The provided code models synaptic transmission in a computational neuroscience simulation. It focuses on the creation and characterization of synapses, specifically chemical synapses, including a detailed representation of NMDA receptor-mediated synaptic transmission. Here’s a breakdown of the biological components modeled in the code: ### Synapses and Receptors 1. **Chemical Synapses**: Synapses are specialized junctions through which neurons signal to each other and to non-neuronal cells, forming the basis of neuronal circuits. The code includes functions to create different synaptic types, highlighting the biochemical and biophysical processes involved in synaptic transmission. 2. **NMDA Receptors**: - **Receptor Channels**: The code explicitly defines the creation of NMDA receptors, a subtype of glutamate receptor that plays a key role in synaptic plasticity, learning, and memory. - **Magnesium Block**: The `Mg_block` represents the voltage-dependent magnesium block characteristic of NMDA receptors. This block prevents ionic current through the receptor channel at resting membrane potential and is relieved upon depolarization. - **Key Parameters**: - `NMDA_CMG`, `NMDA_ETA`, and `NMDA_GAMMA` parameters model aspects of NMDA receptor kinetics and the blocking/unblocking patterns by magnesium ions. ### Synaptic Dynamics 1. **Channel Conductance**: - `gmax`: Represents the peak channel conductance, indicative of the maximum possible flow of ions through the receptor channels when fully activated. 2. **Reversal Potential**: - `revpot` (reversal potential) refers to the membrane potential at which there is no net flow of ions through the receptor's ion channel, a critical aspect of determining synaptic efficacy and neurotransmitter effects. 3. **Time Constants**: - `tau1` and `tau2` are synaptic time constants that help shape the temporal profile of synaptic currents, accounting for rise and decay phases, which influence how signals are integrated temporally by postsynaptic neurons. ### Synaptic Connectivity and Dynamics 1. **Connection Mechanisms**: - `connect_synapse`: The function models the physical and functional connection between pre- and post-synaptic neurons, incorporating parameters like `tdelay` (synaptic delay) and `weight` (synaptic weight), essential for signaling propagation and synaptic strength. 2. **Gaussian Fall-off Modeling**: - **Spatial Dependency**: The `gauss` function implies a spatial aspect of synaptic strength that decreases with distance, modeling how synaptic coupling can spatially decay in biological systems. This is critical for simulating the spatial organization of neurons and synapse efficacy depending on their locations. Together, these components create a simplified yet biologically relevant model of how neurons communicate across synaptic junctions, particularly emphasizing the role of NMDA receptors and the spatial-temporal dynamics critical for brain function.