The code represents a simple computational model of passive ion leak currents across a neuronal membrane. It specifically models the passive movement of sodium (\(Na^+\)) and potassium (\(K^+\)) ions, which are crucial for maintaining the resting membrane potential of neurons in biological systems. ### Biological Basis: 1. **Ion Channels:** - The code models passive leak currents for sodium (Na) and potassium (K) ions. These ions contribute to the resting potential, which is essential for the excitability of neurons. - It utilizes the Nernst potentials (`ek` for potassium and `ena` for sodium) to calculate the driving force for ion movement across the membrane. 2. **Passive Currents:** - The overall intention is to simulate the leak channels that allow ions to passively diffuse across the cell membrane without the input of energy. Leak channels are critical in stabilizing the membrane potential near a specific value, typically close to the equilibrium potential of potassium due to its dominant permeability. 3. **Conductance Parameters:** - `gbar` signifies the overall conductance of the leak pathways in the membrane, and it determines the extent to which ions can passively move across the membrane. - `epas` is analogous to the passive resting potential that the neuron tends to have in the absence of input, typically slightly more hyperpolarized than the threshold for action potential initiation. 4. **Relative Ion Permeabilities:** - The code calculates the separate conductances for sodium and potassium (`gna_p` and `gk_p`, respectively) based on their respective contribution to the leak current using the Goldman equation principle. This reflects the higher permeability of potassium relative to sodium in resting membranes. 5. **Membrane Current Components:** - `ina` and `ik` represent the passive currents for sodium and potassium, respectively. These currents, when combined (`i`), describe the total leak current flowing across the membrane, thereby influencing the resting membrane potential. This part of a computational model highlights the fundamental concepts of ion permeability and membrane potential maintenance in neurons, which are crucial for their function in generating electrical signals. By modeling these passive leak currents, the code aims to mimic one of the key background processes underlying neuronal excitability and homeostasis.