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 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 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.
*/

objref AmpaSchemeMenu,mfpanel,ifpanel


Ampa_Gmax=600
Gaba_gmax = Granule[0].synG[0].gmax
Nmda_Gmax2_ES=16000
GmaxCOD=Ampa_Gmax
GmaxNES = Nmda_Gmax2_ES
GmaxGaba = 2500
Ampa_G = 600
Nmda_G = Granule[0].synNS[0].gmax
NumSin=0
NumISin=0

/*
xpanel("Control Panel")
	xlabel(" ===== Command Panel ===== ")
	//xbutton("GrC Soma","GrcPanel()")
	xbutton("Postsynaptic","Synapses()")
	xbutton("Exc Presyn Parameters","PresynParam()")
	xbutton("Inh Presyn Parameters","PresynGParam()")
	xmenu("Mossy Fibers")
		     xradiobutton("Homogeneous mf","HomogeneousMf()")	
		     //xradiobutton("Heterogeneous mf","HeterogeneousMF()")
		     xradiobutton("Mossy traces","MossyFiberTraces()")
	xmenu()
	xmenu("Inhibitory Synapses")
		     xradiobutton("Homogeneous if","HomogeneousIf()")	
		     //xradiobutton("Heterogeneous if","HeterogeneousIF()")
		     xradiobutton("Inhib Synaptic fiber traces","InhibFiberTraces()")
	xmenu()
xpanel()
*/

// *************************** GrC parameters ***********************//

objref GRCparams

proc GrcPanel(){
	GRCparams = new VBox()
	GRCparams.intercept(1)   
	xpanel("1")  
	xlabel("Soma Properties")
	xvalue("gNabar","Granule[0].soma.gnabar_GRC_NA", 1,"", 0, 0 )
	xvalue("gKVbar","Granule[0].soma.gkbar_GRC_KV", 1,"", 0, 0 )
	xvalue("gKAbar","Granule[0].soma.gkbar_GRC_KA", 1,"", 0, 0 )
	xvalue("gKirbar","Granule[0].soma.gkbar_GRC_KIR", 1,"", 0, 0 )
	xvalue("gKCabar","Granule[0].soma.gkbar_GRC_KCA", 1,"", 0, 0 )
	xvalue("gCaHVAbar","Granule[0].soma.gcabar_GRC_CA", 1,"", 0, 0 )
	xvalue("gKSlowbar","Granule[0].soma.gkbar_GRC_KM", 1,"", 0, 0 )
	xvalue("gLeakage","Granule[0].soma.gl_GRC_LKG1", 1,"", 0, 0 )
	xvalue("gGabaA","Granule[0].soma.ggaba_GRC_LKG2", 1,"", 0, 0 )
	xpanel()
	xpanel("2")
	xlabel("Calcium parameters")
	xvalue("Shell thickness","Granule[0].soma.d_GRC_CALC", 1,"", 0, 0 )
	xvalue("Initial concentration","Granule[0].soma.cai0_GRC_CALC", 1,"", 0, 0 )
	xvalue("Removal rate","Granule[0].soma.beta_GRC_CALC", 1,"", 0, 0 ) 
	xpanel()
	GRCparams.intercept(0)
	GRCparams.map("Granule Cell Parameters")
}


proc UpdateAmpaNmda() {
	Ampa_Gmax = Ampa_G
	Nmda_Gmax2_ES = Nmda_G
	for(i=0;i<8;i=i+1) {
		AmpaCOD[i].gmax=Ampa_Gmax
		NMDAS[i].gmax=Nmda_Gmax2_ES
	}
	for(i=0;i<4;i=i+1) {
		Granule[0].synA[i].gmax=Ampa_Gmax
		Granule[0].synNS[i].gmax=Nmda_Gmax2_ES
	}
}

proc UpdateGaba() {
	for(i=0;i<8;i=i+1) {
		GRC_GABA[i].gmax=GmaxGaba
		GRC_GABA[i].U=0.34
	}
	for(i=0;i<4;i=i+1) {
		Granule[0].synG[i].gmax=GmaxGaba
	}
}

