The provided code models gap junctions, which are specialized intercellular connections that facilitate direct electrical communication between neuron pairs. These gap junctions allow the passage of ions and small molecules, contributing to the synchronization of neuronal activities across connected cells. ### Biological Basis Gap junctions are formed by connexin proteins that create channels between adjacent neurons. These conduits are crucial for certain fast and synchronized neuronal activities. They are less selective than chemical synapses, as they allow the direct transfer of ionic currents and, subsequently, changes in membrane potential between neurons. ### Key Biological Components in the Code 1. **Gap Junctions (`gap`)**: The code creates gap junction objects (`g[0]` and `g[1]`) that link two sets of dendritic sections (`dend4a` and `dend4b`) from different neurons. The gap junctions are critical for mediating the electrical coupling between the two neurons. 2. **Dendritic Locations**: The code specifies different locations on dendrites (`dend4a` and `dend4b`) that approximate distal and proximal regions and are connected via gap junctions. This reflects how dendritic architecture influences signal transmission and integration in neuronal circuits. 3. **Electrical Resistance (`r`)**: The model includes different resistances for gap junctions, ranging from high to low values, representing different levels of conductance (or potential for current flow). These values imitate varying physiological strengths of gap junction coupling. 4. **Recording of Membrane Potentials**: The recording vectors (`cella_soma_vec`, `cella_prox_vec`, etc.) capture voltage changes at different neuronal and dendritic locations, suggesting interest in how synaptic inputs (or intrinsic activities) propagate within and between neurons through gap junctions. 5. **Stimulation**: The introduction of current injections (`IClamp`) simulates neuronal activity by depolarizing neuron segments (soma locations) to observe how current flows across the neurons connected by gap junctions. ### Conclusion Overall, the code simulates the dynamic behavior of two neurons coupled through gap junctions, focusing on how changes in electrical coupling (resistance) affect the transmission of membrane potentials from one neuron to another. The varying resistances (or conductances) allow exploration of the influence of gap junction strength on neuronal synchronization and communication. This kind of model helps elucidate the role of electrical synapses in neural function and information processing.