The provided code models synaptic transmission using the Tsodyks-Markram (TM) model of short-term synaptic plasticity. This model captures dynamic changes in synaptic strength that occur over short timescales, typically milliseconds to seconds, and are critical for understanding how synapses contribute to neural computations and information processing in the brain. ### Biological Basis 1. **Synaptic Short-Term Plasticity (STP):** - The model incorporates three types of synaptic dynamics: **facilitation (F)**, **depression (D)**, and **pool (P)**. They respectively represent different synaptic modifications: - **Facilitation** refers to the temporary increase in synaptic strength following presynaptic spikes, attributed to residual calcium concentration influencing neurotransmitter release probability. - **Depression** reflects the depletion of available neurotransmitter vesicles following synaptic activity, resulting in a temporary decrease in synaptic efficacy. - The "P" component may represent a combination of mechanisms involved in balancing facilitation and depression effects. 2. **Dynamic Variables:** - **`r(p,i)`** represents the fraction of synaptic resources in the state ready to be utilized (or released) for neurotransmitter release upon the arrival of an action potential. - **`x(p,i)`** reflects the fraction of synaptic resources available at the synapse, after accounting for depression. - **`Is(p,i)`** is the synaptic current component, which integrates over time following the release of neurotransmitters modeled by **A** (amplitude). 3. **Time Constants:** - **`tauf` and `taud`:** These are the time constants representing facilitation and depression kinetics, respectively. They determine how quickly the synaptic variables recover to their baseline states following activity. - **`tausI`:** This parameter characterizes the time constant of the synaptic current's decay, which is related to how neurotransmitters influence the postsynaptic neuron after release. 4. **Parameters and Inputs:** - **`U`:** Represents the utilization parameter, indicating how likely the resources are to be used upon an action potential arriving at the presynaptic terminal. - **`A`:** The amplitude of the synaptic response, which, alongside other variables, determines the postsynaptic impact. 5. **Poissonian Inputs:** - The code implies a stochastic synaptic input, common in biological synapses where neuron firing can be modeled by Poisson processes (evidenced by `spd` representing synaptic input as a Poisson process). The TM model elegantly captures the complex interplay of synaptic facilitation and depression, providing a mechanistic framework for understanding how synaptic transmission is modulated over short timescales. This is essential for tasks like rapid processing, learning, and memory in neurons where temporal dynamics are crucial.