Reconstructing cerebellar granule layer evoked LFP using convolution (ReConv) (Diwakar et al. 2011)

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Accession:139883
The model allows reconstruction of evoked local field potentials as seen in the cerebellar granular layer. The approach uses a detailed model of cerebellar granule neuron to generate data traces and then uses a "ReConv" or jittered repetitive convolution technique to reproduce post-synaptic local field potentials in the granular layer. The algorithm was used to generate both in vitro and in vivo evoked LFP and reflected the changes seen during LTP and LTD, when such changes were induced in the underlying neurons by modulating release probability of synapses and sodium channel regulated intrinsic excitability of the cells.
Reference:
1 . Diwakar S, Lombardo P, Solinas S, Naldi G, D'Angelo E (2011) Local field potential modeling predicts dense activation in cerebellar granule cells clusters under LTP and LTD control. PLoS One 6:e21928 [PubMed]
Model Information (Click on a link to find other models with that property)
Model Type: Neuron or other electrically excitable cell; Extracellular;
Brain Region(s)/Organism:
Cell Type(s): Cerebellum interneuron granule GLU cell;
Channel(s): I K; I M; I K,Ca; I Sodium; I Calcium; I Cl, leak;
Gap Junctions:
Receptor(s): GabaA; AMPA; NMDA;
Gene(s):
Transmitter(s):
Simulation Environment: NEURON; MATLAB; Octave;
Model Concept(s): Extracellular Fields; Evoked LFP;
Implementer(s): Diwakar, Shyam [shyam at amrita.edu];
Search NeuronDB for information about:  Cerebellum interneuron granule GLU cell; GabaA; AMPA; NMDA; I K; I M; I K,Ca; I Sodium; I Calcium; I Cl, leak;
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ReConv
data
readme.html
AmpaCOD.mod *
GRC_CA.mod *
GRC_CALC.mod *
GRC_GABA.mod *
GRC_KA.mod *
GRC_KCA.mod *
GRC_KIR.mod *
GRC_KM.mod *
GRC_KV.mod *
GRC_LKG1.mod *
GRC_LKG2.mod *
GRC_NA.mod *
NmdaS.mod *
Pregen.mod *
ComPanel.hoc
Grc_Cell.hoc
mosinit.hoc
Parametri.hoc
ReConv_GrC.jpg
ReConv_invitro.jpg
ReConv_invivo.jpg
Record_vext.hoc
Start.hoc
                            
// ReConv algorithm for reconstructing evoked LFP in cerebellar granular layer
// Uses Multicompartmental GrC model (see http://senselab.med.yale.edu/ModelDb/showmodel.asp?model=116835)
// Last updated 11-June-2011
// Model developer: Shyam Diwakar M.
// Developed at Amrita School of Biotechnology (India) and at Prof. Egidio D'Angelo's Lab at Univ of Pavia (Italy)
// Amrita School of Biotechnology, Amritapuri
// Clappana P.O., Kollam, 690 525, Kerala, India.
// http://research.amrita.edu/compneuro
// Email:shyam@amrita.edu

 
/* Model published as [Diwakar et al., 2011, manuscript accepted, PLoS ONE]
Shyam Diwakar, Paola Lombardo, Sergio Solinas, Giovanni Naldi, Egidio D'Angelo. "Local field potential modeling predicts dense activation in cerebellar granule cells clusters under LTP and LTD control", PLoS ONE, 2011.
*/



//A Panel for Channels and someof their controls

objref panel
panel = new VBox()

//new stuff 10 May 2005
Nag = Granule[0].soma.gnabar_GRC_NA
Kvg = Granule[0].soma.gkbar_GRC_KV
Kmg = Granule[0].soma.gkbar_GRC_KM
glL = 5.68e-5
ell = -16.5

ndend = 4
nsg = 5
naxon = 30
ncomp = 1+(4*ndend)+nsg+naxon

Rappaxon = ((9.76*9.76)/(naxon*Granule[0].axon[0].L*Granule[0].axon[0].diam))
Granule[0].soma.gnabar_GRC_NA = 0
Granule[0].soma.gkbar_GRC_KV = 0


KirGmax=0.0009  //Standard reference value - Kir
KaGmax=0.0032  //Standard reference value -Ka
CaGmax=0.00046 //Standard reference value - Ca
KCaGmax=0.003 //Standard reference value - KCa
beta=0.6 //Standard reference value - removal rate

inicon=0.001 //Standard reference value - Initial condition

//Configuration mode flags for state/compartment selection for ion channel distribution 
Camode1 =0
Camode2 =0
Camode3 =0
Camode4 =1
CamodeS =0
KCamode1 =0
KCamode2 =0
KCamode3 =0
KCamode4 =1
KCamodeS=0
Kirmode1 =1
Kirmode2 =1
Kirmode3 =0
Kirmode4 =0
KirmodeS =0
Kamode1 =0
Kamode2 =0
Kamode3 =0
Kamode4 =0
KamodeS =1

