The provided code models the role of gap junctions in neuronal networks, focusing on their biological basis. ### Biological Basis #### Gap Junctions In mammals, gap junctions are specialized intercellular connections that facilitate direct electrical and chemical communication between neighboring cells. In the context of neuroscience, they are crucial for synchronizing activities in a network of neurons. Structurally, these junctions are composed of connexin proteins that form channels allowing ions and small molecules to pass directly from the cytoplasm of one neuron to another. #### Function in Neuronal Networks Gap junctions enable the passage of ionic currents and small signaling molecules between neurons, contributing to the rapid transmission of electrical signals and the synchronization of neuronal oscillations. This direct electrical coupling is significant in various processes, including rhythmic activities in the brain, development of the nervous system, and more. ### Relevance to the Code 1. **Conductance (`g_gj`)**: The code uses a variable `g_gj` to represent the conductance of the gap junctions. This parameter is a measure of how easily ions (and thus electrical current) can flow through the gap junctions, heavily influencing the level of electrical coupling between neurons. 2. **Network Setup (`gap_junction_setup`)**: The code includes a setup function that initializes the gap junction network, storing the connection pattern and conductance value for each neuron. Importantly, the `conn` array denotes the connectivity of the neurons via gap junctions, reflecting how biological neurons are interconnected. 3. **Voltage Handling (`pac`)**: The use of the `pac` array suggests that the model is concerned with the penultimate axonal compartment's voltage of each neuron. This is relevant as it implies the model is considering the variation of membrane potentials across neurons, crucial for electrical signal propagation. 4. **Current Calculation (`gap_junction_current`)**: The function `gap_junction_current` computes the current flowing between connected neurons via gap junctions. This is biologically relevant as the current dictates how synchrony and signal propagation are achieved across the network of neurons. In summary, the code focuses on modeling how gap junctions contribute to neuronal communication and synchronization through the spread of ionic currents, thereby mimicking biological interactions and electrical coupling present in neuronal tissue. This model is integral for understanding complex brain dynamics such as oscillatory behavior and network synchronization.