// ************************************* Presynpatic-Exc and Inh *********************************


proc PresynParam(){
	Tau_rec=Granule[0].synA[0].tau_rec
	Tau_facil=Granule[0].synA[0].tau_facil
	Tau_1=Granule[0].synA[0].tau_1
	U=Granule[0].synA[0].U
	xpanel("PRESYNAPTIC PARAMETERS")   
		xlabel("Exc Presynaptic parameters")
		xvalue("T_transition (ms)","Tau_1", 1,"UpDatePre()", 0, 0 )
		xvalue("T_recovery (ms)","Tau_rec", 1,"UpDatePre()", 0, 0 )
		xvalue("T_facilita (ms)","Tau_facil", 1,"UpDatePre()", 0, 0 )
		xvalue("Release probability","U", 1,"UpDatePre()", 0, 0 )
	xpanel()
}

proc UpDatePre(){
	for (i=0;i<NumSin+4;i=i+1) {
		AmpaCOD[i].tau_rec=Tau_rec
		AmpaCOD[i].tau_facil=Tau_facil
		AmpaCOD[i].tau_1=Tau_1
		AmpaCOD[i].U=U
		

		NMDAS[i].tau_rec=Tau_rec
		NMDAS[i].tau_facil=Tau_facil
		NMDAS[i].tau_1=Tau_1
		NMDAS[i].U=U

	}
}

proc PresynGParam(){
	Tau_rec_if=Granule[0].synG[0].tau_rec
	Tau_facil_if=Granule[0].synG[0].tau_facil
	Tau_1_if=Granule[0].synG[0].tau_1
	U_if=Granule[0].synG[0].U
	xpanel("If PRESYNAPTIC PARAMETERS")   
		xvalue("T_transition (ms)","Tau_1_if", 1,"UpDateGPre()", 0, 0 )
		xvalue("T_recovery (ms)","Tau_rec_if", 1,"UpDateGPre()", 0, 0 )
		xvalue("T_facilita (ms)","Tau_facil_if", 1,"UpDateGPre()", 0, 0 )
		xvalue("Release probability","U_if", 1,"UpDateGPre()", 0, 0 )
	xpanel()
}

proc UpDateGPre(){
	for (ifn=0;ifn<NumISin+4;ifn=ifn+1) {
		GRC_GABA[ifn].tau_rec=Tau_rec_if
		GRC_GABA[ifn].tau_facil=Tau_facil_if
		GRC_GABA[ifn].tau_1=Tau_1_if
		GRC_GABA[ifn].U=U_if
		
	}
}

// ******************************** Mossy Fiber  ************************************// 

objref grafici[8]
ngraph = 0

proc addgraph() { local ii	
	ngraph = ngraph+1
	ii = ngraph-1
	grafici[ii] = new Graph(0)
	grafici[ii].size(0,tstop,$2,$3)
	grafici[ii].view(0,0,150,48,0,48,150,48)
	grafici[ii].xaxis()
	grafici[ii].yaxis()
	grafici[ii].addvar($s1,1,0)
	grafici[ii].save_name("graphList[0].")
	graphList[0].append(grafici[ii])
	grafici[ii].exec_menu("View = plot")
	grafici[ii].flush()
}


proc MossyFiberTraces(){
	mfpanel = new VBox()
	mfpanel.intercept(1)
	xpanel("Mossy fibers traces")   
		addgraph("Mossy[0].y",0,2)
		addgraph("Mossy[1].y",0,2)
		addgraph("Mossy[2].y",0,2)
		addgraph("Mossy[3].y",0,2)
	xpanel()
	mfpanel.intercept(0)
	mfpanel.map("Mossy Fiber traces")
}

NumSin = 0
NumISin = 0

proc HomogeneousMf(){
	xpanel("MOSSY PARAMETERS")   
	xlabel("Homogeneous mossy fibers")
	InSpike=10
	InBurst=1e5
	NumSpikes=1
	StartIn=20
	EndIn=1e10
	Rumore=0
	NumSin=0
	delay=Mossy[0].delay		
	UpDateMossyO()
	xvalue("Number of synapses ","NumSin", 1,"UpDateMossyO()", 0, 0 )
	xvalue("Interspike interval (ms)","InSpike", 1,"UpDateMossyO()", 0, 0 )
	xvalue("Interburst interval (ms)","InBurst", 1,"UpDateMossyO()", 0, 0 )
	xvalue("Spikes per burst        ","NumSpikes", 1,"UpDateMossyO()", 0, 0 )
	xvalue("Begin of the Input (ms)","StartIn", 1,"UpDateMossyO()", 0, 0 )
	xvalue("End of the Input (ms)","EndIn", 1,"UpDateMossyO()", 0, 0 )
	xpanel()
}

