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

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Accession:266802
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.
Reference:
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;
Gene(s):
Transmitter(s): Glutamate;
Simulation Environment: NEURON;
Model Concept(s): Synaptic Integration;
Implementer(s):
Search NeuronDB for information about:  Dentate gyrus granule GLU cell; Hippocampus CA3 pyramidal GLU cell; AMPA; NMDA; I Na,t; I K; Glutamate; 
Description:

	
This simulation is similar to that for Figure 2, but for branches on a dentate granule cell (DGC), with an arbitrary voltage threshold of 5 mV above somatic RMP.

The DGC morphology is adapted from Schmidt-Hieber C, Jonas P, Bischofberger J (2007) Subthreshold Dendritic Signal Processing and Coincidence Detection in Dentate Gyrus Granule Cells. J Neurosci 27:8430-8441.


1. Compile the mod files (0_syn_g.mod, 0_nmda.mod, 0_na.mod, 0_kv.mod). 


2. Run "init_DGC.hoc".

3. Data output will, for each dendritic branch (1-17) and each dendritic location and RMP, list the threshold number of synapses to drive the soma 5 mV above RMP. Each column is for a different stochastic pattern of synaptic input, each row is for ever-more-distal dendritic location (at 10 µm intervals). Name convention is as follows:

Example file name: V55_DGC1Br12_ASoma.dat

This file is for a dentate granule cell (DGC) with RMP set to -55mV ("V55_...") for synaptic input pattern 1 ("...DGC1...") to branch number 12 ("...Br12_...") for AMPA-only synapses ("..._ASoma.dat"). Branches range from 1 to 17 (e.g., "...Br1_..." to "...Br17_...") and includes NMDA-only ("...NSoma.dat") and AMPA+NMDA ("...BSoma.dat") trials (in addition to AMPA-only trials). Thus, each output file will have a single column of synaptic thresholds for one branch/RMP/pattern/conductance combination, with each row being a 10 um increment along the dendritic path.

Note: This simulation runs very slowly, and it may be useful to modify the code in "init_DGC.hoc" to limit simulations to a subset of RMPs, branch numbers, synaptic patterns, or conductance types.