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

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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;
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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 *
                            
// DEFINE FUNCTIONS THAT HELP WITH SYNAPSE ALLOCATION.

// identify vgat synapses that are part of a given branch
// provide a SectionRef instance to seek, and optionally, two x values for 
// low and high values to seek.
// $o1: SectionRef instance.
// $2,$3: optional, variables, low and high values to constrain results.
// NOTE: $2 and $3 are currently implemented to work with EFFECTIVE x values,
// 	not the true x values.
strdef seekSecName
obfunc getVgatInds(){local theX localobj vecOfInds

	vecOfInds = new Vector(0)
	
	
	$o1.sec { seekSecName = secname() }
	for ii=1,totVgatAt {
		{ 
			theX = synVgatAt[ii-1].get_loc() 
			theX = synVgatAt[ii-1].xEff
			
			if(abs(strcmp(secname(),seekSecName))<0.00001){
				if(numarg()>1.1){
					// x values supplied
					if(theX>$2&&theX<$3){	
						vecOfInds.append(ii-1)
					}
				}else{
					// no x values supplied, simply take
					vecOfInds.append(ii-1)
				}
			}
			pop_section()
		}
	}
	return vecOfInds
}

theRandomSeed = 1000000
// Sample values from the given vector without replacement.  
// $o1: Vector.  The vector of values to sample from.
// $2: numeric.  The number of values to sample.
// $3: numeric.  The seed.  If set =-1 (or just <=-0.1), sets a random seed that increments.  If >-0.1, uses
//	the supplied seed.
obfunc sampleNoReplace(){local tempRand,jj localobj randGen,theOut,theOut2
	if($2>$o1.size()){
		//print "Number of items to return exceeds vector size."
		//print "Returning the original vector."
		//print "Wanted/got:",$2,$o1.size()
		return $o1
	}
	
	if($2<0.0001){
		theOut2 = new Vector()
		return theOut2
	}
	
	if($3>(-0.1)) {
		// supplied a random seed; go with it.
		randGen = new Random($3)
	}else{
		// define a random number generator with new seed
		randGen = new Random(theRandomSeed)
		theRandomSeed+=1
	}
	randGen.uniform(0,$o1.size())
	theOut = new Vector()
	theOut2 = new Vector($2)
	while(1){
		tempRand = int(randGen.repick())
		// add new number if not present
		if(theOut.contains(tempRand)){
			// do nothing
		}else{
			// add
			theOut.append(tempRand)
		}
		
		if(abs(theOut.size()-$2)<0.0001){
			// have filled the desired vector.			
			for jj=1,$2{
				theOut2.x[jj-1] = $o1.x[theOut.x[jj-1]]
			}
			return theOut2
		}
	}
}

// Sample a given branch (and potential limits on x locations) to identify the 
// a subset of synapses.  This is used for tagging VGAT+ synapses as other types
// of synapses.
// $o1: SectionRef.  The section to look at.
// $2: numeric.  The number of sampled synapse indices to return.
// $3: numeric.  The seed to use.
// $3,$4: numerics (optional).  The low and high x values to consider for the 
//	particular SectionRef.
obfunc synIndSubset(){localobj allInds,theOut
	allInds = new Vector()
	if(numarg()<3.1){
		allInds = getVgatInds($o1)
	}else{
		allInds = getVgatInds($o1,$4,$5)
	}
	theOut = sampleNoReplace(allInds,$2,$3)
	return theOut
}

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