Layer 5 Pyramidal Neuron (Shai et al., 2015)

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Accession:180373
This work contains a NEURON model for a layer 5 pyramidal neuron (based on Hay et al., 2011) with distributed groups of synapses across the basal and tuft dendrites. The results of that simulation are used to fit a phenomenological model, which is also included in this file.
Reference:
1 . Shai AS, Anastassiou CA, Larkum ME, Koch C (2015) Physiology of layer 5 pyramidal neurons in mouse primary visual cortex: coincidence detection through bursting. PLoS Comput Biol 11:e1004090 [PubMed]
Model Information (Click on a link to find other models with that property)
Model Type: Neuron or other electrically excitable cell;
Brain Region(s)/Organism:
Cell Type(s): Neocortex V1 L6 pyramidal corticothalamic GLU cell;
Channel(s):
Gap Junctions:
Receptor(s):
Gene(s):
Transmitter(s): Glutamate;
Simulation Environment: NEURON; MATLAB;
Model Concept(s): Dendritic Action Potentials; Active Dendrites;
Implementer(s):
Search NeuronDB for information about:  Neocortex V1 L6 pyramidal corticothalamic GLU cell; Glutamate;
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ShaiEtAl2015
simulationcode
Ca_HVA.mod *
Ca_LVAst.mod *
CaDynamics_E2.mod *
epsp.mod *
glutamate.mod *
Ih.mod *
Im.mod *
K_Pst.mod *
K_Tst.mod *
Nap_Et2.mod *
NaTa_t.mod *
NaTs2_t.mod *
SK_E2.mod *
SKv3_1.mod *
AccessoryFunctions.hoc
DefineSections.hoc
distSynsCluster.hoc
distSynsCluster2.hoc
distSynsUniform.hoc
distSynsUniform2.hoc
distSynsUniformAlpha.hoc
Fig3A.hoc
neuron203.os
O_vd_C.dat
O_vs_C.dat
Run.hoc
varycolor.m
                            
// This model/simulation file has been modified by Adam Shai, 2015

// The original file comes from: Etay Hay, 2011
//  Models of Neocortical Layer 5b Pyramidal Cells Capturing a Wide Range of
//  Dendritic and Perisomatic Active Properties
//  (Hay et al., PLoS Computational Biology, 2011) 

// Here we modify the simulation to randomly distribute synapses along the 
// dendrites of the cell, as described in (Shai et al., PLoS Computational
// Biology, 2015). This takes a few days to run for each condition. Outputs
// of these simulations are given in the data folder, as well as the matlab
// file to plot them.


//====================== General files and tools =====================
load_file("nrngui.hoc")

//====================== cvode =======================================
objref cvode

cvode = new CVode()
cvode.active(1)

//=================== creating cell object ===========================
load_file("import3d.hoc")
objref L5PC

strdef morphology_file
morphology_file = "../morphologies/cell1.asc"

load_file("../models/L5PCbiophys5.hoc")
load_file("../models/L5PCtemplate.hoc")
L5PC = new L5PCtemplate(morphology_file)

//=================== load files for distributing synapses ===========================
xopen("distSynsUniform.hoc")
xopen("distSynsCluster2.hoc")

//=================== create a Section List for the apical tufts ===========================
forall{ nseg = L/0.25+2 } // changing this can make the simulation run faster but can change the results
objref tuftList
tuftList = new SectionList()
access L5PC.apic[36]
tuftList.subtree()
tuftList.remove()

//=================== Plot the neuron ===========================
objref shapeG
shapeG = new Shape()
//shapeG.color_list(L5PC.basal,3) //red
//shapeG.color_list(tuftList,5) //blue
//shapeG.color_list(basalList,5) // orange
//shapeG.color_list(axonList,0)
//shapeG.color_list(allSections,1)


//=================== create a Section List for the apical trunk ===========================
objref trunk
trunk = new SectionList()
access L5PC.apic[0]
trunk.append()
access L5PC.apic[1]
trunk.append()
access L5PC.apic[2]
trunk.append()
access L5PC.apic[3]
trunk.append()
access L5PC.apic[4]
trunk.append()
access L5PC.apic[14]
trunk.append()
access L5PC.apic[20]
trunk.append()
access L5PC.apic[26]
trunk.append()
access L5PC.apic[34]
trunk.append()
access L5PC.apic[36]
trunk.append()



