# Biological Basis of the Synaptic Model Code The provided code models an updated dual-component synapse consisting of AMPA and NMDA receptor channels, with a specific focus on NMDA receptors that are subject to magnesium (Mg²⁺) block. This model is inspired by synaptic channels observed in striatal medium spiny neurons, drawing from literature references by Du et al., 2017; Chapman et al., 2003; and Ding et al., 2008. ## Key Biological Components ### 1. **Synaptic Receptors:** - **AMPA Receptors:** These receptors mediate fast synaptic transmission. The code models them with two exponential processes characterized by rise (`tau1_ampa`) and decay (`tau2_ampa`) time constants. - **NMDA Receptors:** These receptors are involved in slower synaptic transmission and are modulated by magnesium block. They also have rise (`tau1_nmda`) and decay (`tau2_nmda`) time constants, but these are significantly longer than those for AMPA, reflecting the slower kinetics of NMDA receptor channels. ### 2. **Magnesium Block of NMDA Receptors:** - NMDA receptors are subject to voltage-dependent blockade by extracellular Mg²⁺. At resting membrane potential, Mg²⁺ ions block NMDA receptors, preventing the flow of ions. The function `MgBlock()` implements this voltage-dependence, where depolarization reduces the block and allows the flow of ions, mainly Ca²⁺ and Na⁺. ### 3. **Calcium Dynamics:** - NMDA receptors allow Ca²⁺ influx, which is crucial for synaptic plasticity mechanisms such as long-term potentiation (LTP) and long-term depression (LTD). The variable `ica_nmda` reflects the calcium current through NMDA receptors, accounting for a fraction of the total NMDA-mediated current (`nmda_ca_fraction`). ### 4. **Synaptic Plasticity:** - The model includes mechanisms for activity-dependent synaptic plasticity influenced by intracellular calcium concentrations and dopamine signaling. - **LTP and LTD:** The processes of LTP and LTD are modeled by the functions `pind_LTP` and `pind_LTD`, which depend on calcium concentrations surpassing specific thresholds (`thresh_LTP` and `thresh_LTD`). Dopaminergic modulation (`last_dopamine`) can lead to weight increments or decrements, mimicking synaptic strength increases (LTP) or decreases (LTD). ### 5. **Dopaminergic Modulation:** - Dopamine, a critical neuromodulator in the striatum, influences synaptic plasticity. The code uses a pointer `dopamine` to represent this modulation's influence on synaptic weight changes in learning rules. ### 6. **Weighted Synaptic Response:** - The synaptic conductance and the resultant current are scaled by `weight`, which represents the efficacy or strength of the synaptic connection. It initializes to `w0` and varies with synaptic activity and dopaminergic influence, bounded by `wmax` and `wmin`. ## Conclusion This code implements a computational model of a glutamatergic synapse with both AMPA and NMDA components. The model captures key biophysical properties such as fast/slow synaptic transmission through AMPA/NMDA receptors, magnesium block of NMDA receptors, calcium-mediated synaptic plasticity, and dopaminergic modulation, especially relevant in the context of medium spiny neurons in the striatum.