# Biological Basis of the Computational Model The provided code is a computational model used to simulate and study the electrophysiological behavior of two hypothetical neuronal cells, referred to as "Richy" (Cell 1) and "Starfish" (Cell 2). The focus of the model is on computationally understanding the intrinsic properties of these neurons, relating to how they respond to electrical stimuli and maintain membrane potentials. The relevant biological aspects of the model are as follows: ## Neuronal Cell Types - **Richy (Cell 1) and Starfish (Cell 2):** The code is structured to handle two specific neuronal cell types. The choice between these cells dictates which configuration files are loaded, each corresponding to different properties for membrane conductances and initial conditions. ## Membrane Potential and Ion Channels - **Target Membrane Potential (`target_Vm`):** The model attempts to fit the holding current to bring the membrane potential of the neuron to a specific target value, set at approximately \(-73.8 \, \text{mV}\). This target value simulates a physiological resting membrane potential that is typical for many neurons. - **Active Membrane Properties:** The inclusion of files such as `active_mods.hoc` and `param_active_cond.hoc` suggests that the model incorporates active properties, likely involving voltage-gated ion channels. These are crucial for simulating the dynamic changes in membrane potential when responding to stimuli. ## Conductance and Current Injection - **Maximum Conductance Densities:** The code attempts to set conductance densities based on command-line arguments. This method allows for exploration of various conductance configurations to see how they affect cell behavior. - **Holding Current (`ihold`):** The code adapts the model to inject a holding current to mimic the biological process of maintaining a stable membrane potential despite inherent cellular noise and depolarizing influences. - **Step Current Protocols:** The model uses current clamps (e.g., `holdclamp` and `injclamp`) to either stabilize membrane potential or exploit dynamic properties, indicative of typical experimental current injection protocols. ## Spike Threshold and Resistance - **Spiking Behavior:** The initial portion of the code is dedicated to determining whether the neuron will spike in response to its intrinsic properties and the injected currents. The introduction of a spiking threshold (\(-10 \, \text{mV}\)) identifies the model's capacity to engage in action potentials, a fundamental physiological property of neurons. - **Input Resistance Calculation:** The code calculates the direct current (DC) input resistance, a measure of the neuron's responsiveness to injected currents, reflecting the neuronal membrane's passive properties. ## Use of Numerical Methods - **Fixed and Variable Time Steps:** The model distinguishes between phases where variable time steps are used to detect spiking (high temporal resolution) and where fixed time steps are used for average membrane potential calculations (stability over long periods), reflecting dynamic and static states of neurons. ## Integration and Continuity - **Automatic Refitting:** Automatic adjustment of holding current (refitting) following parameter changes is a mechanism that ensures model stability and accuracy, akin to biological homeostatic mechanisms that neurons utilize to stabilize their internal environments. In summary, the code represents a detailed simulation of neuronal behavior, focusing on how specific cellular properties contribute to overall electrophysiological characteristics. Its primary aim is to emulate biological processes such as resting potential maintenance, spiking behavior, and the effects of ionic conductances, thereby providing insights into the relationship between neuronal biophysics and behavior.