# Biological Basis of the Computational Model The provided code models synaptic transmission through two key types of glutamatergic receptors: AMPA and NMDA receptors. These receptors play crucial roles in synaptic plasticity, learning, and memory in the central nervous system. Synaptic currents formed by these receptors are modeled using dual exponential conductances to capture their distinct dynamic properties. ## AMPA Receptors AMPA receptors mediate the fast excitatory postsynaptic potentials (EPSPs) through the influx of sodium (Na+) ions. They are modeled using a simple dual exponential function to represent the synaptic conductance (gA). The parameters `tau1` and `tau2` denote the rise and decay times of the AMPA receptor-mediated conductance, simulating the quick response of these receptors to glutamate release. ## NMDA Receptors NMDA receptors contribute to slower components of synaptic transmission and conduct both sodium (Na+) and calcium (Ca2+) ions. Unlike AMPA receptors, NMDA receptors are voltage-dependent due to a magnesium (Mg2+) block. This voltage-dependence introduces a non-linear response to synaptic activity, crucial for the receptors' role in synaptic plasticity (e.g., Long-Term Potentiation, LTP). The code uses a second dual exponential function to model the NMDA receptor conductance (gN), with `Ntau1` and `Ntau2` defining its rise and decay times. The NMDA conductance is adjusted by a voltage-dependent magnesium blockade, calculated using the parameters `eta`, `Mg`, and `gamma`. This blockade is crucial for NMDA's role in detecting coincident presynaptic and postsynaptic activity. ## Synaptic Current The synaptic current (`i`) is determined by the combined conductances of the AMPA and NMDA receptors. The equation `i = (gA + gN) * (v - e)` represents the total current based on the specific conductance values and the driving force (the difference between the membrane potential `v` and the reversal potential `e`). ## Conclusion This model captures the dynamics of fast and slow components of synaptic currents mediated by AMPA and NMDA receptors, respectively. By including voltage-dependence in the NMDA component, the model also incorporates one of the key mechanisms of synaptic integration and plasticity, linking the computational elements of the model with their biological counterparts.