CA1 pyramidal neuron synaptic integration (Bloss et al. 2016)

 Download zip file   Auto-launch 
Help downloading and running models
Accession:187610
"... We examined synaptic connectivity between molecularly defined inhibitory interneurons and CA1 pyramidal cell dendrites using correlative light-electron microscopy and large-volume array tomography. We show that interneurons can be highly selective in their connectivity to specific dendritic branch types and, furthermore, exhibit precisely targeted connectivity to the origin or end of individual branches. Computational simulations indicate that the observed subcellular targeting enables control over the nonlinear integration of synaptic input or the initiation and backpropagation of action potentials in a branchselective manner. Our results demonstrate that connectivity between interneurons and pyramidal cell dendrites is more precise and spatially segregated than previously appreciated, which may be a critical determinant of how inhibition shapes dendritic computation."
Reference:
1 . Bloss EB, Cembrowski MS, Karsh B, Colonell J, Fetter RD, Spruston N (2016) Structured Dendritic Inhibition Supports Branch-Selective Integration in CA1 Pyramidal Cells. Neuron 89:1016-30 [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): I Na,t; I K;
Gap Junctions:
Receptor(s): AMPA; NMDA; Gaba;
Gene(s):
Transmitter(s):
Simulation Environment: NEURON;
Model Concept(s): Synaptic Integration;
Implementer(s): Cembrowski, Mark S [cembrowskim at janelia.hhmi.org];
Search NeuronDB for information about:  Hippocampus CA1 pyramidal GLU cell; AMPA; NMDA; Gaba; I Na,t; I K;
/
arrayTomography
README.txt
dists.mod *
eff.mod *
exc.mod
id.mod *
inh.mod
kad.mod *
kap.mod *
kdr.mod *
na3.mod *
nmdaSyn.mod
syns.mod *
activateExcitation.hoc
activateInhibition.hoc
addChannels.hoc *
addExcitation.hoc
addVgatInhibition.hoc
channelParameters.hoc *
flagVgatInhibition.hoc
getBranchOrder.hoc *
idMorph.hoc
inhibitionBiophysics.hoc
initializationAndRun.hoc *
loadMorph.hoc *
mosinit.hoc *
naceaxon.nrn *
processMorph.hoc *
proofreadMorph.hoc *
resetNSeg.hoc *
start.hoc
synHelperScripts.hoc
twinApical.swc *
                            
// THIS FILE ALLOCATES EXCITATORY SYNAPSES ACROSS THE ARBOR.

// Preallocate objects for excitation.
// Prep for synapse placement.
nExc = 100000
objref nsAmpa[nExc],ncAmpa[nExc],synAmpa[nExc]
objref nsNmda[nExc],ncNmda[nExc],synNmda[nExc]
synInd = 0

// This function takes a SectionRef and a vector with surface area synapse
// scaling and places the number of correspond excitatory synapses.  Note that
// the low index of the first placed synapse needs to be specified in order to
// give indices to all of the generated synapses.
//
// $o1: SectionRef instance.  Contains the section to implement with the
// 	scaling rule.
// $o2: Vector instance.  Contains the scaling rule to implement.  If the
//	density is uniform across the branch (e.g., 0.1 synapses/um2), then
//	simply supply as 
//		foo = new Vector(1); foo.x[0] = 0.1; $o1 = foo
// 	Conversely, if
// 	there is spatial variation across the branch (say, 0.1 synapses/um2 in the
//	first third, 0.2 synapses/um2 in the second third, and 0.3 synapses/um2
// 	in the final third), then, supply as
//		foo = new Vector(3) ; foo.x[0] = 0.1, foo.x[1] = 0.2;
//			foo.x[2] = 0.3 ; $o1 = foo
// $3: numeric.  The index of the first synapse to be created and placed.
//
// The number of synapses added is returned.
func addExcitation() {local nDiv,runningSa,synAdd,synCur,ii,jj
	nDiv = $o2.size()
	synCur = $3
	// Place synapses.
	$o1.sec {
		for ii=1,nDiv {
	
			runningSa=0
			synAdd=0
			
			for(x,0){
				if(((ii-1)/nDiv)<x_eff(x)){
					if(x_eff(x)<(ii/nDiv+0.00000001)){
						runningSa+=area(x)
						if(int(runningSa*$o2.x[ii-1])>(synAdd+0.000001)){
							toAdd = int(runningSa*$o2.x[ii-1]-synAdd)

