.cai", 2, 2, 0.8, 0.9, 2)  

g7 = new Graph(0)
addplot(g7, 0)
g7.size(0,tstop,0,1)
g7.view(0,0,tstop,1, 0,900,500,150)
g7.addvar("IA.m", "Gran.dend.m_kamt", 5, 2, 0.8, 0.9, 2) 
g7.addvar("IA.h", "Gran.dend.h_kamt", 1, 2, 0.8, 0.9, 2)  

g3 = new Graph(0)
addplot(g3, 0)
g3.size(0,tstop,-0.05,0.05)
g3.view(0,-0.05,tstop,0.1, 0,900,500,130)
g3.addvar("IA", "Gran.dend.ik_kamt", 3, 2, 0.8, 0.9, 2)  


/*

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("Ican.m", "Gran.dend.m_Ican", 5, 2, 0.8, 0.9, 2)  //1: black; 2: red; 3: blue

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("Soma.Ca", "Gran.soma.cai", 2, 2, 0.8, 0.9, 2)
*/

}

fig1()
run()
save_data()