The provided code models synaptic transmission involving AMPA and NMDA receptors at a synapse, incorporating elements of presynaptic short-term plasticity. Let's break down the biological basis of these components: ### AMPA and NMDA Receptors 1. **Receptor Types:** - **AMPA Receptors**: These are ionotropic glutamate receptors responsible for fast excitatory synaptic transmission. They open upon glutamate binding, allowing Na⁺ ions to enter the neuron, causing depolarization. - **NMDA Receptors**: These receptors also bind glutamate but have distinct properties, including voltage-dependent Mg²⁺ block. They allow Ca²⁺ to enter the cell under specific conditions, which is crucial for synaptic plasticity mechanisms like long-term potentiation (LTP). 2. **Dual-Exponential Conductance:** - The model uses dual-exponential functions to represent the time course of synaptic conductance change for both AMPA and NMDA receptors following neurotransmitter release. This mimics the rise and decay of synaptic currents observed experimentally. - `tau_r` and `tau_d` parameters represent the rise and decay time constants for each receptor’s conductance, which are based on experimental data showing that AMPA and NMDA receptors activate and deactivate at different rates. 3. **Magnesium Block (Mg²⁺):** - The NMDA receptor conductance is modulated by a magnesium (Mg²⁺) gate. At resting membrane potential, Mg²⁺ ions block the channel, which is relieved upon depolarization of the postsynaptic membrane. This Mg²⁺ block is essential for the role of NMDA receptors in coincidence detection and synaptic plasticity. ### Short-Term Synaptic Plasticity 1. **Presynaptic Modulation:** - The code models presynaptic short-term plasticity using variables such as `Use`, `Dep`, and `Fac` (facilitation and depression). These represent the resources available for release and the modulation of release probability based on previous activity. - **Facilitation (`Fac`)**: This represents the increase in synaptic strength due to successive presynaptic action potentials when synaptic vesicle release probability increases. - **Depression (`Dep`)**: This occurs when repeated activation depletes the available pool of synaptic vesicles, reducing release probability. 2. **Release Probability and Vesicle Dynamics:** - The model considers the probability of vesicle release (`Pr`) based on available vesicle pools, capturing dynamics that are critical for understanding short-term changes in synaptic strength. 3. **Variable `u`:** - Represents a dynamically updated value influencing the probability of neurotransmitter release, modulated by facilitation and the baseline utilization of synaptic efficacy (`Use`). ### Synaptic Current Computation - The model computes synaptic currents (`i_AMPA` and `i_NMDA`) based on conductance changes and the difference between the membrane potential and the reversal potential (`e`). This reflects the driving force for ion flow mediated by these receptors. ### Randomness in Synaptic Release - **Random Synaptic Events**: Synaptic release is inherently probabilistic. The code incorporates randomness to simulate this probabilistic vesicle release, a feature that mimics the experimental observation that neurotransmitter release can vary with each presynaptic action potential. ### Conclusion Overall, this model offers a detailed representation of both AMPA and NMDA receptor-mediated synaptic currents and the presynaptic mechanisms of short-term plasticity found in real synapses. Such modeling is crucial for understanding the complexities of synaptic transmission and plasticity, fundamental processes underlying learning and memory in the brain.