							for jj=1,toAdd{	
								synCur = synCur+1
								synAdd = synAdd+1
								synAmpa[synCur-1] = new excSyn(x)
								synAmpa[synCur-1].xEff = x_eff(x)
								nsAmpa[synCur-1] = new NetStim(x)
								ncAmpa[synCur-1] = new NetCon(nsAmpa[synCur-1],synAmpa[synCur-1])
	
								synNmda[synCur-1] = new Exp2SynNmda(x)
								nsNmda[synCur-1] = new NetStim(x)
								ncNmda[synCur-1] = new NetCon(nsNmda[synCur-1],synNmda[synCur-1])
							}
				
						}else{
							toAdd = 0
						}
						exc_syns(x) = toAdd
					}
				}
			}
		}
		

	}
	
	return synCur-$3
}




// Add synapses to tuft.
	objref denExcTuft
	denExcTuft = new Vector(1)
		denExcTuft.x[0] = 0.2
		
	forsec tuftList {
		curSec = new SectionRef()
		numAdded = addExcitation(curSec,denExcTuft,synInd)	
		synInd+=numAdded
	}
	
// Add synapses to obliques.
	objref denExcObl
	denExcObl = new Vector(1)
		denExcObl.x[0] = 1.2 // spines/um2
		
	forsec obliqueList {
		curSec = new SectionRef()	
		numAdded = addExcitation(curSec,denExcObl,synInd)
		synInd+=numAdded
	}
	
// Add synapses to trunk.
	objref denExcTrunk,denExcTrunkTemp
	denExcTrunk = new Vector(1)
		denExcTrunk.x[0] = 0.8
	denExcTrunkTemp = new Vector(denExcTrunk.size())
	soma.sec{distance()}
	
	forsec primList {
		curSec = new SectionRef()	
		scaleFact = theDist/200
		denExcTrunkTemp.copy(denExcTrunk)
		denExcTrunkTemp.mul(scaleFact)
		
		numAdded = addExcitation(curSec,denExcTrunkTemp,synInd)
		synInd+=numAdded
	}

// Add synapses to obliques.
	objref denExcBasalPrim,denExcBasalSec,denExcBasalTerm
	denExcBasalPrim = new Vector(1)
		denExcBasalPrim.x[0] = 0
	denExcBasalSec = new Vector(1)
		denExcBasalSec.x[0] = 0.1
	denExcBasalTerm = new Vector(1)
		denExcBasalTerm.x[0] = 0.9
	
		
	forsec basalList {
		curSec = new SectionRef()
		if(isTerm_id){
			numAdded = addExcitation(curSec,denExcBasalTerm,synInd)
		}else{
			if(abs(brOrd_id-1)<0.001){
				numAdded = addExcitation(curSec,denExcBasalPrim,synInd)
			}else{
				if(abs(brOrd_id-2)<0.001){
					numAdded = addExcitation(curSec,denExcBasalSec,synInd)
				}else{
					numAdded = addExcitation(curSec,denExcBasalSec,synInd) // keep as 2ary for time being
				}
			}
		}

		synInd+=numAdded
	}
	
// reset value of nExc
nExc = synInd	
//print "Number of excitatory synapses is: ",nExc


// Change properties of excitatory inputs.
for ii=1,nExc {

	synAmpa[ii-1].tau1= 0.2  // Jarsky et al., 2005
	synAmpa[ii-1].tau2 = 2 // Jarsky et al., 2005
	synAmpa[ii-1].e = 0
	
	
	nsAmpa[ii-1].interval = 0
	nsAmpa[ii-1].number = 1
	nsAmpa[ii-1].start = 97
	nsAmpa[ii-1].noise = 0
	ncAmpa[ii-1].weight = 0.00018 // 0.18 nS; from Jarsky et al 2005
	ncAmpa[ii-1].delay = 0
	
	synNmda[ii-1].tau1= 5  
	synNmda[ii-1].tau2 = 100 
	synNmda[ii-1].e = 0
	
	
	nsNmda[ii-1].interval = 0
	nsNmda[ii-1].number = 1
	nsNmda[ii-1].start = 97
	nsNmda[ii-1].noise = 0
	ncNmda[ii-1].weight = 0.00018
	ncNmda[ii-1].delay = 0


}

Loading data, please wait...