CA1 pyramidal neuron: nonlinear a5-GABAAR controls synaptic NMDAR activation (Schulz et al 2018)

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Accession:258867
The study shows that IPSCs mediated by a5-subunit containing GABAA receptors are strongly outward-rectifying generating 4-fold larger conductances above -50?mV than at rest. Experiments and modeling show that synaptic activation of these receptors can very effectively control voltage-dependent NMDA-receptor activation in a spatiotemporally controlled manner in fine dendrites of CA1 pyramidal cells. The files contain the NEURON code for Fig.8, Fig.S8 and Fig.S9 of the paper. The model is based on the model published by Bloss et al., 2017. Physiological properties of GABA synapses were modified as determined by optogenetic activation of inputs during voltage-clamp recordings in Schulz et al. 2018. Other changes include stochastic synaptic release and short-term synaptic plasticity. All changes of mechanisms and parameters are detailed in the Methods of the paper. Simulation can be run by starting start_simulation.hoc after running mknrndll. The files that model the individual figures have to be uncommented in start_simulation.hoc beforehand.
Reference:
1 . Schulz JM, Knoflach F, Hernandez MC, Bischofberger J (2018) Dendrite-targeting interneurons control synaptic NMDA-receptor activation via nonlinear a5-GABAA receptors. Nat Commun 9:3576 [PubMed]
Model Information (Click on a link to find other models with that property)
Model Type: Neuron or other electrically excitable cell; Dendrite; Synapse;
Brain Region(s)/Organism: Hippocampus; Mouse;
Cell Type(s): Hippocampus CA1 pyramidal GLU cell;
Channel(s): I h; I A;
Gap Junctions:
Receptor(s): GabaA; GabaB; AMPA; NMDA;
Gene(s):
Transmitter(s): Gaba; Glutamate;
Simulation Environment: NEURON;
Model Concept(s):
Implementer(s): Schulz, Jan M [j.schulz at unibas.ch];
Search NeuronDB for information about:  Hippocampus CA1 pyramidal GLU cell; GabaA; GabaB; AMPA; NMDA; I A; I h; Gaba; Glutamate;
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Alpha5_NMDA_CA1_pyr
README.html
dists.mod *
eff.mod *
exc.mod *
gabab.mod
h.mod
id.mod *
inh.mod
kad.mod *
kap.mod *
kdr.mod *
na3.mod *
nmdaSyn.mod
syns.mod *
tonic.mod
activateExcitation.hoc
activateInhibition_JMS.hoc
addChannels_JMS.hoc
addExcitation_JMS.hoc
addVgatInhibition_JMS.hoc
channelParameters.hoc
Connect_Stimulator2ExcSyn.hoc
Connect_Stimulator2InhSyn.hoc
Fig8_tuft_NMDA_spike.hoc
FigS8_SR_SLM_burst_stim.hoc
FigS9_test_TI.hoc
flagVgatInhibition_JMS.hoc
Generate_Stimulator.hoc
getBranchOrder.hoc *
idMorph.hoc
inhibitionBiophysics_JMS.hoc
initializationAndRun.hoc *
loadMorph.hoc *
mosinit.hoc
naceaxon.nrn *
Print-to-File.hoc
processMorph.hoc *
proofreadMorph.hoc *
resetNSeg.hoc *
screenshot.png
start_simulation.hoc
synHelperScripts.hoc
SynStim_SR_SLM_control.hoc
SynStim_SR_SLM_noInh.hoc
SynStim_SR_SLM_redInh.hoc
SynStim_SR_SLM_TI.hoc
tuft_NMDA_spike_fast.hoc
tuft_NMDA_spike_noRect.hoc
twinApical.swc *
update_Synapses.hoc
                            
// GENERATES AN IO CURVE OF RESPONSES TO BRIEF HIGH FREQUENCY STIMULATION 
// OF INCREASING NUMBER OF EXCITATORY INPUTS IN SLM
// TESTS FOR EFFECT OF GABAA ON INDUCTION OF NMDAR-MEDIATED DENDRITIC SPIKES 
//
// This script:
// - runs a family of two simulations: 
//		stimulation in stratum rediatum and stratum lacunosum moleculare,
//		same stimulation in the presence of AP5.
// For each simulation, voltages are displayed for the soma, a proximal tuft
// dendrite, and a distal tuft dendrite. Additionally, shape plots are used
// to illustrate the location of excitatory and inhibitory synapses.

