The code provided aims to model synaptic transmission, focusing specifically on AMPA and NMDA receptor-mediated synaptic currents, and incorporating presynaptic short-term plasticity mechanisms. Here is a breakdown of its biological basis: ### AMPA and NMDA Receptors 1. **Dual-Exponential Conductance Profile**: - The code models synaptic currents that occur through AMPA and NMDA receptors using a dual-exponential conductance profile. This profile mimics the way actual synaptic current rises and falls over time in biological systems. - AMPA receptors contribute to fast synaptic transmission, while NMDA receptors mediate slower, longer-lasting synaptic responses and involve calcium permeability critical for synaptic plasticity. 2. **Reversal Potential**: - Both AMPA and NMDA receptors share a reversal potential in the model, set at 0 mV (`e = 0 mV`). This reflects the non-specific cation permeability of these receptors in biological synapses. ### Short-Term Synaptic Plasticity The code implements mechanisms to simulate short-term synaptic plasticity based on synaptic efficacy: 1. **Utilization of Synaptic Efficacy (Use)**: - Represents the initial probability of neurotransmitter release. This can be dynamically adjusted during simulations to reflect variable excitatory post-synaptic potential (EPSP) strength. 2. **Facilitation and Depression**: - **Facilitation (Fac)**: If greater than 0, it models the tendency for synaptic efficacy to increase when pre-synaptic spikes occur in quick succession. It is implemented as a process where the effective neurotransmitter release probability increases with closely-timed stimuli. - **Depression (Dep)**: Represents the phenomenon where repeated firing leads to reduced synaptic strength due to depletion of neurotransmitter resources or receptor desensitization, modeled as a relaxation process. 3. **Probability Variables (Pv and Pr)**: - **Pv**: Probability of a vesicle being available for release, capturing the synaptic resource availability. - **Pr**: Product of Utilization (`u`) and Vesicle Probability (`Pv`), reflecting the overall probability of neurotransmitter release during an action potential. ### Stochastic Synaptic Release - The model includes random activation of synapses, using both deterministic and stochastic methods to decide whether a synapse is activated upon a spike. This reflects the probabilistic nature of synaptic transmission at individual synapses. ### Random Number Generation - The code uses random numbers to simulate variability in synaptic release, crucial for capturing the inherent stochasticity of neurotransmitter release in real synapses. A specific random noise mechanism ensures reproducibility and independence of random sequences across parallel simulations. This model represents a sophisticated attempt to capture both the temporal dynamics and probabilistic nature of synaptic transmission, leveraging biophysical principles observed in real neural tissues to model short-term synaptic plasticity and the dynamics of receptor activity.