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 *
                            
// This function turns on a given number of excitatory synapses contained within a 
// given SectionList.  This function is analogous to the activateInhibition()
// proc but does not do anything genotype-specific.
//
// !!NOTE!!: this function does not attempt to turn off
// all synapses before activating the desired synapses.  It merely sets a 
// subset of synapses to "on", agnostic to whether they were previously on
// or not.  Note, however, that this function can be called twice to first
// turn all synapses off, and then turn on only the desired synapses.  See
// notes below.
//
// This function can also be used to toggle off synapses in the following way:
// A) to shut off all synapses in a given SectionList, set $2<0.
// B) to shut off all synapses in all sections, set $2<0 and set $o2 to be
//	the whole tree.
//
// Also, to activate all synapses in a given region, just set $2 to be a huge
//	number; this will activate all of the synapses in the desired region.
//
// INPUT:
// $o1: SectionList instance.  Sections to draw from.
// $2: variable.  Number of synapses to activeate.
// $3: variable.  The seed to assign.
//
// OUTPUT:
// the .isOn flag is toggled as desired for the synapses selected.
//

strdef curGenStr
obfunc activateExcitation() {local ii localobj allSyns,theSec,finalSyns
	allSyns = new Vector() // all (potential) synapses that qualify for activation	
	finalSyns = new Vector() // vector of synapse indices that will be activated
	
	
	// interate over all synapses, adding those that are in the right
	// domain.
	for ii=0,nExc-1{
		synAmpa[ii].get_loc()
		theSec = new SectionRef()
		if(sectionRefInList(theSec,$o1)){
			allSyns.append(ii)
		}
		pop_section()
	}
	
	if($2<0){
		// Want to shut off all synapses in the provided region.
		for ii=0,allSyns.size()-1{
			synAmpa[allSyns.x[ii]].isOn = 0
			synNmda[allSyns.x[ii]].isOn = 0
		}
		return finalSyns
	}else{		
		// Choose random indices from desired subset.
		finalSyns = sampleNoReplace(allSyns,$2,$3)  // supply non-random seed
		
		// Activate these synapses.
		for ii=0,finalSyns.size()-1{
			synAmpa[finalSyns.x[ii]].isOn=1
			synNmda[finalSyns.x[ii]].isOn=1
		}
		return finalSyns
	}
}
		
		

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