The provided code models synaptic transmission with a focus on AMPA and NMDA receptors and incorporates presynaptic short-term plasticity. It is implemented within the NEURON simulation environment, which is commonly used for computational neuroscience research. ### Biological Basis 1. **Synaptic Transmission**: - This model simulates synaptic transmission involving AMPA (α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid) and NMDA (N-methyl-D-aspartate) receptors, which are types of glutamate receptors widely distributed in the brain. Glutamate is the primary excitatory neurotransmitter in the central nervous system. - AMPA receptors mediate fast synaptic transmission. Their dynamics are modeled using a dual-exponential conductance profile, characterized by a rapid rise time constant (`tau_r`) and a slower decay time constant (`tau_d`). 2. **Reversal Potential**: - Both AMPA and NMDA receptors share a common reversal potential (`e` set to 0 mV in the code), which is important in determining the direction and magnitude of the ionic current flowing through these channels when activated. 3. **Short-term Plasticity**: - The model incorporates mechanisms of short-term synaptic plasticity, specifically depression and facilitation. These are forms of synaptic plasticity that modulate synaptic strength over short time scales, ranging from milliseconds to a few seconds. - **Depression** (`Dep`): A decrease in synaptic strength due to resource depletion, modeled here with a relaxation time constant indicating the recovery time for synaptic resources. - **Facilitation** (`Fac`): An increase in synaptic strength due to residual calcium effects, leading to an increased probability of neurotransmitter release. This is reflected in the model by adjusting the `u` variable, which modulates synaptic efficacy over time. 4. **Probabilistic Release and Resource Dynamics**: - The release of neurotransmitters is probabilistic, mimicking biological synapses where each action potential does not necessarily result in neurotransmitter release. Variables like `Pr` (probability of release) and `Pv` (probability of vesicle availability) embody this stochastic nature. - The `NET_RECEIVE` block outlines the conditions under which neurotransmitter release occurs, incorporating various biological factors such as the probability of vesicle availability and the facilitation and depression dynamics. 5. **Normalization Factor**: - The model employs a normalization factor (`factor`) to ensure the synaptic conductance reaches its peak value at the expected time (`tp`), aligning model dynamics with biological observations. 6. **Random Number Generation**: - Random numbers are employed to simulate the probabilistic nature of synaptic transmission, which is fundamental to capturing the inherent variability observed in biological synapses. ### Summary This code effectively captures key aspects of synaptic transmission involving AMPA and NMDA receptors, emphasizing the dynamic modulation of synaptic strength through short-term plasticity mechanisms. The model is built upon established physiological principles and explores the probabilistic nature of synaptic events, offering insights into the complex behaviors that arise from interactions between synaptic components and neurotransmitter systems.