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 Computational Biology 13(11):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
                            
// Connectivity of the 2D OB network

objref outfile, outfile2
outfile  = new File()
outfile2 = new File()

strdef outfilepath, filename
outfilepath = "connection/"

for k = 0, ngranx-1 {
	    for l = 0, ngrany-1 {
		sprint(filename, "%sGC%d%d",outfilepath, k,l)
		outfile2.wopen(filename)
		outfile2.printf("This GC connects to the following MCs\n")
		outfile2.close()
	}
	 }

objref ru, rn
ru = new Random(seedU)
rn = new Random(seedN)

null = ru.uniform(0, 1)

if (NICOTIN == 0) { 
    gnic_MC = 0.0e-3  // mS/cm2
    gnic_PG = 0.0e-3  
   } else {
    gnic_MC = 1.0e-3  // mS/cm2
    gnic_PG = 8.0e-3  //  
   }
   

//=========================================================================  
//                           Create cells 
//=========================================================================

objref mit[nmitx][nmity], pg[npgx][npgy], gran[ngranx][ngrany]
// MC
for i = 0, nmitx-1 {
    for j = 0, nmity-1 {
	  seed = i*nmity+j
      mit[i][j] = new Mitral(gnic_MC)
    }
}

// PG
for i = 0, npgx-1 {
    for j = 0, npgy-1 {
      pg[i][j] = new PGcell(gnic_PG)
    }
}

// GC
for i = 0, ngranx-1 {
    for j = 0, ngrany-1 {
      gran[i][j] = new Granule(MUSCARIN)
    }
}

//=========================================================================
//                    Connection between MCs and PGs
//=========================================================================

objref m2pAMPA[nmitx][nmity], m2pNMDA[nmitx][nmity], p2m[nmitx][nmity]

for i=0, nmitx-1 {
  for j = 0, nmity-1 {
    // AMPA synapses
    AMPAgmax = Wm2p*AMPAgmaxPG
    pg[i][j].gemmbody m2pAMPA[i][j] = new gradAMPA(0.5)	
    setpointer    m2pAMPA[i][j].vpre, mit[i][j].tuft.v(0.5) 
    m2pAMPA[i][j].gmax  = AMPAgmax  
    m2pAMPA[i][j].alpha = AMPAalpha 
    m2pAMPA[i][j].beta  = AMPAbeta  	
    m2pAMPA[i][j].thetasyn = AMPAact
    m2pAMPA[i][j].sigma = AMPAsigma
    m2pAMPA[i][j].e = AMPArev 
    
    // NMDA synapses
    NMDAgmax = Wm2p*NMDAgmaxPG 
    pg[i][j].gemmbody m2pNMDA[i][j] = new gradNMDA(0.5)	
    setpointer    m2pNMDA[i][j].vpre, mit[i][j].tuft.v(0.5) 
    m2pNMDA[i][j].gmax  = NMDAgmax  
    m2pNMDA[i][j].alpha = NMDAalpha 
    m2pNMDA[i][j].beta  = NMDAbeta  	
    m2pNMDA[i][j].thetasyn = NMDAact
    m2pNMDA[i][j].sigma = NMDAsigma
    m2pNMDA[i][j].e = NMDArev 	
	
   // GABAA synapses
    GABAAgmax = Wp2m*GABAAgmaxPG
    mit[i][j].tuft p2m[i][j] = new gradGABA(0.5)	
    setpointer     p2m[i][j].vpre, pg[i][j].gemmbody.v(0.5) 
    p2m[i][j].gmax  = GABAAgmax  
    p2m[i][j].alpha = GABAAalpha_PG 
    p2m[i][j].beta  = GABAAbeta_PG  	
    p2m[i][j].thetasyn = GABAAact
    p2m[i][j].sigma = GABAAsigma
    p2m[i][j].e = GABAArev 
    
   }
  }



//====================================================================================
//                 Connection between MCs and GCs  
//====================================================================================


objref NC
NC = new Vector()

objref m2gAMPA[nGran][nMit], m2gNMDA[nGran][nMit], g2m[nMit][nGran]

double MGS[ngranx][ngrany]   // record the number of synapses from MCs to each GC
double GMS[nMit]

for i = 0, ngranx-1 { 
   for j = 0, ngrany-1 {
   MGS[i][j] = 0
   }
}

null = ru.uniform(0, 1)


outfile.wopen("connection/MC2GC")
outfile.printf("Connections from MC cells to GC cells:\n")
outfile.close


for i=0, nmitx-1 {
   for j = 0, nmity-1 {
    
	count = 0
	M = i*nmity+j  // for the pointer
	Z = 0          // for the pointer 

	outfile.aopen("connection/MC2GC")
	outfile.printf("From the %d MC cell(%d,%d):\n", M+1,i,j)
	outfile.close
	
	x0 = dm*i  // coordinate x for MC
	y0 = dm*j  // coordinate y for MC
	
    for k = 0, ngranx-1 {
	for l = 0, ngrany-1 {
	         
        N = k*ngrany+l   		
		
        x1 = dg*k  // real coordinate x in the 2D space
        y1 = dg*l  // real coordinate y	in the 2D space	
			 
        dx = x0-x1
        dy = y0-y1

        nx = abs(dx)/dg 
        ny = abs(dy)/dg		
	        
  	if (nx > Hgc) {
	    if (dx>0) {
	      kk = ngranx + k
            } else {
              kk = k - ngranx  
            }			
		
