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
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 *

This simulation builds ball-and-stick neurons of variable length (200 to 1,000 µm dendrites) with active sodium and potassium channels in the soma, AIS, and axon. It then delivers stochastic patterns of synaptic input iteratively to determine the threshold number of synapses necessary to initiate an action potential in the AIS for each dendritic location (typically at 10 µm intervals) and each of seven RMPs (-55 to -85 mV), for inputs having maximum AMPA conductance ranging from 100 to 1000 pS. Output files include runs for AMPA-only ("...A.dat" files) or both AMPA and NMDA (1 nS) conductance ("...B.dat" files) according to the following naming convention:

Example file name: A9Tr0ThLen600V65A.dat

This file is for a ball and stick neuron with a 600 um dendrite ("...ThLen600...") with RMP set to -65mV ("...V65_...") for AMPA-only inputs ("...A.dat") having an AMPA conductance of 900 pS ("A9..."). 

1. Compile mod files for synaptic conductances and voltage-gated sodium and potassium channels. 

2. Run the "init_BallStick.hoc" file.

3. The "makeSavestates" file will run first to build steady-state files for each of the neuron morphologies / RMPs, then it will begin to find synaptic thresholds for all dendritic locations and RMPs for ball-and-stick neurons of varying length (200 to 1,000 um). 

6. To test the 1.5 nS AMPA conductance, just modify the "init_BallStick.hoc" file so that ac_ind is locked to 15 (e.g., " for ac_ind = 15,15 { ").

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