# Biological Basis of the Code The provided code is focused on simulating and visualizing the activity of different neuronal cell types in the cerebellar cortex. This includes mossy fibers, Golgi cells, granule cells, and stellate cells. The cerebellum is a critical part of the brain involved in motor control and coordination. The activity and interactions of these neurons are essential for understanding cerebellar function. ## Mossy Fibers Mossy fibers are one of the primary excitatory inputs to the cerebellum. They originate from various sources, such as the spinal cord and brainstem nuclei, and transmit sensory and proprioceptive information. In this model, their spikes are plotted to visualize their firing over time, reflecting their role in conveying input signals to the cerebellar cortex. ## Golgi Cells Golgi cells are inhibitory interneurons found within the granule cell layer of the cerebellum. They play a key role in regulating the excitability of granule cells by providing feedback inhibition. The code visualizes the membrane potential (`Vm`) of Golgi cells, which is important for understanding their inhibitory modulating functions on granule cells. ## Granule Cells Granule cells are the most numerous neurons in the brain and are located in the granule cell layer of the cerebellum. They receive input from mossy fibers and project their axons as parallel fibers, which synapse onto the dendrites of Purkinje cells and other interneurons. Monitoring their membrane potential is essential for studying how they integrate sensory input and contribute to cerebellar output. ## Stellate Cells Stellate cells are inhibitory interneurons located in the molecular layer of the cerebellar cortex. They provide lateral inhibition to Purkinje cell dendrites, helping to refine and shape the spatial and temporal patterns of Purkinje cell firing. The code captures their membrane potential dynamics, which highlights their role in fine-tuning cerebellar output through inhibition. ## Summary Overall, this code is designed to simulate the interactions and dynamics of key cerebellar neuron types, focusing on the visualization of their electrical activity. Such models are crucial for exploring the cerebellar contributions to motor control and learning by examining the cellular mechanisms that underpin these processes. The inclusion of variable graph dimensions and attributes highlights the importance of visualizing neuronal data in concert with their physiological properties.