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
                            
// Assign orders to a given branch.  This is implemented in the following way:
// 1) give a seed section that will receive a given order; then,
// 2) all other branches in the same effective branch will be given the same
// 	order ;
// 3) all children of the effective branch will receive the next order
//
// Steps 2 and 3 repeat until all effective child branches of the assigned
// branch are given an order.
// INPUT:
// $o1: SectionRef instance, corresponding to the section to order.
// $o2: [optional] variable.  First order value to assign (default = 1).  This
//	allows flexibility to call the soma as being order "0".
// OUTPUT:
// The value brOrd_id is assigned for all daughters of the given section.
strdef secToMatch
proc assignBranchOrder(){local nn,oo,brN,curOrd,numsSecs localobj curSecs,nextSecs,usedSecs,tempSec,theHits
	// Identify the associated effective branch.
	$o1.sec {
		secToMatch = secname()
	}
	
	for nn=1,numEffBr{
		forsec brEff[nn-1]{
			if(abs(strcmp(secToMatch,secname()))<0.0001){
				brN = nn-1
			}
		}
	}
	
	
	// Assign all branches in effective branch to be the sections to
	// iterate over.
	curSecs = new SectionList()
	nextSecs = new SectionList()
	numSecs = 0
	forsec brEff[brN] {
		nextSecs.append()
		numSecs+=1
	}
	
	// Assign first order.
	if(numarg()>1.1){
		curOrd = $2
	}else{
		curOrd = 1
	}
		
	// Iterative assign orders.
	usedSecs = new SectionList()

	while(numSecs>0.1){
		
		curSecs = new SectionList()
		// Add the effective branch associated with each branch.  
		// Populate curSecs; nextSecs will then be reset.
		theHits = new Vector()
		forsec nextSecs {
			secToMatch = secname()
			for nn=1,numEffBr{
				forsec brEff[nn-1]{
					gotHit = 0
					if(abs(strcmp(secToMatch,secname()))<0.0001){
						theHits.append(nn-1)
					}
				}
				
				
			}
		}
		
		for nn=1,theHits.size() {
			forsec brEff[theHits.x[nn-1]]{
				curSecs.append()
			}
		}
		nextSecs = new SectionList()
		
		numSecs = 0
		// Assign branch order and populate all potential next visited
		// dendrites.
		forsec curSecs {
			brOrd_id = curOrd
			nextSecs.children()
			usedSecs.append()
		}
		
		// Take only unique used sections.
		usedSecs.unique()
		
		// Remove any branches that have already been assigned a 
		// branch order.
		forsec usedSecs {
			secToMatch = secname()
			forsec nextSecs {
				if(abs(strcmp(secToMatch,secname()))<0.0001){
					nextSecs.remove()
				}
			}
		}
			
		// Calculate total number of remaining sections to visit.
		forsec nextSecs {
			numSecs +=1
		}
		curOrd+=1

	}
}



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