//=================== Parameters for simulation ===========================
blockca = 1
isDC = 0

strdef s1, s2, s3, s4
if(blockca==0){s1=""}
if(blockca==1){s1="qCa"}

if(blockca){
	forsec L5PC.apical{	if(ismembrane("Ca_LVAst")){	gCa_LVAstbar_Ca_LVAst = gCa_LVAstbar_Ca_LVAst*.25}}
	forsec L5PC.apical{	if(ismembrane("Ca_HVA")){	gCa_HVAbar_Ca_HVA = gCa_HVAbar_Ca_HVA*.25}}
}

nSS = 175
nSD = 100

//=================== Initialize output files ===========================
objref ff1,ff2

ff1 = new File()
ff2 = new File()

//=================== Initialize biophysical parameters ===========================
objref theStim
access L5PC.soma
theStim = new IClamp(0.5)
theStim.amp = 0.0
if(isDC){
	theStim.amp = -0.2
	theStim.dur = 800
	theStim.del = 0
}

//=================== Initialize synapses ===========================
objref somaSynList, dendSynList, backSynList

somaSynList = new List()
dendSynList = new List()
backSynList = new List()

temp = 0
forsec L5PC.basal{ temp = temp+nseg }
forsec tuftList{ temp = temp+nseg }

bigNum = temp*3
print bigNum

// numClust = $1 //number of clusters
	// section list for synapses to be distributed over= $2
	// gmax = $3, this is the max conductance of the nmda current
	// ntar = $4, this defines the conductance of the ampa current
	//            with ampa current = gmax/ntar
	// synsPerClust =$5 //number of synapses per cluster
	// lClust = $6 //length of cluster
//somaSynList = distSynsCluster2(nClustBasal,L5PC.basal,1,1,nPerClust,15)	 // clustered tuft input
//dendSynList = distSynsCluster2(nClustTuft,tuftList,1,1,nPerClust,15) // cluster basal input
//backSynList = distSynsUniform(0,allSections,1,1) // background input
somaSynList = distSynsUniform(bigNum,L5PC.basal,1,1)	 // clustered tuft input
dendSynList = distSynsUniform(bigNum,tuftList,1,1) // cluster basal input



//=================== Plot synapse locations ===========================
for i=0,somaSynList.count()-1{
    shapeG.point_mark(somaSynList.object(i),2,"O",3)
}

for i=0,dendSynList.count()-1{
    shapeG.point_mark(dendSynList.object(i),2,"O",3)
}

//=================== Initialize simulation ===========================
// this is to make sure the model is at rest
// Set all conductances to zero
for i=0,somaSynList.count()-1{ somaSynList.object(i).gmax = 0.0 }
for i=0,dendSynList.count()-1{ dendSynList.object(i).gmax = 0.0 }
//for i=0,backSynList.count()-1{ backSynList.object(i).gmax = 0.0 }

// Find resting potential
access L5PC.soma
objref vrec
vrec = new Vector()
//vrec.record(&L5PC.soma.v(0.5))
cvode.active(1)
//forall{v=-75}
tstop = 1000
run()
vrest = v
print "REST AFTER LONG CVODE RUN IS ", vrest, " AT TIME ", t

objref svstate
svstate  = new SaveState()
svstate.save()


cvode.active(0)
svstate.restore()
tstop = t+20
dt = 0.025
while(t<tstop){fadvance()}
vrest = v
print "REST AFTER NONCVODE RUN IS ", vrest, " AT TIME ", t

simStopTime = t
svstate.save()
//=================== End initialize simulation ===========================




objref trec, g1
g1 = new Graph()


access L5PC.soma
distance(0)
sprint(s2,"%s%s%s","freq_",s1,".dat")
ff1.wopen(s2) //freq
sprint(s2,"%s%s%s","spikes_",s1,".dat")
ff2.wopen(s2) //spikes



objref vrec, vrec2, trec


nSomaClust = 30
nDendClust = 20
clustSize = 10
nPerClust = clustSize
nClustBasal = nSomaClust
nClustTuft = nDendClust


objref nClustSyns
nClustSyns = new Vector(nPerClust)

objref rvec
objref rn
rn = new Random()
rn.discunif(0, bigNum-1)
rvec = new Vector()

objref rn2
rn2 = new Random()
rn2.uniform(55,105)

objref apc
objref apcv
objref isiv



// matrixes to put data into
objref mSpikes, mFreq
mSpikes = new Matrix(nClustBasal+1,nClustTuft+1)
mFreq = new Matrix(nClustBasal+1,nClustTuft+1)

print "Begin iterating through clusters"
objref nClustSyns
nClustSyns = new Vector(nPerClust)


