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_nmda.mod *
0_syn_g.mod *
makeSavestates.hoc *
Threshold_Template.hoc *

Potassium channel, Hodgkin-Huxley style kinetics
Kinetic rates based roughly on Sah et al. and Hamill et al. (1991)

Author: Zach Mainen, Salk Institute, 1995, zach@salk.edu
26 Ago 2002 Modification of original channel to allow 
variable time step and to correct an initialization error.
Done by Michael Hines(michael.hines@yale.e) and 
Ruggero Scorcioni(rscorcio@gmu.edu) at EU Advance Course 
in Computational Neuroscience. Obidos, Portugal

20110202 made threadsafe by Ted Carnevale
20120514 fixed singularity in PROCEDURE rates

Special comment:

This mechanism was designed to be run at a single operating 
temperature--37 deg C--which can be specified by the hoc 
assignment statement
celsius = 37
This mechanism is not intended to be used at other temperatures, 
or to investigate the effects of temperature changes.

Zach Mainen created this particular model by adapting conductances 
from lower temperature to run at higher temperature, and found it 
necessary to reduce the temperature sensitivity of spike amplitude 
and time course.  He accomplished this by increasing the net ionic 
conductance through the heuristic of changing the standard HH 
  g = gbar*product_of_gating_variables
  g = tadj*gbar*product_of_gating_variables
  tadj = q10^((celsius - temp)/10)
  temp is the "reference temperature" (at which the gating variable
    time constants were originally determined)
  celsius is the "operating temperature"

Users should note that this is equivalent to changing the channel 
density from gbar at the "reference temperature" temp (the 
temperature at which the at which the gating variable time 
constants were originally determined) to tadj*gbar at the 
"operating temperature" celsius.

	RANGE n, gk, gbar
	RANGE ninf, ntau
	GLOBAL q10, temp, tadj, vmin, vmax

	(mA) = (milliamp)
	(mV) = (millivolt)
	(pS) = (picosiemens)
	(um) = (micron)

	gbar = 5   	(pS/um2)	: 0.03 mho/cm2
	tha  = 25	(mV)		: v 1/2 for inf
	qa   = 9	(mV)		: inf slope		
	Ra   = 0.02	(/ms)		: max act rate
	Rb   = 0.002	(/ms)		: max deact rate	

:	dt		(ms)
	temp = 23	(degC)		: original temp 	
	q10  = 2.3			: temperature sensitivity

	vmin = -120	(mV)
	vmax = 100	(mV)

	v 		(mV)
	celsius		(degC)
	a		(/ms)
	b		(/ms)
	ik 		(mA/cm2)
	gk		(pS/um2)
	ek		(mV)
	ntau (ms)	

STATE { n }

    tadj = q10^((celsius - temp)/(10 (degC))) : make all threads calculate tadj at initialization

	n = ninf

        SOLVE states METHOD cnexp
	gk = tadj*gbar*n
	ik = (1e-4) * gk * (v - ek)

DERIVATIVE  states {   :Computes state variable n 
        trates(v)      :             at the current v and dt.
        n' =  (ninf-n)/ntau

PROCEDURE trates(v (mV)) {  :Computes rate and other constants at current v.
                      :Call once from HOC to initialize inf at resting v.
    TABLE ninf, ntau
    DEPEND celsius, temp, Ra, Rb, tha, qa
    FROM vmin TO vmax WITH 199

	rates(v): not consistently executed from here if usetable_hh == 1

:        tinc = -dt * tadj
:        nexp = 1 - exp(tinc/ntau)

PROCEDURE rates(v (mV)) {  :Computes rate and other constants at current v.
                      :Call once from HOC to initialize inf at resting v.

    : singular when v = tha
:    a = Ra * (v - tha) / (1 - exp(-(v - tha)/qa))
:    a = Ra * qa*((v - tha)/qa) / (1 - exp(-(v - tha)/qa))
:    a = Ra * qa*(-(v - tha)/qa) / (exp(-(v - tha)/qa) - 1)
    a = Ra * qa * efun(-(v - tha)/qa)

    : singular when v = tha
:    b = -Rb * (v - tha) / (1 - exp((v - tha)/qa))
:    b = -Rb * qa*((v - tha)/qa) / (1 - exp((v - tha)/qa))
:    b = Rb * qa*((v - tha)/qa) / (exp((v - tha)/qa) - 1)
    b = Rb * qa * efun((v - tha)/qa)

        tadj = q10^((celsius - temp)/10)
        ntau = 1/tadj/(a+b)
	ninf = a/(a+b)

FUNCTION efun(z) {
	if (fabs(z) < 1e-4) {
		efun = 1 - z/2
		efun = z/(exp(z) - 1)