Distance-dependent synaptic strength in CA1 pyramidal neurons (Menon et al. 2013)

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Accession:222726
Menon et al. (2013) describes the experimentally-observed variation in synaptic AMPA and NMDA conductance as a function of distance from the soma. This model explores the effect of this variation on somatic EPSPs and dendritic spike initiation, as compared to the case of uniform AMPA and NMDA conductance.
Reference:
1 . Menon V, Musial TF, Liu A, Katz Y, Kath WL, Spruston N, Nicholson DA (2013) Balanced synaptic impact via distance-dependent synapse distribution and complementary expression of AMPARs and NMDARs in hippocampal dendrites. Neuron 80:1451-63 [PubMed]
Model Information (Click on a link to find other models with that property)
Model Type: Neuron or other electrically excitable cell; Synapse;
Brain Region(s)/Organism: Hippocampus;
Cell Type(s): Hippocampus CA1 pyramidal GLU cell;
Channel(s): I A; I K; I Na, slow inactivation;
Gap Junctions:
Receptor(s): AMPA; NMDA;
Gene(s):
Transmitter(s):
Simulation Environment: NEURON;
Model Concept(s): Synaptic Integration;
Implementer(s): Menon, Vilas [vilasmenon2008 at u dot northwestern dot edu];
Search NeuronDB for information about:  Hippocampus CA1 pyramidal GLU cell; AMPA; NMDA; I A; I K; I Na, slow inactivation;
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MenonEtAl2013
README.txt
dv.mod
ih_new.mod
kadist.mod *
kaprox.mod *
kdrca1.mod *
leakcond.mod *
nafast2.mod
naslowcond2.mod
nmda.mod
spines.mod *
synampa.mod *
vmax.mod
vmax2.mod
vmaxlast.mod
vms.mod
code_membrane.hoc
code_objects.hoc
code_point_processes.hoc
code_routine_for_runs.hoc
code_run_multiple_spines_on_branch.hoc
code_run_single_spine.hoc
code_synapse_array_setup.hoc
code_synapse_setup.hoc
mosinit.hoc
ri06.nrn *
simulated_axon.nrn *
spinearraygeom.nrn
spinegeom.nrn
                            
//Procedures and functions to place synapses with AMPA and NMDA conductances

proc syn_cc_array() {			//procedure to place synapse with AMPA conductance
	dendno=$1
	secno=$2
	gsyn=$3
	index=$4
	option=2
	if (option==1) {
		dend[dendno].sec {ampasyn[index].loc(secno)}
	} else {
		spineheadarray[index].sec {ampasyn[index].loc(0.5)}
	
	}
	ampasyn[index].gmax = gsyn
	ampasyn[index].tau0 = 0.2
	ampasyn[index].tau1 = 2
	ampasyn[index].e = 0
	ampasyn[index].onset = synwait
	
}

proc syn_nmdacc_array() {		//procedure to place synapse with NMDA conductance	
	dendno=$1
	secno=$2
	gsyn=$3
	index=$4
	option=2
	if (option==1) {
		dend[dendno].sec {nmdasyn[index].loc(secno)}
	} else {
		spineheadarray[index].sec {nmdasyn[index].loc(0.5)}
	}
	nmdasyn[index].gmax = gsyn
	nmdasyn[index].onset = synwait
}

//conductance values based on EM data
parta=0.00007	
partn=0.00006	
NA=3.95*parta		//nonperforated synapse AMPA 
PAp=8.01*parta		//proximal perforated synapse AMPA
PAm=11.60*parta		//middle perforated synapse AMPA
PAd=12.43*parta		//distal perforated synapse AMPA
NN=2.50*partn		//nonperforated synapse NMDA
PNp=8.29*partn		//proximal perforated synapse NMDA
PNm=5.90*partn		//middle perforated synapse NMDA
PNd=5.95*partn		//distal perforated synapse NMDA

func calc_syn_strength() {local type
	//function to calculate synaptic strength based on distance from the soma, using EM derived data for linear conductance change
	type=$1		//1=AMPA, 2=NMDA
	distval=$2
	if 	(type==1) {
		if (distval<135) {
			return (0.0653*distval+2.786)*parta
		} else {
			return (0.0166*distval+9.359)*parta
		}
	} else {
		if (distval<135) {
			return (-0.0435*distval+11.77)*partn
		} else {
			return (-0.001*distval+6.035)*partn
		}
	}
}

func findpoint() {
	//pick a random point with linearly decreasing probability, mimicking the nonperforated synapse scaling from the data
	dendlength=$1
	point=$2
	fb=-0.02*dendlength*dendlength+(5.8*dendlength)
	outpoint=25*(5.8-sqrt(5.8*5.8-0.08*point*fb))/dendlength
	return outpoint
}


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