# Biological Basis of the Model The provided code is designed to analyze electrical activity in neural structures, specifically focusing on the activation of axons under electrical stimulation. This type of modeling is commonly used in computational neuroscience to study how nerves and axons respond to various input stimuli, which can have applications in neuroprosthetics and understanding nerve function. ## Key Biological Aspects ### 1. Axonal Activity and Action Potentials - **Action Potentials (APs):** The code processes electrical activity data to identify the occurrence of action potentials in different axons. Action potentials are rapid rises and falls in membrane voltage that are fundamental to neuronal signaling and communication. - **Voltage Threshold:** A critical aspect of the model is the determination of whether an axon fires an action potential based on its membrane voltage exceeding a threshold (`vcrit = 15.`). This reflects the biological concept of depolarization, where a neuron must reach a certain threshold to initiate an AP. ### 2. Neural Structure and Geometry - **Axon and Fascicle Topology:** The code utilizes geometrical data to determine the location of axons within nerve bundles or fascicles. This is important for understanding how different nerve fibers within a nerve are affected by electrical stimuli. - **Spatial Mapping:** By using coordinates (xx_, yy_, rr_), the code maps the spatial layout of axons, addressing how the anatomical structure impacts function. This reflects biological reality where axonal positioning within nerve bundles impacts their activation. ### 3. Electrical Stimulation and Recruitment - **Stimulation Protocol:** The code reads stimulation settings from an external file (`electrodes.json`) to mimic the effect of electrical stimulation on nerves. This is analogous to how electrical currents can be used to activate nerves in clinical settings, such as in deep brain stimulation or peripheral nerve stimulation. - **Recruitment Curves:** It generates recruitment data indicating how many axons are activated at different current levels. In biological terms, recruitment curves are important for understanding the graded response of nerves to increasing stimuli and are critical for applications like tuning stimulation parameters in neural prostheses. ### 4. Neural Modeling - **Temporal Dynamics:** The model includes temporal dynamics (`dt = 0.005`), which accounts for the time progression in action potentials and axonal firing. Time resolution is important in action potential modeling as it reflects neuronal responsiveness over time. - **Local Maxima in Voltage Data:** `argrelextrema` is used to find local maxima in voltage recordings, which correlates with identifying peak times of action potentials, reflecting the transient nature of rapid depolarizations during nerve signaling. ### 5. Data Processing and Analysis - **Result Compilation:** The model aggregates data on action potential timings, which convey latency information that is vital for understanding how quickly a nerve fiber responds following stimulation. - **Fired Axons Calculation:** Determining which axons fired (using `hasAP` dictionary) gives insight into the distribution of excitability among nerve fibers within a fascicle. Overall, this model captures key aspects of neural conduction and response to electrical stimulation, reflecting complex interplays between anatomical geometry, electrical properties of the neural tissue, and stimulation parameters. This type of model is instrumental in bridging the gap between physiological experiments and the development of biomedical devices.