Biological Basis of the Code

The code provided is part of a computational neuroscience model that simulates neuronal activity, focusing on the action potential (AP) dynamics within a neuron. Here's a breakdown of the biological aspects the code is likely modeling:

1. Ionic Conductances

The code features parameters related to ionic conductances, particularly for sodium (Na+) and potassium (K+) ions:

• Sodium Conductance (gNa): Parameters such as gna_soma, gna_prox_axon, and gna_distal_axon model the conductance of sodium channels in different parts of the neuron, such as the soma and various axonal segments. Sodium conductance plays a critical role in the depolarization phase of the action potential.

• Potassium Conductance (gK): gk_scale_axon, gk_scale_soma, gk_axon, and gk_soma are related to the conductance and kinetics of voltage-gated potassium channels, crucial for repolarizing the membrane potential following an action potential.

2. Neuronal Compartments

The code makes distinctions between different regions of a neuron:

• Soma: The cell body where the integration of incoming signals primarily occurs.
• Proximal and Distal Axon: Different segments of the axon where APs are propagated, with variable sodium channel densities to reflect the biological differences in conductance along the axon.

3. Action Potential Characteristics

Functions like maxRise, maxDecay, and t50 are used to analyze key characteristics of action potentials:

• Maximal Rise and Decay: These functions calculate the steepest slope during the rise and fall of an action potential, reflecting ion channel dynamics.

• Full Width at Half-Maximum (FWHM): t50 provides a measure of the duration of an action potential at half its peak height, indicating the temporal profile of the potential and kinetics of ionic currents.

4. Filtering and Frequency Analysis

The use of frequency filters like gaussian_filter suggests signal processing techniques to analyze action potentials, comparable to filtering biological signals in electrophysiology to identify meaningful components.

5. Gating Variables

The presence of gating suggests a model incorporating ion channel gating dynamics, likely voltage-dependent opening and closing of channels, which are fundamental for action potential initiation and propagation.

6. Temperature and Environment

The init_h function sets environmental parameters like temperature (T) and initial membrane potential (v_init), as these influence the biophysical properties of ionic channels.

7. Recursive Structure Analysis

With functions like find_longest_axon and rec_parent, the code explores the structure of the neuron, possibly modeling how the geometry affects signal propagation, consistent with real neurons having complex branching patterns.

8. Donnan Effect

The variable has_donnan suggests an adjustment for the Donnan equilibrium, which can influence ion distribution across the neuronal membrane due to charged macromolecules trapped inside the cell.

Conclusion

Overall, this code is modeling the electrical properties and dynamics of a neuron by simulating ionic conductances, spatial compartments, and signal characteristics, which are foundational for understanding neuronal excitability and action potential propagation.