The provided code appears to simulate aspects of neuronal action potential propagation, specifically focusing on the propagation velocity across nodes of Ranvier in myelinated nerve fibers. This is indicated by several key elements within the code, with a notable focus on conduction velocity (CV) calculations, peak detection, and the structure and functionality of myelinated axons. ### Biological Basis: 1. **Action Potentials and Conduction:** - The code reads a dataset that likely contains voltage data across time for multiple nodes along axonal fibers or individual neuron models. The primary focus of the data processing is to detect action potentials (APs), calculate their propagation speed, and potentially characterize their shape near the nodes of Ranvier. - Action potentials are rapid changes in membrane potential that propagate along neurons, facilitating communication within the nervous system. The code's task appears to identify these APs and assess their timing and velocity of propagation. 2. **Nodes of Ranvier:** - Nodes of Ranvier are periodic gaps in the myelin sheath covering myelinated axons, enabling saltatory conduction. This type of conduction facilitates rapid transmission of electrical impulses by allowing APs to "jump" from one node to the next. - Conduction velocity (CV) is a critical measure for understanding how fast electrical signals travel along neurons. The code calculates CV between successive peaks of action potentials detected at these nodes, reflecting the speed of impulse propagation in the myelinated sections. 3. **Peak Detection and Threshold:** - The use of `findpeaks` suggests that the code identifies maximum values in the voltage signal, corresponding to the peak of action potentials. A threshold condition (`MinPeakHeight`) helps ensure that only significant voltage changes, indicative of real APs, are recognized. - The threshold of -20 mV suggests an examination of the repolarization phase of APs, offering insights into when the potential reaches a specific negative value during the AP's downward phase. 4. **Conduction Velocity (CV):** - CV is derived by calculating the time difference between successive AP peaks at different nodes and using this to compute the speed of signal transmission. The physio-anatomical properties determining this velocity often include the diameter of the axon, the presence of myelin, and the distance between nodes, some of which are implied by variables like `nodes_dist`. 5. **Polynomial Fitting for Baseline Estimation:** - The code utilizes polynomial fitting to estimate the time at which a specific voltage threshold (-20 mV) is achieved. This aids in determining precise timing of AP onset or specific phases of the AP, further contributing to accurate CV determination. ### Conclusion: Overall, this simulation aims to model the biophysical dynamics of AP propagation in set arrays of axonal nodes, emphasizing the precise measurement of CV. The detailed look at voltage thresholds, peak assessment, and spatial-temporal AP dynamics provides insight into the role played by myelination and node spacing in neural information transmission. Such simulations help in understanding disorders that affect myelinated fibers, like multiple sclerosis, and facilitate the design of biomimetic computational models.