The provided code models the electrophysiological properties of neurons, focusing specifically on their threshold electrotonus (TE). This involves simulating how neurons respond to electrical stimuli, particularly regarding how their excitability is modulated over time with varying stimulus parameters. The central biological components relevant to this code include: ### Key Biological Concepts 1. **Threshold Electrotonus (TE):** - TE assesses the excitability of a neuron during a prolonged subthreshold current pulse. It's a measure of how a neuron's membrane potential changes in response to an applied current over time. The code is used to compare the TE of two different neuronal models. 2. **Subthreshold Current Pulses:** - These are small electrical stimuli that don't trigger an action potential. The response to such stimuli provides insights into the passive membrane properties and the initial stages of neuronal excitability. 3. **Electrotonic Properties:** - These refer to the passive spread of electrical signals within the neuronal membrane. This property is crucial for understanding how signals attenuate as they travel through neurons, affecting the potential for action potential initiation. 4. **Excitability Matching:** - The main function of the code is to adjust the parameters of model M2 such that its threshold electrotonus matches that of model M1. This involves optimizing certain parameters, likely related to how quickly ions move across the neuron's membrane in response to an electrical stimulus. ### Key Aspects of the Code - **Pulse Input (`pulse` and `setDC`):** The code generates an input pulse and sets a direct current component, reflecting how stimuli can be configured in biological experiments to probe neuronal properties. - **Excitation Function:** The code's `excitation` function computes how much input is required to reach excitation (albeit subthreshold) in the neuronal model M2, highlighting the dynamic range of input that can affect neuronal behavior. - **Optimization Process:** Utilizing a fitting procedure (`FMINBND`), the script adjusts parameters to minimize the difference in electrotonic responses between two models (quantified as `err`). This mimics biological procedures where ionic conductances might be adjusted experimentally to observe changes in neuronal excitability. ### Biological Relevance This script is part of a computational investigation of neuronal excitability. By comparing and adjusting two models of neuron electrotonus, researchers can better understand how electrical properties are related to underlying channels and membrane dynamics. Such studies can contribute insights into how specific ion channels or structural components of neurons influence their firing patterns, potentially providing information relevant to neurological conditions characterized by altered excitability. Overall, the focus is on the nuances of neuronal behavior in response to controlled electrical inputs, a foundational aspect of neurophysiological research.