2D model of olfactory bulb gamma oscillations (Li and Cleland 2017)

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Accession:232097
This is a biophysical model of the olfactory bulb (OB) that contains three types of neurons: mitral cells, granule cells and periglomerular cells. The model is used to study the cellular and synaptic mechanisms of OB gamma oscillations. We concluded that OB gamma oscillations can be best modeled by the coupled oscillator architecture termed pyramidal resonance inhibition network gamma (PRING).
Reference:
1 . Li G, Cleland TA (2017) A coupled-oscillator model of olfactory bulb gamma oscillations. PLoS Comput Biol 13:e1005760 [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 granule MC cell; Olfactory bulb main interneuron periglomerular cell;
Channel(s):
Gap Junctions:
Receptor(s): AMPA; NMDA; GabaA;
Gene(s):
Transmitter(s):
Simulation Environment: NEURON;
Model Concept(s): 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; GabaA; AMPA; NMDA;
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OBGAMMA
celldata
connection
data0
input
README
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 *
SineInput.mod
Background.hoc
Cal_Synch.hoc
Connect.hoc
Figure.hoc
GC_def.hoc
GC_save.hoc *
GC_Stim.hoc
Input.hoc
mathslib.hoc
MC_def.hoc
MC_save.hoc
MC_Stim.hoc
mosinit.hoc
OBNet.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 // to match pas 
tstop   = 2500
celsius = 35

MUSCAR = 0  // 1: Muscarinic effect

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    // 0.045 / 0.1/0.11


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()


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