The provided code models the probabilistic nature of neurotransmitter release at synapses with short-term synaptic plasticity mechanisms, focusing on facilitation and depression processes. Here’s a breakdown of the biological basis of this model: ### Biological Basis 1. **Stochastic Neurotransmitter Release:** - The key focus of this model is simulating the stochastic (random) nature of neurotransmitter release at synaptic junctions. Neurotransmitter release is not deterministic; rather, it occurs with a certain probability when a presynaptic neuron fires an action potential. 2. **Release Probability (`P`):** - The variable `P` in the model represents the instantaneous probability of neurotransmitter release. This probability is determined by both presynaptic facilitation and depression processes. 3. **Short-Term Synaptic Plasticity:** - The model incorporates two primary forms of short-term synaptic plasticity: - **Facilitation:** An increase in synaptic strength resulting from previous activity that temporarily enhances the probability of neurotransmitter release. This is simulated in the model by the parameters `f` and `tau_F`, representing the additive facilitation per spike and its decay time constant, respectively. - **Depression:** A decrease in synaptic strength due to the depletion of readily releasable vesicles following repetitive stimulation, leading to a temporary reduction in release probability. In the model, `d1` and `tau_D1` represent the multiplicative depression per spike and its decay, respectively. - Notably, the model includes variables to capture slow depression (commented out here), which suggests an extended vesicle depletion or recycling process, reflective of biological synapses with more complex dynamics. 4. **Varela et al. Model:** - The code is inspired by the model developed by Varela et al., which quantitatively describes short-term plasticity at excitatory synapses in layer 2/3 of the rat primary visual cortex. This reference underscores the biological basis for incorporating facilitation and depression dynamics into synaptic release probability. 5. **Randomness and Separate Streams:** - The `randGen()` function suggests that each synapse instance can have an independent random number generator, emphasizing the autonomy and variability in synaptic transmission across different synapses. By leveraging the biological mechanisms of short-term synaptic facilitation and depression, this computational model simulates the nuanced and complex behavior of synaptic transmission in response to action potentials, reflective of the variability and temporal dynamics observed in cortical neurons.