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 L5/6 pyramidal 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 L5/6 pyramidal 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 this cell, as described in (Shai et al., PLoS Computational
// Biology, 2015)

//====================== 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/L5PCbiophys4.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/1.0+2 } // uncomment this to modify length of dendritic segment
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 ===========================
somaInput = 1 // synapses into the basal dendrites?
dendInput = 1 // synapses into the tuft dendrites?
nSS = 175 // number of synapses at soma
nSD = 100 // number of synapses in dendrite

//=================== 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(somaInput==1&&dendInput==1){
		ff1.wopen("O_vs_C.dat")
		ff2.wopen("O_vd_C.dat")
		nSyns = nSS
		nSynsD = nSD
		}
	if(somaInput==1&&dendInput==0){
		ff1.wopen("O_vs_C_onlyS.dat")
		ff2.wopen("O_vd_C_onlyS.dat")
		nSyns = nSS
		nSynsD=0
		}
	if(somaInput==0&&dendInput==1){
		ff1.wopen("O_vs_C_onlyD.dat")
		ff2.wopen("O_vd_C_onlyD.dat")
		nSyns=0
		nSynsD=nSD
		}
		







//=================== 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)	 // distributed tuft input
dendSynList = distSynsUniform(bigNum,tuftList,1,1) // distributed basal input

//=================== Plot synapses ===========================
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 ===========================
// 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()

access L5PC.soma
distance(0)

objref vrec, vrec2, trec

//=================== Initialize synapses ===========================
objref rvec
objref rn
rn = new Random(239480)
rn.discunif(0, bigNum-1)

objref rn2
rn2 = new Random(2139)
rn2.uniform(55,105)
	i=0	
	for iii=0,dendSynList.count()-1{ dendSynList.object(iii).gmax = 0.0 }
	for iii=0,somaSynList.count()-1{ somaSynList.object(iii).gmax = 0.0 }
	
	//set somatic ones
	if(i!=-1){
		rvec = new Vector(nSyns)
		rvec.setrand(rn)
		print "random soma synapses are:"
		print nSyns
		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())
	}
	
		//set dend ones
	if(i!=-1){
		rvec = new Vector(nSynsD)
		rvec.setrand(rn)
		print "random dend synapses are:"
		print nSynsD
		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())
	}
	

	
//=================== Run Simulation ===========================
		vrec = new Vector()
		vrec2 = new Vector()
		trec = new Vector()
		//vrec.record(&L5PC.soma.v(0.5))
		svstate.restore(1)
		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")
	print "soma v_max is ", vrec.max()
	print "dend v_max is ", vrec2.max()
	print ""

ff1.close()
ff2.close()

//Plot membrane potential during simulation
objref g
g = new Graph()
g.size(0, 350, -90,40)
vrec.plot(g,.1)

objref g2
g2 = new Graph()
g2.size(0, 350, -90,40)
vrec2.plot(g2,.1)
//quit()






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