NMDA receptors enhance the fidelity of synaptic integration (Li and Gulledge 2021)

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Excitatory synaptic transmission in many neurons is mediated by two co-expressed ionotropic glutamate receptor subtypes, AMPA and NMDA receptors, that differ in their kinetics, ion-selectivity, and voltage-sensitivity. AMPA receptors have fast kinetics and are voltage-insensitive, while NMDA receptors have slower kinetics and increased conductance at depolarized membrane potentials. Here we report that the voltage-dependency and kinetics of NMDA receptors act synergistically to stabilize synaptic integration of excitatory postsynaptic potentials (EPSPs) across spatial and voltage domains. Simulations of synaptic integration in simplified and morphologically realistic dendritic trees revealed that the combined presence of AMPA and NMDA conductances reduces the variability of somatic responses to spatiotemporal patterns of excitatory synaptic input presented at different initial membrane potentials and/or in different dendritic domains. This moderating effect of the NMDA conductance on synaptic integration was robust across a wide range of AMPA-to-NMDA ratios, and results from synergistic interaction of NMDA kinetics (which reduces variability across membrane potential) and voltage-dependence (which favors stabilization across dendritic location). When combined with AMPA conductance, the NMDA conductance balances voltage- and impedance-dependent changes in synaptic driving force, and distance-dependent attenuation of synaptic potentials arriving at the axon, to increase the fidelity of synaptic integration and EPSP-spike coupling across neuron state (i.e., initial membrane potential) and dendritic location of synaptic input. Thus, synaptic NMDA receptors convey advantages for synaptic integration that are independent of, but fully compatible with, their importance for coincidence detection and synaptic plasticity.
1 . Li C, Gulledge AT (2021) NMDA receptors enhance the fidelity of synaptic integration eNeuro
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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): Dentate gyrus granule GLU cell; Hippocampus CA3 pyramidal GLU cell;
Channel(s): I K; I Na,t;
Gap Junctions:
Receptor(s): AMPA; NMDA;
Transmitter(s): Glutamate;
Simulation Environment: NEURON;
Model Concept(s): Synaptic Integration;
Search NeuronDB for information about:  Dentate gyrus granule GLU cell; Hippocampus CA3 pyramidal GLU cell; AMPA; NMDA; I Na,t; I K; Glutamate;
0_kv.mod *
0_na.mod *
0_syn_g.mod *
Threshold_Template.hoc *
// The ball and stick model as a template with inputs length and v_init.

begintemplate Cell
public init, biophys

public soma, iseg, axon, dend, spine_head, spine_neck

proc init() {
	// $1 is branch length in microns
	// $2 is v_init
	bLength = $1
	voltInput = $2

create iseg[40]
create axon
create soma
create dend[1]
create spine_head[1]
create spine_neck[1]
create bigdend[3]

proc biophys() {
	length = $1 		// length of dendrite in microns
	v_init = $2 		// resting membrane potential

	celsius=37			// nominal temperature of simulation
	Ri=100    			// internal resistivity in ohm-cm
	Cm=1.0	      		// specific membrane capacitance in uF/cm^2
	Rm=15000      		// specific membrane resistivity in ohm/cm^2
	//na_dend=30			// default Na channel density in pS/um^2
	//kv_dend=30			// default K channel density in pS/um^2
	na_soma=100			// somatic Na channel density in pS/um^2
	kv_soma=100			// somatic k channel density in pS/um^2

	access soma

	create dend[length]
	create spine_head[length]
	create spine_neck[length]

	soma {
		nseg = 3
		diam = 10
		L = 20
		insert na 
		insert kv
		connect iseg[0](0), 0
		nao = 150
		ek = -96
		for i = 0,2 {
		connect bigdend[i] (0), 0.5

	for i = 0,2 {
		bigdend[i] {
		nseg = 21
		diam (0:1) = 5:1
		L = 600

	for i = 0, 39 {
		iseg[i] {
			nseg = 1
			diam = 2 - (1.5*(i/40))
			L = 1
			insert na
			insert kv 
			if (i < 5) {
				gbar_na = na_soma
				gbar_kv = kv_soma
			} else {
				gbar_na = 8000
				gbar_kv = 2000
			nao = 150
			ek = -96  

			if (i<39) connect iseg[i+1](0), 1
			if (i==39) connect axon(0),1  

	axon {
		nseg = 201
		L = 2000
		diam = .5
		insert na
		gbar_na = 300
		insert kv
		gbar_kv = 60
		nao = 150
		ek = -96

	for i = 0, length-1 {
		access soma

		access dend[i] 
			nseg = 1
			L = 1
			diam = 5 - ((4*i)/(length-1))
			if (i == 0) {
			connect soma(1), 0
			} else {
			connect dend[i-1](1), 0

			//Adding spines for every micron
			dend[i] connect spine_neck[i](0),.5
			spine_neck[i] connect spine_head[i](0),1
			spine_neck[i] {nseg=1 L=1 diam=0.0504573} 
			spine_head[i] {nseg=1 L=0.5 diam=0.5}

	forall {
		insert pas
		g_pas = 1/Rm
		cm = Cm
		Ra = Ri
		e_pas = v_init

	access axon

endtemplate Cell