The provided code is a computational model simulating a synaptic transmission mechanism, specifically focusing on the dynamics of AMPA receptor-mediated synaptic currents. This model is characteristic of excitatory neurotransmission usually involving the release of glutamate at synapses, and it also includes a diffusion process. ### Biological Basis 1. **Synaptic Transmission**: - **Point Process**: The model defines a `POINT_PROCESS` named `GRANULE_Ampa_det_vi`, indicating its focus on local synaptic events such as neurotransmitter release and receptor dynamics. - **AMPA Receptors**: AMPA receptors are glutamate-gated ion channels responsible for fast excitatory postsynaptic potentials (EPSPs). They mediate synaptic currents when glutamate binds, leading to cation (e.g., Na⁺, K⁺, and sometimes Ca²⁺) flow. 2. **Receptor Kinetics**: - **State Variables**: The model uses states `C`, `O`, and `D` likely representing Closed, Open, and Desensitized states of the AMPA receptor, respectively, reflecting the typical behavior of ligand-gated ion channels. - **Kinetics**: Transition rates between these states (`r1`, `r2`, `r5`, `r6`) are influenced by the presence of glutamate, modeled by the `Trelease` variable which represents the concentration of neurotransmitter available for receptor binding. 3. **Temperature Effect**: - **Q10 Values**: The model incorporates temperature sensitivity using Q10 coefficients for diffusion and channel kinetics (`Q10_diff`, `Q10_channel`). These coefficients adjust reaction rates based on temperature changes, representing the biological fact that many biochemical processes are temperature-dependent. 4. **Synaptic Conductance**: - **Conductance Calculation**: The conductance `g` is calculated as a function of the number of open receptors (`O` state), modulated by `gmax`, the maximum conductance of the synapse. This reflects how the synaptic current (i) is driven by the potential difference `(v - Erev)` where `Erev` is the reversal potential for the AMPA receptor, typically around 0 mV. 5. **Presynaptic Dynamics**: - **Short-term Plasticity**: Variables like `tau_rec`, `tau_facil`, and `U` are involved in short-term plasticity mechanisms, simulating dynamic adjustments in neurotransmitter release probability during repeated synaptic activation. - **Facilitation and Depression**: These parameters capture the phenomena of synaptic facilitation and depression, where increased frequency of stimuli can modify synaptic strength transiently. 6. **Neurotransmitter Diffusion**: - **Diffusion Model**: The `diffusione()` function models the spread of neurotransmitter molecules from the presynaptic release site. It uses parameters like `Diff` (diffusion coefficient), `Rd` (release site radius), and `M` (number of molecules), simulating how neurotransmitter concentration decays over time and distance in the synaptic cleft. 7. **Glutamate Release and Detection**: - **Release Dynamics**: The `NET_RECEIVE` block describes how spikes trigger neurotransmitter release (T is set to `Tmax*y`) and the subsequent detection at the postsynaptic site. - **Integration with Spikes**: An array `tspike` is used to store spike times, and mechanisms ensure neurotransmitter availability tightly follows the presynaptic spike train for synaptic modulation. Overall, this code represents a detailed mechanistic model of AMPA receptor dynamics and synaptic transmission, incorporating elements such as short-term synaptic plasticity and neurotransmitter diffusion influenced by synaptic activity patterns.