//Compartment area estimation
SomaArea=Granule[0].soma.L*Granule[0].soma.diam*PI
Dend12Area=Granule[0].dend_1[0].L*Granule[0].dend_1[0].diam*PI
Dend34Area=Granule[0].dend_4[0].L*Granule[0].dend_4[0].diam*PI
SomascArea=PI*9.76*9.76

//Scale factors for compartaments
RappSomaDend12=SomascArea/(4*Dend12Area)
RappSomaDend34=SomascArea/(4*Dend34Area)
RappSomaNew=SomascArea/SomaArea
RappSomahill=SomascArea/(3.75*PI) 
RappAH = 3.75/(naxon*Granule[0].axon[0].L*Granule[0].axon[0].diam)


gG = Granule[0].soma.ggaba_GRC_LKG2

// Functions to identify multiplecompartments - ignore description 
proc alpKCaM() {

	alpKCa = ($1==1)+($2==1)+($3==1)+($4==1)+($5==1)
}

proc alpCaM() {
	alpCa = ($1==1)+($2==1)+($3==1)+($4==1)+($5==1)
}

proc alpKaM() {
	alpKa = ($1==1)+($2==1)+($3==1)+($4==1)+($5==1)
}

proc alpKirM() {
	alpKir = ($1==1)+($2==1)+($3==1)+($4==1)+($5==1)
}

gamma = 0.5  //Percentage of NA/Kv in axon-hillock

//For specific dendrite, axon and Hillock manipulations
NagH = Nag
KvgH = Kvg
NagA = Nag
KvgA = Kvg
KCaD = KCaGmax
CaD = CaGmax

DendFact=1 //default morphology scaling ratio

KaRapp = KaGmax
KirRapp = KirGmax
CaRapp = CaGmax
KCaRapp = KCaGmax

//For Dendritic Morphology Scaling - Unused
proc DendGeomFact(){
	
	for (i=0;i<4;i=i+1) {
		Granule[0].dend_1[i].diam=0.75/DendFact
		Granule[0].dend_2[i].diam=0.75/DendFact
		Granule[0].dend_3[i].diam=0.75/DendFact
		Granule[0].dend_4[i].diam=0.75/DendFact
		Granule[0].dend_1[i].L=5*DendFact
		Granule[0].dend_2[i].L=5*DendFact
		Granule[0].dend_3[i].L=2.5*DendFact
		Granule[0].dend_4[i].L=2.5*DendFact
	}
	//print "L1= ",Granule[0].dend_1[0].L," L2= ",Granule[0].dend_2[0].L," L3= ",Granule[0].dend_3[0].L," L4= ",Granule[0].dend_4[0].L
	//print "D1=D2=D3=D4= ",Granule[0].dend_1[0].diam
}

//Updating leakage current
proc glUpdate() {
	Granule[0].soma.gl_GRC_LKG1 = glL*(RappSomaNew)*(2/3)
	Granule[0].soma.el_GRC_LKG1 = ell
	for(i=0;i<5;i=i+1) {
		Granule[0].hillock[i].gl_GRC_LKG1=glL*(RappSomahill)*(1/15)
		Granule[0].hillock[i].el_GRC_LKG1=ell
	}
	for(i=0;i<naxon;i=i+1) {
		Granule[0].axon[i].gl_GRC_LKG1=glL*(Rappaxon)*(1/30)
		Granule[0].axon[i].el_GRC_LKG1 =ell
	}
	for(i=0;i<4;i=i+1) {
		Granule[0].dend_1[i].gl_GRC_LKG1=glL*(RappSomaDend12)*(1/16)
		Granule[0].dend_2[i].gl_GRC_LKG1=glL*(RappSomaDend12)*(1/16)
		Granule[0].dend_3[i].gl_GRC_LKG1=glL*(RappSomaDend34)*(1/16)
		Granule[0].dend_4[i].gl_GRC_LKG1=glL*(RappSomaDend34)*(1/16)
		Granule[0].dend_1[i].el_GRC_LKG1=ell
		Granule[0].dend_2[i].el_GRC_LKG1=ell
		Granule[0].dend_3[i].el_GRC_LKG1=ell
		Granule[0].dend_4[i].el_GRC_LKG1=ell
	}
}