	} else {
	     kk = k
	}
	
	
	if (ny > Hgc) {
            if (dy>0) {
	      ll = ngrany + l
            } else {
              ll = l - ngrany  
            }			
	} else {
	      ll = l
	}		
		
	x2 = dg*kk       // mapped coordinate x  in the torus
	y2 = dg*ll	 // mapped coordinate y  in the torus
	   
	d = sqrt((x2-x0)^2+(y2-y0)^2)
	  if (d>R) {
	    d = R
 	}

	Pr = ru.repick()

	if (Pr<= Pc) {
				
	  sprint(filename, "%sGC%d%d",outfilepath, k,l)
	  outfile2.aopen(filename)
	  outfile2.printf("(%d,%d) %3.2f \n", i,j,d)
	  outfile2.close()
	
	  H = MGS[k][l]	
		
	// AMPA synapses
	// AMPAgmax  = (We*Gampa)/Ng    // ~2 nS
	AMPAgmax = Wm2g*AMPAgmaxGC
        gran[k][l].gemmbody m2gAMPA[N][H] = new gradAMPA(0.5)	
	setpointer          m2gAMPA[N][H].vpre, mit[i][j].dend.v(d/R) 
	m2gAMPA[N][H].gmax  = AMPAgmax  
        m2gAMPA[N][H].alpha = AMPAalpha 
        m2gAMPA[N][H].beta  = AMPAbeta  	
        m2gAMPA[N][H].thetasyn = AMPAact
	m2gAMPA[N][H].sigma = AMPAsigma
        m2gAMPA[N][H].e = AMPArev
	
        // NMDA synapses
	// NMDAgmax  = (We*Gnmda)/Ng    // ~1 nS
 	NMDAgmax = Wm2g*NMDAgmaxGC       
	gran[k][l].gemmbody m2gNMDA[N][H] = new gradNMDA(0.5)	
	setpointer          m2gNMDA[N][H].vpre, mit[i][j].dend.v(d/R) 
        m2gNMDA[N][H].gmax  = NMDAgmax   
        m2gNMDA[N][H].alpha = NMDAalpha
        m2gNMDA[N][H].beta  = NMDAbeta	
        m2gNMDA[N][H].thetasyn = NMDAact
	m2gNMDA[N][H].sigma = NMDAsigma
        m2gNMDA[N][H].e = NMDArev	
		
	// Graded inhibtion
	// GABAAgmax = (Wi*Ggaba)/Nm    // ~2 nS
        GABAAgmax = Wg2m*GABAAgmaxGC
	mit[i][j].dend g2m[M][Z] = new gradGABA(d/R)	
	setpointer     g2m[M][Z].vpre, gran[k][l].gemmbody.v(0.5)
        g2m[M][Z].gmax  = GABAAgmax   
        g2m[M][Z].alpha = GABAAalpha_GC
        g2m[M][Z].beta  = GABAAbeta_GC
        g2m[M][Z].thetasyn = GABAAact
	g2m[M][Z].sigma = GABAAsigma
        g2m[M][Z].e = GABAArev
		
	Z = Z + 1
	MGS[k][l] = MGS[k][l]+1
		
        count = count + 1	
	if ( count/6-int(count/5) == 0){
	   outfile.printf("\n")
	}

	outfile.aopen("connection/MC2GC")
        outfile.printf("(%d,%d)(%d, %d) %3.2f; ", k, l, kk, ll,d/R)
      }

    }
   }
	GMS[M]=Z
        
        NC.append(count)
	outfile.printf("\n%d\n\n", count)
	outfile.close()	 
 }
}	



outfile.wopen("connection/MGS")
for i = 0, ngranx-1 { 
   for j = 0, ngrany-1 {
   index = i*ngranx+j
   if ( (index%10) == 0){
      outfile.printf("\n")
   }
   outfile.printf("%d  ", MGS[i][j])
   }
}

outfile.close()


Ntotal = NC.sum()
print "\nTotal number of MC-GC projection is\n"
print Ntotal	 

print "\nThe average number of GC inputs per MC is\n"
print Ntotal/nMit
print "\nThe average number of MC inputs per GC is\n"
print Ntotal/nGran
print "\n"	 






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