The provided code models synaptic transmission and plasticity between pyramidal neurons in the brain, focusing on AMPA and NMDA receptor-mediated currents and the role of calcium dynamics in synaptic plasticity. Below are the key biological aspects modeled in the code: ### Synaptic Transmission 1. **AMPA and NMDA Receptors:** - **AMPA Receptors:** These receptors mediate fast excitatory synaptic transmission. The code uses parameters like `Cdur_ampa` (duration), `AlphaTmax_ampa` (maximum transition rate to the open state), and `Beta_ampa` (closing rate) to simulate their dynamic behavior. The conductance `g_ampa` is modulated by synaptic weights, integrating concepts of synaptic efficacy. - **NMDA Receptors:** These receptors are slower than AMPA receptors and are both voltage- and ligand-gated. The code incorporates NMDA receptor dynamics using `Cdur_nmda`, `AlphaTmax_nmda`, `Beta_nmda`, and incorporates a voltage-dependent function `sfunc(v)` to model the magnesium block characteristic of NMDA receptors. NMDA receptors also contribute to calcium influx into the cell, which is crucial for synaptic plasticity. 2. **Calcium Dynamics:** - The code models local calcium concentration changes in response to NMDA receptor activity. Calcium influx (`ICa`) is calculated based on the conductance of NMDA receptors and the driving force of calcium. Calcium levels affect synaptic plasticity mechanisms modeled in the code. ### Synaptic Plasticity 1. **Spike-Timing Dependent Plasticity (STDP):** - **Synaptic Weight Modification:** The process of synaptic weight change is influenced by the intracellular calcium concentration (`capoolcon`) and uses functions like `eta` and `omega` to determine potentiation or depression of synaptic weights. - **Learning Rules:** Parameters `lambda1` and `lambda2` control the degree of potentiation and depression, respectively. Synaptic changes depend on calcium levels crossing certain thresholds (`threshold1`, `threshold2`), consistent with STDP. 2. **Facilitation and Depression:** - The code models short-term synaptic plasticity through facilitation and depression mechanisms. Variables like `F` (facilitation) and `D1`, `D2` (depression due to vesicle depletion) are updated based on recent synaptic activity, impacting the efficacy of neurotransmitter release and receptor binding. ### Specialized Features 1. **Local Calcium Pools:** - The code uses a local calcium pool model (`capoolcon`) with parameters like `Cainf`, `pooldiam` to simulate calcium ion dynamics in a confined space, reflecting the complex internal cellular environments. 2. **Biological Constants:** - Physical constants like `FARADAY` (Faraday's constant) and `eca` (equilibrium potential for calcium) are used to compute ionic currents and dynamics, reflecting real-world biological and physical constraints. ### Randomness and External Influences 1. **Random Synaptic Activation:** - A uniform random function (`unirand()`) is employed to simulate variability in synaptic transmission, mirroring the stochastic nature of neuronal firing and neurotransmitter release. 2. **Gap Junction Modulation:** - The code includes a function `GAP1`, representing time-dependent modulation of synaptic properties, potentially mimicking metabolic or hormonal influences on synaptic weights. In summary, this code provides a detailed model of synaptic interactions between pyramidal neurons, emphasizing the roles of AMPA and NMDA receptor dynamics and calcium-mediated synaptic plasticity mechanisms, corresponding to biologically observed phenomena underlying neural communication and learning in the brain.