The provided code is modeling a passive membrane channel, which is a fundamental aspect of neuronal membrane physiology. This model aims to represent basic ion flow through non-gated (or "leak") channels in neuronal axons. Below is a description of the biological basis, focusing on key aspects relevant to the code: ### Biological Basis 1. **Passive Membrane Channels:** - **Non-Gated Channels:** The code models passive or "leak" channels, which are non-gated ion channels that allow ions to diffuse across the neuronal membrane based on their concentration gradients. Unlike voltage-gated or ligand-gated channels, these channels do not open or close in response to specific signals but remain continuously open. - **Ion Flow:** These channels primarily maintain the resting membrane potential and contribute to the membrane's electrical resistance. The flow of ions is described by Ohm's Law, relating the current (i), conductance (g), and the difference between the membrane potential (v) and the reversal potential (erev). 2. **Key Parameters:** - **Conductance (g):** It represents the channel's ability to pass ions; in this code, it is set to a value of 0.001 mho/cm², indicating a low but constant flow of ions, characteristic of a passive membrane. - **Reversal Potential (erev):** The reversal potential is set at -70 mV, which is typically close to the resting membrane potential for neurons. This parameter represents the potential at which there is no net flow of ions through the channel, dictating the direction and magnitude of ion flow across the membrane. 3. **Current Calculation:** - The `BREAKPOINT` block in the code uses the equation \( i = g \times (v - erev) \) to calculate the current flowing through the passive channels. This formulation directly models how the driving force (voltage difference from reversal potential) and channel conductance determine ionic current across the membrane. ### Implications - **Resting Membrane Potential:** Such passive channels contribute to setting and maintaining the resting membrane potential in neurons, which is crucial for keeping neurons responsive to input signals. - **Membrane Resistance:** They also influence membrane resistance and time constants, impacting how the membrane potential responds to synaptic inputs and how signals integrate over time and space within the neuron. Overall, this code represents a simplified model of passive ionic conductance, capturing the essential attributes of the neuronal membrane's passive properties.