Effects of critical neuronal properties on the Shannon information entropy of granular cells in the cerebellar network

Cerebellar neural networks can be applied to robotic arm control, unmanned aircraft system localization, and humanoid robots. Despite many applications of brain-inspired intelligence, the firing mechanism of granular cells in the cerebellum has not been studied. In brain-inspired intelligence, it is significant to study the firing mechanism of granular cells (GR), which are the only excitatory neurons and the most numerous neurons in the cerebellum. In this paper, the Shannon information entropy was put forward to measure the degree of disorder in the firing activities of granular cells. The effects of critical neuronal properties on granular cells' firing activities were studied. Given inhibitory synapses between granular cells and Golgi cells (GO), we examined the effects of membrane capacitance and firing time constant of granular cells and Golgi cells on the firing activities of granular cells. Then, we discussed the impact of the strength of the connection between GR and GO on the Shannon information entropy of GR. Through the above research, we concluded the effects of critical neuronal properties’ impact trends on the Shannon information entropy of GR. Appropriate adjustments of the above parameters can change the firing activities of granular cells and meet the specific needs of different neuronal networks. Then, the designed neuronal networks can be used in adaptive motor control systems, such as in robotic coordination and autonomous driving under complex environments.