# Biological Basis of the `ampa_D2.mod` Code The provided `ampa_D2.mod` code is for a computational model designed to simulate AMPA receptor-mediated synaptic transmission in neuronal networks, with a focus on the incorporation of dynamics such as short-term synaptic depression and spontaneous excitatory postsynaptic potentials (EPSPs). Below, I will elaborate on the biological concepts represented in the code. ## AMPA Receptors and Synaptic Transmission ### AMPA Receptor Function AMPA (α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid) receptors are ionotropic glutamate receptors that mediate fast excitatory synaptic transmission in the central nervous system. They're responsible for the majority of rapid excitatory post-synaptic potentials (EPSPs) within the brain. ### Model Dynamics The model uses kinetic schemes to capture the dynamics of AMPA receptors following the methodology from Destexhe et al., 1994. It includes synaptic conductance changes upon neurotransmitter release and incorporates first-order synaptic dynamics, which are characteristic of receptor binding and unbinding kinetics (represented by the parameters `Alpha` and `Beta` in the code). ## Key Biological Features in the Code ### Synaptic Conductance - **`gmax` and `g`:** The maximum synaptic conductance (`gmax`) and the actual conductance (`g`) represent how the synapse controls ion flow across the post-synaptic membrane, influenced by the binding of neurotransmitters to the AMPA receptors. - **`Cdur`, `Cmax`:** These parameters relate to the duration of neurotransmitter presence in the synaptic cleft and the maximal concentration, influencing the receptor activation dynamics. ### Excitatory Postsynaptic Potentials (EPSPs) - **Stochastic EPSPs:** The code simulates spontaneous EPSPs (`psp_weight`, `mini_fre`, `SS_denom`) reflecting biological observations of miniature EPSPs (mEPSPs) that occur in the absence of presynaptic action potentials, attributed to random vesicle release events. - **`synon`:** Represents the sum of weights of all synapses in the "onset" state (active state), modulating the total synaptic input by scaling `gmax`. ### Short-term Synaptic Depression Short-term synaptic depression is modeled by the factor `E`, representing the depletion of synaptic resources. It quantitatively influences the synaptic weight (`weight`) to account for reduced synaptic efficacy following successive neurotransmitter release, a biological phenomenon observed as a temporary reduction in synaptic strength. - **`Tr`:** The depression time constant reflects how quickly synaptic resources recover post-depression. ### Timing and Interaction - **`deadtime` and `afterspike_time`:** These represent refractory-like periods where synapses are inactive following neurotransmitter release or an action potential, preventing immediate subsequent transmissions. They ensure realistic synaptic temporal dynamics like recovery time after activity. ## Randomness and Variability Incorporating randomness (via Random123) in the simulation captures the inherent stochastic nature of neurotransmitter release and post-synaptic receptor interaction, reflecting biological variability in synaptic function. ## Conclusion The `ampa_D2.mod` code models biological synaptic mechanisms focusing on AMPA receptor-mediated transmission, depicting synaptic conductance dynamics, spontaneous and triggered EPSPs, and short-term synaptic depression. These elements collectively capture the complex, dynamic nature of synaptic transmission in neuronal circuits.