Biological Basis of the Computational Model

The provided code is a computational model aimed at understanding the action potential threshold in different neuron types by simulating the injection of current into a model neuron and observing when an action potential (AP) occurs. This model is focused on evaluating the electrophysiological properties of modeled neurons under varying conditions.

Key Biological Concepts

1. Action Potential (AP):

   • An action potential is a rapid rise and subsequent fall in voltage or membrane potential across a cellular membrane. It's a critical process in neuronal signaling, involving ion exchanges primarily through sodium (Na⁺) and potassium (K⁺) channels.

2. Neuron Types:

   • The code defines templates for different neuron types: WT-2005, R859C, and Mixed. These likely represent different genotypes or conditions under which cells were studied, with WT-2005 potentially being a wild-type reference and R859C being a mutant or altered form.

3. Ion Channels:

   • The model incorporates two specific ion channel templates, ichanWT2005 and ichanR859C1, which simulate the conductance properties of sodium and potassium channels in the neurons.
   • Parameters such as gnatbar, gkfbar, and gl represent the maximal conductances for the sodium (Na⁺) and potassium (K⁺) channels and the leak conductance, respectively.

4. Electrophysiological Properties:

   • Membrane Capacitance (cm): Represents the cell's ability to hold charge.
   • Axial Resistance (Ra): Reflects the internal resistance within the neuron, affecting current flow.
   • Reversal Potentials (enat and ekf): These set the driving force for the ionic currents through the channels.

5. Current Injection and AP Threshold:

   • The model assesses the AP threshold by varying the amplitude of injected current into the soma (the cell body of the neuron). The goal is to determine the minimal amount of current necessary to elicit an AP.
   • A IClamp (current clamp) device is simulated to inject current pulses into the neuron, with specific parameters like delay (stim.del), duration (stim.dur), and amplitude (stim.amp).

6. Simulation of Neuronal Response:

   • The model runs simulations with different current stimuli to emulate different experimental conditions. Each modeled neuron type might have unique properties to reflect, for instance, specific ion channel mutations or mixed channel properties.

Conclusion

Overall, the model simulates neuronal behavior in response to current stimulation, focusing on the minimal current required to reach an AP threshold. This is critical for understanding how specific genetic or phenotypic variations in ion channels affect neuronal excitability, and it provides insights relevant to both normal physiological conditions and pathophysiological conditions (such as mutations leading to altered excitability).