Experimentally Constrained Mechanistic and Data-Driven Models for Simulating NMDA Receptor Dynamics

Background: The N-methyl-d-aspartate receptor (NMDA-R) is a glutamate ionotropic receptor in the brain that is crucial for synaptic plasticity, which underlies learning and memory formation. Dysfunction of NMDA receptors is implicated in various neurological diseases due to their roles in both normal cognition and excitotoxicity. However, their dynamics are challenging to capture accurately due to their high complexity and non-linear behavior. Methods: This article presents the elaboration and calibration of experimentally constrained computational models of GluN1/GluN2A NMDA-R dynamics: (1) a nine-state kinetic model optimized to replicate experimental data and (2) a computationally efficient look-up table model capable of replicating the dynamics of the nine-state kinetic model with a highly reduced footprint. Determination of the kinetic model's parameter values was performed using the particle swarm optimization algorithm. The optimized kinetic model was then used to generate a rich input-output dataset to train the look-up table synapse model and estimate its coefficients. Results: Optimization produced a kinetic model capable of accurately reproducing experimentally found results such as frequency-dependent potentiation and the temporal response due to synaptic release of glutamate. Furthermore, the look-up table synapse model was able to closely mimic the dynamics of the optimized kinetic model. Conclusions: The results obtained with both models indicate that they constitute accurate alternatives for faithfully reproducing the dynamics of NMDA-Rs. High computational efficiency is also achieved with the use of the look-up table synapse model, making this implementation an ideal option for inclusion in large-scale neuronal models.