proc HeterogeneousMF(){
	xpanel("Mossy heterogeneus")   
	xlabel("Heterogeneous mossy fibers")

	xvalue("Interspike interval (ms)","Mossy[0].fast_invl", 1,"", 0, 0 )
	xvalue("Interburst interval (ms)","Mossy[0].slow_invl", 1,"", 0, 0 )
	xvalue("Spikes per burst","Mossy[0].burst_len", 1,"", 0, 0 )
	xvalue("Begin of the Input (ms)","Mossy[0].start", 1,"", 0, 0 )
	xvalue("End of the Input (ms)","Mossy[0].end", 1,"", 0, 0 )

	xlabel("Mossy 2")
	xvalue("Interspike interval (ms)","Mossy[1].fast_invl", 1,"", 0, 0 )
	xvalue("Interburst interval (ms)","Mossy[1].slow_invl", 1,"", 0, 0 )
	xvalue("Spikes per burst","Mossy[1].burst_len", 1,"", 0, 0 )
	xvalue("Begin of the Input (ms)","Mossy[1].start", 1,"", 0, 0 )
	xvalue("End of the Input (ms)","Mossy[1].end", 1,"", 0, 0 )

	xlabel("Mossy 3")
	xvalue("Interspike interval (ms)","Mossy[2].fast_invl", 1,"", 0, 0 )
	xvalue("Interburst interval (ms)","Mossy[2].slow_invl", 1,"", 0, 0 )
	xvalue("Spikes per burst","Mossy[2].burst_len", 1,"", 0, 0 )
	xvalue("Begin of the Input (ms)","Mossy[2].start", 1,"", 0, 0 )
	xvalue("End of the Input (ms)","Mossy[2].end", 1,"", 0, 0 )

	xlabel("Mossy 4")
	xvalue("Interspike interval (ms)","Mossy[3].fast_invl", 1,"", 0, 0 )
	xvalue("Interburst interval (ms)","Mossy[3].slow_invl", 1,"", 0, 0 )
	xvalue("Spikes per burst","Mossy[3].burst_len", 1,"", 0, 0 )
	xvalue("Begin of the Input (ms)","Mossy[3].start", 1,"", 0, 0 )
	xvalue("End of the Input (ms)","Mossy[3].end", 1,"", 0, 0 )

	xpanel()
}

proc UpDateMossyO(){
	print "UpDating Mossy parameters"
	for(i=0;i<4;i=i+1) {	
		Mossy[i].fast_invl=InSpike
		Mossy[i].slow_invl=InBurst
		Mossy[i].burst_len=NumSpikes
		Mossy[i].start=StartIn
		Mossy[i].noise=Rumore
		Mossy[i].delay=delay
		if (i<NumSin) {
			Mossy[i].end=EndIn
		} else {
			Mossy[i].end=0
		}
	}
}
 

// *************Inhibitory Synaptic Fiber**************************// 

objref graffz[8]
ngraff = 0

proc addgraph() { local ix	
	ngraff = ngraff+1
	ix = ngraff-1
	graffz[ix] = new Graph(0)
	graffz[ix].size(0,tstop,$2,$3)
	graffz[ix].view(0,0,150,48,0,48,150,48)
	graffz[ix].xaxis()
	graffz[ix].yaxis()
	graffz[ix].addvar($s1,1,0)
	graffz[ix].save_name("graphList[1].")
	graphList[1].append(graffz[ix])
	graffz[ix].exec_menu("View = plot")
	graffz[ix].flush()
}


proc InhibFiberTraces(){
	ifpanel = new VBox()
	ifpanel.intercept(1)
	xpanel("Inhib fibers traces")   
		addgraph("Inhib[0].y",0,2)
		addgraph("Inhib[1].y",0,2)
		addgraph("Inhib[2].y",0,2)
		addgraph("Inhib[3].y",0,2)
	xpanel()
	ifpanel.intercept(0)
	ifpanel.map("Inhibitory synaptic Fiber traces")
}

