The code provided simulates a computational model rooted in the dynamics of neuronal action potentials and channels associated with neuronal excitability. The biological basis of this code is primarily connected to the Hodgkin-Huxley (HHS) model, a well-known mathematical framework used to describe how action potentials in neurons are initiated and propagated through the dynamics of ion channels. ### Key Biological Concepts Modeled 1. **Ion Channels**: The model simulates the behavior of ion channels, which are crucial for the generation and propagation of action potentials (APs). Specifically, it considers a large number of channels (e.g., sodium, potassium) characterized by their states of 'available' or 'inactive,' which influence the neuronal excitability. 2. **Action Potentials (APs)**: The code models the generation of action potentials, which are rapid changes in membrane potential that constitute the signaling mechanism in neurons. An AP occurs if a sufficient number of channels transition to an 'available' state, contributing to threshold crossing. 3. **Stimulation and Timing**: The simulation includes periodic stimulation, reflecting experimental conditions where neurons might be repeatedly stimulated to evoke APs. Parameters such as stimulation frequency (e.g., 20 Hz) and cycle time (`T_TIME`) are key aspects of this part of the model. 4. **Probabilistic Channel Dynamics**: The probabilistic transitions of channels between states are influenced by parameters like `gamma_p`, `gamma_m`, and `gamma_0`, which represent different rates corresponding to various periods (e.g., during AP, rest, or stimulation). The probabilities (`P_I`, `P_A`) determine the likelihood of a channel becoming inactivated or available after stimulation. 5. **Excitability Map**: This term refers to the model's capability to predict when an AP can be generated, based on the probabilistic configuration of channels and their modulation by external inputs. The `prob` function embodies a form of an excitability map, determining the likelihood of firing based on channel states. 6. **Channel Population and Diversity**: The model accounts for variability across channel types (`M` represents the number of channel types), and `N_v` denotes the number of channels of each type. This reflects the diversity found in neuronal membranes where different channels contribute variably to action potential generation. Ultimately, the code simulates the neuronal response to periodic stimulations in terms of AP generation, grounded in the stochastic dynamics of ion channel gating behavior. This approach bridges theoretical neuroscience and experimental practices by allowing researchers to understand how certain parameters and conditions affect neuronal excitability and signal transmission.