proc gGUpdate() {
	Granule[0].soma.ggaba_GRC_LKG2 = 0
	for(i=0;i<4;i=i+1) {
		Granule[0].dend_1[i].ggaba_GRC_LKG2=gG*(1/ndend)*(RappSomaDend12)
		Granule[0].dend_2[i].ggaba_GRC_LKG2=gG*(1/ndend)*(RappSomaDend12)
		Granule[0].dend_3[i].ggaba_GRC_LKG2=gG*(1/ndend)*(RappSomaDend34)
		Granule[0].dend_4[i].ggaba_GRC_LKG2=gG*(1/ndend)*(RappSomaDend34)
	}
}


//Updating Calcium buffer removal rate 
proc UpdateBeta() {
             //print "Updating Removal Rate of Calcium ----"
             for (i=0;i<4;i=i+1) {
                  Granule[0].dend_1[i].beta_GRC_CALC = beta
                  //print "dend_1 [",i,"]beta =",Granule[0].dend_1[i].beta_GRC_CALC,"                |"
                  Granule[0].dend_2[i].beta_GRC_CALC = beta
                  //print "dend_2 [",i,"]beta =",Granule[0].dend_2[i].beta_GRC_CALC,"                |"
                  Granule[0].dend_3[i].beta_GRC_CALC = beta
                  //print "dend_3 [",i,"]beta =",Granule[0].dend_3[i].beta_GRC_CALC,"                |"
                  Granule[0].dend_4[i].beta_GRC_CALC = beta
                  //print "dend_4 [",i,"]beta =",Granule[0].dend_4[i].beta_GRC_CALC,"                |"

             }
             //print "Update Complete ---------------------"
}
//Updating Calcium shell thickness
proc UpdateShelld() {
             //print "Updating Calcium Shell Thickness----"
             /*for (i=0;i<4;i=i+1) {
                  Granule[0].dend_1[i].d_GRC_CALC = shell*RappSomaDend12
                  print "dend_1 [",i,"]d =",Granule[0].dend_1[i].d_GRC_CALC,"                |"
                  Granule[0].dend_2[i].d_GRC_CALC = shell*RappSomaDend12
                  print "dend_2 [",i,"]d =",Granule[0].dend_2[i].d_GRC_CALC,"                |"
                  Granule[0].dend_3[i].d_GRC_CALC = shell*RappSomaDend34
                  print "dend_3 [",i,"]d =",Granule[0].dend_3[i].d_GRC_CALC,"                |"
                  Granule[0].dend_4[i].d_GRC_CALC = shell*RappSomaDend34
                  print "dend_4 [",i,"]d =",Granule[0].dend_4[i].d_GRC_CALC,"                |"

             }*/
	     //print "Improbable Update Terminating ---------------------"
}

//Updating initial Concentration 
proc UpdateInicon() {
             //print "Updating Initial Ca ion Conc--------"
             for (i=0;i<4;i=i+1) {
                  Granule[0].dend_1[i].cai0_GRC_CALC = inicon
                  //print "dend_1 [",i,"]cai0 =",Granule[0].dend_1[i].cai0_GRC_CALC,"                |"
                  Granule[0].dend_2[i].cai0_GRC_CALC = inicon
                  //print "dend_2 [",i,"]cai0 =",Granule[0].dend_2[i].cai0_GRC_CALC,"                |"
                  Granule[0].dend_3[i].cai0_GRC_CALC = inicon
                  //print "dend_3 [",i,"]cai0 =",Granule[0].dend_3[i].cai0_GRC_CALC,"                |"
                  Granule[0].dend_4[i].cai0_GRC_CALC = inicon
                  //print "dend_4 [",i,"]cai0 =",Granule[0].dend_4[i].cai0_GRC_CALC,"                |"

             }
             //print "Update Complete ---------------------"
}

//Reset functions: resets to old state when checkbox is unticked 
proc resetgs() {
		Granule[0].soma.gkbar_GRC_KA = 0//KaRapp
		Granule[0].soma.gcabar_GRC_CA = 0
		Granule[0].soma.gkbar_GRC_KIR = 0//KirRapp
		Granule[0].soma.gkbar_GRC_KCA = 0
}
	
