Single excitatory axons form clustered synapses onto CA1 pyramidal cell dendrites (Bloss et al 2018)

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Accession:237728
" ... Here we show that single presynaptic axons form multiple, spatially clustered inputs onto the distal, but not proximal, dendrites of CA1 pyramidal neurons. These compound connections exhibit ultrastructural features indicative of strong synapses and occur much more commonly in entorhinal than in thalamic afferents. Computational simulations revealed that compound connections depolarize dendrites in a biophysically efficient manner, owing to their inherent spatiotemporal clustering. ..."
Reference:
1 . Bloss EB, Cembrowski MS, Karsh B, Colonell J, Fetter RD, Spruston N (2018) Single excitatory axons form clustered synapses onto CA1 pyramidal cell dendrites. Nat Neurosci 21:353-363 [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): Hippocampus CA1 pyramidal GLU cell;
Channel(s):
Gap Junctions:
Receptor(s):
Gene(s):
Transmitter(s):
Simulation Environment: NEURON;
Model Concept(s):
Implementer(s):
Search NeuronDB for information about:  Hippocampus CA1 pyramidal GLU cell;
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BlossEtAl2018
readme.html
dists.mod *
eff.mod *
gScale.mod
id.mod *
kad.mod *
kap.mod *
kdr.mod *
na3.mod *
nmdaSyn.mod
syns.mod *
addChannels.hoc *
addSpines.hoc
addSynapses.hoc
buildCell.hoc
channelParameters.hoc *
createBiophysics.hoc
createMorphology.hoc
createPlots.hoc
createPointers.hoc
doAnalysis.hoc
getBranchOrder.hoc *
idMorph.hoc
initializationAndRun.hoc
loadMorph.hoc
mosinit.hoc *
processMorph.hoc
proofreadMorph.hoc
resetNSeg.hoc *
screenshot.png
simParameters.hoc
singleSim.hoc
singleSimDist.hoc
spineGeom.hoc
spineShaftConc.hoc
start.hoc
trackVoltages.hoc
twinApical.swc *
varyDistribution.hoc
varySpaceTime.hoc
                            
// This script looks at interaction between two inputs. The variable input is
// shifted, in space and time, relative to the reference input across many 
// trials. Results are written to outputDxDt.txt.
//
// The output can be visualised in the R environment by calling
// visualiseDxDt().
objref dxs
dxs = new Vector(nVDend) // will store x locations for synapse #2

ii=0
dendTar.sec{
	for(x,0){
		dxs.x[ii] = x
		ii = ii + 1
	}
}

dtN = 81 // choose odd number, number of time intervals
dtI = 2 // time interval (in msec) to scan across.
objref dts
dts = new Vector(dtN)
for ii=0,dtN-1{
	curDt = ii-(dtN-1)/2
	curDt = curDt*dtI
	dts.x[ii] = curDt
}

objref dendSegMax
objref peakVs
objref parMax
peakVs = new Matrix(dxs.size(),dts.size())

objref curOut

for iii=0,dxs.size()-1{
	for jjj=0,dts.size()-1{
		// Prep simulation.
		initChannels()
		initSynapses()
		initSynapticWeight(weightAmpa*scaleAmp1,weightAmpa*scaleAmp2,weightNmda*scaleAmp1,weightNmda*scaleAmp2)
		initSynapticTime(100,100+dts.x[jjj])
		moveSpines(dendLoc1,dxs.x[iii]) // keep spine 1 fixed, move spine 2
		updatePointers()
		init()
		
		// Run.
		run()
		
		dendMax = vPar[0].max()
		dendMax = dendMax - vPar[0].min() // remove baseline	
			
		peakVs.x[iii][jjj] = dendMax
		
	
	}
}

// Write output.
objref fw
fw = new File()
fw.wopen("outputDxDt.txt")
peakVs.fprint(fw,"%g\t")
fw.close()

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