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
                            
// Parameters of the 2D OB network model

// Random seeds
seedU  = 88    // For uniform distribution   
seedN  = 100   // For normal (Gaussian) distribution  
NSSEED = 2     // Random seed for the background inputs

// Number of neurons
nmitx  = 5   // MC array
nmity  = 5   // MC array
nMit   = nmitx*nmity   // 25

npgx   = 5   // PG array
npgy   = 5   // PG array
nPG    = npgx*npgy     // 25

ngranx = 10  // GC array
ngrany = 10  // GC array
nGran  = ngranx*ngrany // default: 100 
Hgc = (ngranx)/2  // Middle point of GC

RATIO = nGran/100


// Odor inputs
Todor    = 2000   // Start time of odor
RiseRate =  100   // Rise rate of the odor

Pre_Odor_L = 0.1  // Pre_odor lower limit
Pre_Odor_U = 0.2  // Pre_odor upper limit

Odor_L = 0.2      // Steady odor lower limit
Odor_U = 1.0      // Steady odor upper limit  


// Connectivity
Pc = 0.3     // Connectivity probability between MCs and GCs

// Spatial distance
LEN = 1000    // Length of the OB in um
R   =  500    // Length of the MC lateral dendrite
dm = LEN/(nmitx-1)   // Distance between two neighboring MCs
dg = LEN/(ngranx-1)  // Distance between two neighboring GCs 

// Specific inputs
Km2p = 0.4    // Scaling factor of PG to MC input intensity 
  
// Synaptic weights
Wm2p = 1   // MC to PG   
Wm2g = 1   // MC to GC   

Wp2m = 4         // PG to MC      
Wg2m = 2/RATIO   // GC to MC  

// Peak conductance
AMPAgmaxPG  =  2e-3    //  Peak conductance for MC-PG 
NMDAgmaxPG  =  1e-3    //  Peak conductance for MC-PG
GABAAgmaxPG =  2e-3    //  Peak  conductance for PG-MC

AMPAgmaxGC  =  2e-3    //  Peak conductance for MC-GC
NMDAgmaxGC  =  1e-3    //  Peak conductance for MC-GC
GABAAgmaxGC =  2e-3    //  Peak  conductance for GC-MC

Gampa =  30e-3  // 30 nS, summed peak conductance for MC-GC
Gnmda =  15e-3  // 15 nS, summed peak conductance for MC-GC 
Ggaba =  60e-3  // 60 nS, summed peak conductance for GC-MC

// Synaptic time constants
tau1_AMPA  = 1       // AMPA rise time constant
tau2_AMPA  = 5.5     // AMPA decay time constant

tau1_NMDA  = 52      // NMDA rise time constant
tau2_NMDA  = 343     // NMDA decay time constant

tau1_GABA_PG = 1.25    // GABA rise time constant (PG-MC) 
tau2_GABA_PG = 18      // GABA decay time constant (PG-MC)  

tau1_GABA_GC = 1.25      // GABA rise time constant (GC-MC) 
tau2_GABA_GC = 18        // GABA decay time constant (GC-MC) 

AMPAalpha  = 1/tau1_AMPA      
AMPAbeta   = 1/tau2_AMPA
NMDAalpha  = 1/tau1_NMDA
NMDAbeta   = 1/tau2_NMDA

GABAAalpha_PG = 1/tau1_GABA_PG
GABAAbeta_PG  = 1/tau2_GABA_PG

GABAAalpha_GC = 1/tau1_GABA_GC
GABAAbeta_GC  = 1/tau2_GABA_GC

// Reserval potentials
AMPArev = 0
NMDArev = 0
GABAArev = -80

// Synapitc activation threshold
AMPAact  = 0     
NMDAact  = 0     
GABAAact = -40   

// Synaptic gradeness
AMPAsigma  = 0.2     
NMDAsigma  = 0.2     
GABAAsigma = 2


//Simulation of ACh effect (Not used in the current model)
NICOTIN  = 0  // 0: No effect
MUSCARIN = 0  // 0: No effect



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