proc resetgd1() {
		for(i=0;i<4;i=i+1) {
			Granule[0].dend_1[i].gkbar_GRC_KA = 0
			Granule[0].dend_1[i].gcabar_GRC_CA = 0
			Granule[0].dend_1[i].gkbar_GRC_KIR = 0
			Granule[0].dend_1[i].gkbar_GRC_KCA = 0
		}
}
proc resetgd2() {	
		for(i=0;i<4;i=i+1) {
			Granule[0].dend_2[i].gkbar_GRC_KA = 0
			Granule[0].dend_2[i].gcabar_GRC_CA = 0
			Granule[0].dend_2[i].gkbar_GRC_KIR = 0
			Granule[0].dend_2[i].gkbar_GRC_KCA = 0
		}
}
proc resetgd3() {
		for(i=0;i<4;i=i+1) {
			Granule[0].dend_3[i].gkbar_GRC_KA = 0
			Granule[0].dend_3[i].gcabar_GRC_CA = 0
			Granule[0].dend_3[i].gkbar_GRC_KIR = 0
			Granule[0].dend_3[i].gkbar_GRC_KCA = 0
		}
}
proc resetgd4() {
		for(i=0;i<4;i=i+1) {
			Granule[0].dend_4[i].gkbar_GRC_KA = 0
			Granule[0].dend_4[i].gcabar_GRC_CA = 0 
			Granule[0].dend_4[i].gkbar_GRC_KIR = 0
			Granule[0].dend_4[i].gkbar_GRC_KCA = 0 
		}
}	
proc resetg() {
	if($1==0) {
		resetgs()
	}
	if($2==0) {
		resetgd1()
	}
	if($3==0) {
		resetgd2()
	}
	if($4==0) {
		resetgd3()
	}
	if($5==0) {
		resetgd4()
	}
}

proc KaU(){
	//print "Refresh Ka"
	alpKaM($1,$2,$3,$4,$5)
	if(alpKa>=1) {
		for (i=0;i<4;i=i+1) {
			Granule[0].soma.gkbar_GRC_KA=KaGmax*(1/alpKa)*RappSomaNew*$1
			Granule[0].dend_1[i].gkbar_GRC_KA=KaGmax*RappSomaDend12*(1/alpKa)*$2
			Granule[0].dend_2[i].gkbar_GRC_KA=KaGmax*RappSomaDend12*(1/alpKa)*$3
			Granule[0].dend_3[i].gkbar_GRC_KA=KaGmax*RappSomaDend34*(1/alpKa)*$4
			Granule[0].dend_4[i].gkbar_GRC_KA=KaGmax*RappSomaDend34*(1/alpKa)*$5
		}
	}
}

proc CaU(){
	//print "Refresh Ca"
	alpCaM($1,$2,$3,$4,$5)
	if(alpCa>=1) {
		for (i=0;i<4;i=i+1) {
			Granule[0].soma.gcabar_GRC_CA=CaGmax*(1/alpKa)*RappSomaNew*$1
			Granule[0].dend_1[i].gcabar_GRC_CA=CaD*RappSomaDend12*(1/alpCa)*$2
			Granule[0].dend_2[i].gcabar_GRC_CA=CaD*RappSomaDend12*(1/alpCa)*$3
			Granule[0].dend_3[i].gcabar_GRC_CA=CaD*RappSomaDend34*(1/alpCa)*$4
			Granule[0].dend_4[i].gcabar_GRC_CA=CaD*RappSomaDend34*(1/alpCa)*$5		
		}
	}
}
proc KCaU(){
	//print "Refresh KCa"
	//if($1==1) ->addstuff to modify shell d in soma
	alpKCaM($1,$2,$3,$4,$5)
	if(alpKCa>=1) {
		for (i=0;i<4;i=i+1) {
			Granule[0].soma.gkbar_GRC_KCA=KCaD*(1/alpKCa)*RappSomaNew*$1
			Granule[0].dend_1[i].gkbar_GRC_KCA=KCaD*RappSomaDend12*(1/alpKCa)*$2
			Granule[0].dend_2[i].gkbar_GRC_KCA=KCaD*RappSomaDend12*(1/alpKCa)*$3
			Granule[0].dend_3[i].gkbar_GRC_KCA=KCaD*RappSomaDend34*(1/alpKCa)*$4
			Granule[0].dend_4[i].gkbar_GRC_KCA=KCaD*RappSomaDend34*(1/alpKCa)*$5		
		}
	}
}

proc KirU(){
	//print "Refresh Kir"
	alpKirM($1,$2,$3,$4,$5)
	if(alpKir>=1) {
		for (i=0;i<4;i=i+1) {
			Granule[0].soma.gkbar_GRC_KIR=KirGmax*(1/alpKir)*RappSomaNew*$1
			Granule[0].dend_1[i].gkbar_GRC_KIR=KirGmax*RappSomaDend12*(1/alpKir)*$2
			Granule[0].dend_2[i].gkbar_GRC_KIR=KirGmax*RappSomaDend12*(1/alpKir)*$3
			Granule[0].dend_3[i].gkbar_GRC_KIR=KirGmax*RappSomaDend34*(1/alpKir)*$4
			Granule[0].dend_4[i].gkbar_GRC_KIR=KirGmax*RappSomaDend34*(1/alpKir)*$5		
		}
	}
}

