Artificial Intelligence in Cerebellum Activation - A. Mohamed Sikkander; Rajeev Ranjan; Sangeeta R Mishra (International Journal of Cheminformatics, 2024)
Neuroscience plays a significant function during the progression of artificial intelligence. It provided inspiration for the development of human-like AI. There are two ways that neuroscience encourages us to develop AI systems. Neural networks that replicate human cognition and those that match the structure of the brain are the two objectives. Neural networks, which draw inspiration from the architecture of the human brain, are the engine behind contemporary artificial intelligence systems. Creating and using algorithms that mimic the neural systems present in the human brain is the aim of contemporary AI research. The cerebellum's short- and long-term motor memory formation, the learning of gains in ocular reflex, and the timing of eye blink conditioning are all explained by the liquid-state machine (LSM) model. It integrates the Golgi cells—granule cells—random inhibitory recurrent neural network to a basic perception. The LSM model now incorporates the cerebellar internal model, which supports cognitive function and voluntary movement control. The present state of artificial intelligence (AI) is called deep learning, and it is based on neural network models that began with simple observation. It is thought that the cerebellum is the source of modern artificial intelligence (AI), since the LSM model of the cerebellum represents the brain's implementation of deep learning.
Keywords: Artificial Intelligence (AI); Brain function; Cognitive science; Cortex; Little brain; Motor learning