// Numerical parameters for simulation.
tstop = 120 //  stop time of simulation
stimStart=20
dtime=0.2
stim_no=3
stim_inter=5
stim_delay=0
stim_noise=0
simul_iter=10

scale_factor1=0.2
scale_factor0=0.4

//seed of random number generator
randShift_inh = 2.357 
randShift_exc = 0.357 

//parameters for rectifying and non rectifying part of GABAsynapses
GABAweight_total=0.001 
GABAweight0=(GABAweight_total/5) 
GABAweight1=(4*GABAweight_total/5) 
GABAtau2=30
GABAtau1=1.0
GABArelP=1 
slope_factor=3
V50=-52

//establish rectifying and non-rectifying parts of GABA synapses
for ii=1,totVgatAt  {
    synGABA[ii-1].tau1=  GABAtau1/2
    synGABA[ii-1].tau2 =  GABAtau2/2
    synGABA[ii-1].slope_factor=0.3
    synGABA[ii-1].V50=-150

    synGABArect[ii-1].tau1=  GABAtau1
    synGABArect[ii-1].tau2 = GABAtau2
    synGABArect[ii-1].slope_factor=slope_factor
    synGABArect[ii-1].V50=V50
}

load_file("Generate_Stimulator.hoc")
load_file("Connect_Stimulator2ExcSyn.hoc")
load_file("Connect_Stimulator2InhSyn.hoc")

//setup recording vectors etc.
objref recv_tuft1, recv_tuft2, recv_tuft3, recv_soma, rect
objref  recv_tuft4, recv_tuft5, recv_trunk, recv_trunk2
strdef ColLabel, file_name1, file_name2, file_name3, file_name4
objref outfile, storeM_control, storeM_fast, storeM_noRect, storeM_fast_NR
load_file("Print-to-File.hoc")

ColLabel = "Membrane potential"
file_name1="C:/../Control.atf"
file_name3="C:/../fast_scaled.atf"
file_name2="C:/Daten/Modeling/Bloss_Spruston_Neuron_2016/CA1_pyr_JMS_v5/output/Syn_IO_GABAkinetics/nextround2/no_rect.atf"

recv_soma = new Vector()
recv_tuft1 = new Vector()
recv_tuft2 = new Vector()
recv_tuft3 = new Vector()
recv_tuft4 = new Vector()
recv_tuft5 = new Vector()
recv_trunk = new Vector()
recv_trunk2 = new Vector()
rect = new Vector()
recv_soma.record(&soma.sec.v(0.5),dtime)
rect.record(&t)

rec_conditions=8


storeM_fast = new Matrix(tstop/dtime+1,rec_conditions*simul_iter)
storeM_fast_NR = new Matrix(tstop/dtime+1,rec_conditions*simul_iter)
storeM_control = new Matrix(tstop/dtime+1,rec_conditions*simul_iter)
storeM_noRect = new Matrix(tstop/dtime+1,rec_conditions*simul_iter)

nExcAct_SLM = 150// number of excitatory synapses to activate, ~10% of 1464 total with spine density of 0.5/um2
nInhAct_SLM =  10 // number of inhibitory synapses to activate; ~3.5% of 280 total
nExcAct_SR = 0//200//number of excitatory synapses to activate, ~3% of 6000 total (oblique plus trunk; instead of 4920)
nInhAct_SR = 0//30 //number of inhibitory synapses to activate; 21% of 145 total 
// i.e. same proportion of inhibitory and excitatory synapses are activated at step 7

load_file("update_Synapses.hoc")

// Parameters for visualisation. dendInd1 and dendInd2 refer to dendritic
// indices that will have voltage traces plotted.
dendInd1 = 112 // left apical, SLM, with inhibition
dendInd2 = 149 // left apical, terminal neurite, SLM, no inhibition
dendInd3 = 179 // SLM, no inhibition
dendInd4 = 180 // SLM, no inhibition
dendInd5 = 176 // SLM, 2 GABA synapses
dendInd6 = 161 // apical trunk, just at SR/SLM border
dendInd7 = 85 //apical trunk, just at SR/SLM border
objref dend1Ref,dend2Ref,dend3Ref,dend4Ref,dend5Ref,dend6Ref,dend7Ref
	Cell[0].dend[dendInd1] {dend1Ref = new SectionRef()} 
	Cell[0].dend[dendInd2] {dend2Ref = new SectionRef()} 
	Cell[0].dend[dendInd3] {dend3Ref = new SectionRef()} 
	Cell[0].dend[dendInd4] {dend4Ref = new SectionRef()} 
	Cell[0].dend[dendInd5] {dend5Ref = new SectionRef()} 
	Cell[0].dend[dendInd6] {dend6Ref = new SectionRef()} 
	Cell[0].dend[dendInd7] {dend7Ref = new SectionRef()} 
    
recv_tuft1.record(&dend1Ref.sec.v(0.5),dtime)    
recv_tuft2.record(&dend2Ref.sec.v(0.5),dtime)    
recv_tuft3.record(&dend3Ref.sec.v(0.5),dtime)    
recv_tuft4.record(&dend4Ref.sec.v(0.5),dtime)    
recv_tuft5.record(&dend5Ref.sec.v(0.5),dtime)    
recv_trunk.record(&dend7Ref.sec.v(0.5),dtime)    
recv_trunk2.record(&dend6Ref.sec.v(0.5),dtime)

// Determine number of segments to track.
totSegs = 0
forall { for (x,0) { totSegs +=1 } }