//for Na in axon/hillock

proc NaAUpdate() {
	//print "Updating Na in axon"
	for(i=0;i<naxon;i=i+1) {
		access Granule[0].axon[i]
		Granule[0].axon[i].gnabar_GRC_NA = NagA*(1-gamma)*Rappaxon-0.00232//*(1/naxon)//axon n hillock
		Granule[0].axon[i].gkbar_GRC_KV = KvgA*(1-gamma)*Rappaxon-0.00232//*(1/naxon)
	}
}
proc NaHUpdate() {
	//print "Updating Na in hillock"
	for(i=0;i<5;i=i+1) {
		access Granule[0].hillock[i]
		Granule[0].hillock[i].gnabar_GRC_NA = NagH*gamma*RappSomahill-0.00243
		Granule[0].hillock[i].gkbar_GRC_KV = KvgH*gamma*RappSomahill-0.00243
	}
}

UpdateBeta()
UpdateInicon()

betad = 0.8
glUpdate()
//For axon and Hill
	NagH = Nag
	KvgH = Kvg
	NagA = Nag
	KvgA = Kvg
	KcaB = KCaGmax
	CaB = CaGmax


proc UpdateHA() {
	//print "Updating Hillock-Axon Conductances"
	
	glUpdate()
	NaHUpdate()
	NaAUpdate()
}
	
//Activate default set
resetg(0,0,0,0,0)
NaAUpdate()
NaHUpdate()
glUpdate()
gGUpdate()
KaU(1,0,0,0,0)
CaU(0,0,0,0,1)
KCaU(0,0,0,0,1)
KirU(1,0,0,0,0)

//Dendritic params
KcaDe = KCaGmax
CaDe = CaGmax

proc CaDup() {
	//print "Updating Ca/KCa in dendrite(s)"
	for(i=0;i<ndend;i=i+1) {
		access Granule[0].dend_4[i]
		Granule[0].dend_4[i].gcabar_GRC_CA = CaDe*RappSomaDend34
		Granule[0].dend_4[i].gkbar_GRC_KCA = KcaDe*RappSomaDend34

	}
}

CaDup()
glUpdate()
gGUpdate()
inicon=0.00225
UpdateInicon()
beta=0.6
UpdateBeta()
NagH=0.019  //for spike amplitude
NaAUpdate()
NaHUpdate()

// Setting low leak in passive compartments
Granule[0].branch0.gl_GRC_LKG1=0.000000005
Granule[0].branch1.gl_GRC_LKG1=0.000000005
Granule[0].branch2.gl_GRC_LKG1=0.000000005
Granule[0].branch3.gl_GRC_LKG1=0.000000005
/*
//Ion channel properties gmax panel
panel.intercept(1)
xpanel("1")
	xlabel("***Na/Kv parameters***")
	xvalue("H/A ratio","gamma", 1,"UpdateHA()", 0, 0 )
	xlabel("Parameters of hillock compartments")
	xvalue("gNabar","NagH", 1,"NaHUpdate()", 0, 0 )
	xvalue("gKvbar","KvgH", 1,"NaHUpdate()", 0, 0 )
	xlabel("Parameters of axon compartments")
	xvalue("gNabar","NagA", 1,"NaAUpdate()", 0, 0 )
	xvalue("gKvbar","KvgA", 1,"NaAUpdate()", 0, 0 )	
	xlabel("***Calcium params***")	
	xvalue("gCabar","CaDe", 1,"CaDup()", 0, 0 )
	xvalue("gKCabar","KcaDe", 1,"CaDup()", 0, 0 )
	xlabel("***Other K+ params***")
	xvalue("Ka-gmax","KaGmax", 1,"KaU(1,0,0,0,0)", 0, 0 )
	xvalue("Kir-gmax","KirGmax", 1,"KirU(1,0,0,0,0)", 0, 0 )
	xlabel("***Leakage params***")
	xvalue("Lkg1","glL", 1,"glUpdate()", 0, 0 )
	xvalue("Lkg2","gG", 1,"gGUpdate()", 0, 0)
xpanel()
panel.intercept(0)
panel.map("Channels-n-Controls")
*/
UpdateHA()


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