The provided code implements a computational model of neuronal activity by introducing active conductances, which are primarily responsible for generating action potentials, into different compartments of a neuron. This is reflective of how real neurons operate at a biological level, focusing particularly on ion channels and their distributions across the soma, dendrites, and axon. ### Biological Basis 1. **Ion Channels and Conductances:** - **Fast Sodium (Na+) Channels:** - The code specifies a conductance for fast sodium channels (`gnabar`). These channels are crucial for the rapid depolarization phase of the action potential. In neurons, Na+ channels open in response to a threshold voltage, allowing Na+ ions to flood into the cell and drive the membrane potential towards a positive value. - **Potassium (K+) Delayed Rectifier Channels:** - Represented by `gkbar`, these channels contribute to repolarization and hyperpolarization phases of the action potential. They open later than Na+ channels and allow K+ ions to exit the cell, bringing the membrane potential back towards its resting state. - **Leak Channels:** - The `gl` variable represents nonspecific ionic conductance, which allows ions to passively cross membranes and maintain the resting membrane potential. In this model, the ll value is set to 0, indicating these channels are not explicitly modeled here. 2. **Somatic, Axonal, and Dendritic Compartmentalization:** - **Soma:** The model specifies ion channel densities for the soma, which is the main location for integrating synaptic inputs and generating action potentials. - **Axon:** Conductances here are significantly higher for Na+ and K+ channels compared to the soma and dendrites, reflecting the biological reality that action potentials are often initiated in the axon hillock and propagate down the axon. - **Dendrites:** Dendritic ion channel conductances are adjustable; dendrites play key roles in receiving synaptic inputs and can contribute to local dendritic spikes that may influence somatic action potentials. 3. **Compartmental Analysis:** - The code employs a framework for distinguishing between different neuronal compartments, allowing for variation in the distribution and density of ion channels. This reflects the heterogeneous nature of real neuronal membranes, where different segments can have distinct functional roles and ion channel compositions. - **Cutoff Distances** (`active_apical_cutoff` and `active_basal_cutoff`) enable the selective activation of conductances depending on their distance from the soma, mimicking dendritic attenuation and compartmentalization found in biological neurons. 4. **Hodgkin-Huxley Dynamics:** - The model incorporates Hodgkin-Huxley type dynamics (`hh3`), which are a mathematical representation of action potential initiation and propagation in neurons through voltage-gated ion channels. This aligns with classical models of neuronal excitability. In summary, the code models the differential distribution and activity of ion channels across neuronal compartments, focusing on the key biological processes of action potential generation and propagation. It captures the complexity of neuronal signaling by varying conductances across the soma, axon, and dendrites, which in turn reflects the compartmentalized nature of ion channel expression in biological neurons.