proc HomogeneousIf(){
	xpanel("Inhib PARAMETERS")   
	xlabel("Homogeneous inhib fibers")
	IInSpike=10
	IInBurst=1e10
	INumSpikes=1
	IStartIn=24
	IEndIn=1e10
	IRumore=Inhib[0].noise	
	NumISin=0
	Idelay=Inhib[0].delay	
	UpDateInhibO()
	xvalue("Number of synapses ","NumISin", 1,"UpDateInhibO()", 0, 0 )
	xvalue("Interspike interval (ms)","IInSpike", 1,"UpDateInhibO()", 0, 0 )
	xvalue("Interburst interval (ms)","IInBurst", 1,"UpDateInhibO()", 0, 0 )
	xvalue("Spikes per burst        ","INumSpikes", 1,"UpDateInhibO()", 0, 0 )
	xvalue("Begin of the Input (ms)","IStartIn", 1,"UpDateInhibO()", 0, 0 )
	xvalue("End of the Input (ms)","IEndIn", 1,"UpDateInhibO()", 0, 0 )
	xpanel()
}

proc HeterogeneousIF(){
	xpanel("Inhib heterogeneus")   
	xlabel("Heterogeneous inhib fibers")

	xvalue("Interspike interval (ms)","Inhib[0].fast_invl", 1,"", 0, 0 )
	xvalue("Interburst interval (ms)","Inhib[0].slow_invl", 1,"", 0, 0 )
	xvalue("Spikes per burst","Inhib[0].burst_len", 1,"", 0, 0 )
	xvalue("Begin of the Input (ms)","Inhib[0].start", 1,"", 0, 0 )
	xvalue("End of the Input (ms)","Inhib[0].end", 1,"", 0, 0 )
	
	xlabel("Inhib 2")
	xvalue("Interspike interval (ms)","Inhib[1].fast_invl", 1,"", 0, 0 )
	xvalue("Interburst interval (ms)","Inhib[1].slow_invl", 1,"", 0, 0 )
	xvalue("Spikes per burst","Inhib[1].burst_len", 1,"", 0, 0 )
	xvalue("Begin of the Input (ms)","Inhib[1].start", 1,"", 0, 0 )
	xvalue("End of the Input (ms)","Inhib[1].end", 1,"", 0, 0 )
	
	xlabel("Inhib 3")
	xvalue("Interspike interval (ms)","Inhib[2].fast_invl", 1,"", 0, 0 )
	xvalue("Interburst interval (ms)","Inhib[2].slow_invl", 1,"", 0, 0 )
	xvalue("Spikes per burst","Inhib[2].burst_len", 1,"", 0, 0 )
	xvalue("Begin of the Input (ms)","Inhib[2].start", 1,"", 0, 0 )
	xvalue("End of the Input (ms)","Inhib[2].end", 1,"", 0, 0 )
	
	xlabel("Inhib 4")
	xvalue("Interspike interval (ms)","Inhib[3].fast_invl", 1,"", 0, 0 )
	xvalue("Interburst interval (ms)","Inhib[3].slow_invl", 1,"", 0, 0 )
	xvalue("Spikes per burst","Inhib[3].burst_len", 1,"", 0, 0 )
	xvalue("Begin of the Input (ms)","Inhib[3].start", 1,"", 0, 0 )
	xvalue("End of the Input (ms)","Inhib[3].end", 1,"", 0, 0 )
	
	xpanel()
}

proc UpDateInhibO(){
	print "UpDating Inhib-Synaptic parameters"
	for(kk=0;kk<4;kk=kk+1) {	
		Inhib[kk].fast_invl=IInSpike
		Inhib[kk].slow_invl=IInBurst
		Inhib[kk].burst_len=INumSpikes
		Inhib[kk].start=IStartIn
		Inhib[kk].noise=IRumore
		Inhib[kk].delay=Idelay
		if (kk<NumISin) {
			Inhib[kk].end=IEndIn
		} else {
			Inhib[kk].end=0
		}
	}
}

proc Synapses() {
		xpanel("Postsynaptic params")
		xvalue("AMPA Gmax (pS)","Ampa_G", 1,"UpdateAmpaNmda()", 0, 0 )
		xvalue("NMDA Gmax (pS)","Nmda_G", 1,"UpdateAmpaNmda()", 0, 0 )
		xvalue("GABA Gmax","Gaba_gmax",1,"UpdateGaba()",0,0)
		xpanel()
}


//Set up initials
UpDateMossyO()
UpDateInhibO()
UpdateGaba()
UpdateAmpaNmda()

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