//=================== Begin simulations ===========================
print "Begin iterating through clusters"
	print "Syns Soma\tSyns Dend\tSpikes\tFreq"
i=0
for i=-1,nClustBasal-1{ 
	for ii=-1,nClustTuft-1{
		//print "soma synapse: ", clustSize*(i+1)
		//print "dend synapse: ", clustSize*(ii+1)
		
		access L5PC.soma
		apc = new APCount(0.5)
		apcv = new Vector()
		apc.thresh = 0
		apc.record(apcv)
		
		// reset all synapses to zero
		for iii=0,dendSynList.count()-1{ dendSynList.object(iii).gmax = 0.0 }
		for iii=0,somaSynList.count()-1{ somaSynList.object(iii).gmax = 0.0 }
			
	// turn on the appropriate number of randomly chosen basal synapses
	if(i!=-1){ //if basal synapses
		rvec = new Vector(clustSize*(i+1))
		rvec.setrand(rn)
		for k=0,rvec.size()-1{ 
		somaSynList.object(rvec.x[k]).gmax=1.0
		somaSynList.object(rvec.x[k]).del=(simStopTime+1)	
	}}
	for iii=0,rvec.size()-1{somaSynList.object(rvec.x[iii]).del=(simStopTime+rn2.repick())}
	
	// turn on the appropriate number of randomly chosen dendritic synapses
	if(ii!=-1){ //if dend synapses
		rvec = new Vector(clustSize*(ii+1))
		rvec.setrand(rn)
		for k=0,rvec.size()-1{ 
		dendSynList.object(rvec.x[k]).gmax=1.0
		dendSynList.object(rvec.x[k]).del=(simStopTime+1)	
	}}
	for iii=0,rvec.size()-1{dendSynList.object(rvec.x[iii]).del=(simStopTime+rn2.repick())}
	

	
	/*
		vrec = new Vector()
		vrec2 = new Vector()
		trec = new Vector()
		*/
		//vrec.record(&L5PC.soma.v(0.5))
		svstate.restore(1) // go back to rest
		// run simulation
		tstop = t+200
		dt = 0.05
		//dt = 0.1
		while(t<tstop){
			fadvance()
			//vrec.append(L5PC.soma.v)
			//vrec2.append(L5PC.apic[36].v(0.9))
			//trec.append(t)
			}
		
		
		


/*
	vrec.printf(ff1,"%8.4f\t")
	ff1.printf("\n")
	
	vrec2.printf(ff2,"%8.4f\t")
	ff2.printf("\n")
	*/

	isiv = new Vector()
	//print "number of spikes is ", apcv.size()
	for iii=1,apcv.size()-1{
		isiv.append(apcv.x(iii)-apcv.x(iii-1))
	}
	if(apcv.size()==1){isiv.append(1000)} // single spike
	if(apcv.size()==0){isiv.append(10000)} // no spikes
	//print "freq is ", 1000/isiv.mean()
	
	//ff1.printf("%8.4f\t",1000/isiv.min()) // uncommenting this will overwrite data
	//ff2.printf("%8.4f\t",apcv.size()) // uncommenting this will overwrite data

	print clustSize*(i+1),"\t\t",clustSize*(ii+1),"\t\t",apcv.size(),"\t",1000/isiv.min(),"\t",1000/isiv.mean()
	//mFreq.x[i][ii]=1000/isiv.mean()
	//mSpikes.x[i][ii]=apcv.size()
	
}//for ii
ff1.printf("\n")
ff2.printf("\n")
}
//=================== End simulations ===========================

//mFreq.fprint(0,ff1)
//mSpikes.fprint(0,ff2)


ff1.close()
ff2.close()

quit()


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