// Create graphs for visualisation.
	objref voltBL_d1,voltBL_d2, voltAMPA 
	objref shapeExc[simul_iter/2],shapeInh

	voltBL_d1 = new Graph()
	voltBL_d1.addvar("soma.sec.v(0.5)",2,1)
	voltBL_d1.addvar("dend7Ref.sec.v(0.5)",3,1) 
	voltBL_d1.addvar("dend2Ref.sec.v(0.5)",4,1) 
	voltBL_d1.label("Non-linear")
	voltBL_d1.exec_menu("Keep Lines")
	voltBL_d1.size(stimStart-20,tstop,-75,-5)
    
	voltBL_d2 = new Graph()
	voltBL_d2.addvar("soma.sec.v(0.5)",2,1)
	voltBL_d2.addvar("dend7Ref.sec.v(0.5)",3,1) 
	voltBL_d2.addvar("dend1Ref.sec.v(0.5)",4,1) 
	voltBL_d2.label("Non-linear")
	voltBL_d2.exec_menu("Keep Lines")
	voltBL_d2.size(stimStart-20,tstop,-75,-5)
    
    strdef shape_label
	for (ii=1;ii<simul_iter/2+1; ii=ii+1){ //
        sprint(shape_label,"Exc, %d",2*ii)
        print shape_label
        shapeExc[ii-1] = new Shape()
        shapeExc[ii-1].label(shape_label)
    }
	shapeInh = new Shape()
	shapeInh.label("Inh")
    
// Create vectors that will store indices of active excitatory and 
// inhibitory synapses.
objref curExc_SLM, curExc_SR, curInh_SLM, curInh_SR  

// Vectors of normalised release probability 
objref norm_Pr_exc, norm_Pr_inh_SLM, ActSyn, ActSyn_inh

// Initialise simulations by adding channels and assigning genotypes to
// inhibitory synapses.
initChannels()
seedGenotypes()
	
norm_Pr_exc = new Vector(5) //, [1, 1.55,	1.83,	1.93,	2.03])
ActSyn = new Vector(5)
norm_Pr_exc.x[0] = 1
norm_Pr_exc.x[1] = 1.5 
norm_Pr_exc.x[2] = 1.8
norm_Pr_exc.x[3] = 1.9 
norm_Pr_exc.x[4] = 2.0

// DO SIMULATION AND PLOT RESULTS.
// turn off cvode variable time step in order to avoid errors during repetitive simulations
cvode_active(0)


// run simulation with inhibition
curGr = graphList[0].append(voltBL_d1)
curGr = graphList[0].append(voltBL_d2)
norm_Pr_inh_SLM = new Vector(5) //
ActSyn_inh = new Vector(5)
norm_Pr_inh_SLM.x[0] = 1
norm_Pr_inh_SLM.x[1] = 1
norm_Pr_inh_SLM.x[2] = 1   
norm_Pr_inh_SLM.x[3] = 1 
norm_Pr_inh_SLM.x[4] = 1

activateInhibition(cellList,-1,1) // clear inhibition
curInh_SLM = activateInhibition(tuftList,nInhAct_SLM,randShift_inh,"vgat",1) 
ActSyn_inh = set_InhSyn_syn_fixed(curInh_SLM, randShift_inh/4, nInhAct_SLM, shapeInh) 
print "In SLM, the number of activated inhibitory synapses at each pulse are: ",ActSyn_inh.x[0], ", " , ActSyn_inh.x[1], ", " , ActSyn_inh.x[2], ", " , ActSyn_inh.x[3], ", " , ActSyn_inh.x[4]

for(jj=1; jj<simul_iter+1; jj=jj+1){
    print jj
	// Clear excitation and then turn on select synapses.
	activateExcitation(cellList,-1,1) // clear excitation
	curExc_SLM = activateExcitation(tuftList,jj*nExcAct_SLM,randShift_exc) // activate excitatory synapses
	
    shape_no=(jj/2)-1
    if (jj%2==1){shape_no=(jj-1)/2}
	
    ActSyn = set_gluSyn_fixed_N(curExc_SLM, randShift_exc/2, 0.1, norm_Pr_exc, shapeExc[shape_no])
	print "In SLM, the number of activated synapses at each pulse are: ",ActSyn.x[0], ", " , ActSyn.x[1], ", " , ActSyn.x[2], ", " , ActSyn.x[3], ", " , ActSyn.x[4]
	    
    init() 
    run()
    storeM_control.setcol(jj-1,recv_soma)
    storeM_control.setcol(jj-1+simul_iter,recv_tuft1) 
    storeM_control.setcol(jj-1+2*simul_iter,recv_tuft2) 
    storeM_control.setcol(jj-1+3*simul_iter,recv_tuft3)
    storeM_control.setcol(jj-1+4*simul_iter,recv_tuft4) 
    storeM_control.setcol(jj-1+5*simul_iter,recv_tuft5) 
    storeM_control.setcol(jj-1+6*simul_iter,recv_trunk) 
    storeM_control.setcol(jj-1+7*simul_iter,recv_trunk2)
        
    reset_gluSyn()
}
graphList[0].remove_all()


// SAVE OUTPUT
//print2file(storeM_control,file_name1,ColLabel)


// run simulation without rectification inhibition
load_file("tuft_NMDA_spike_noRect.hoc")

// run simulation with PV-like dynamics
load_file("tuft_NMDA_spike_fast.hoc")

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