  Achieving General Artificial Intelligence (AGI) has long been a grand
challenge in the field of AI, and brain-inspired computing is widely
acknowledged as one of the most promising approaches to realize this goal. This
paper introduces a novel brain-inspired AI framework, Orangutan. It simulates
the structure and computational mechanisms of biological brains on multiple
scales, encompassing multi-compartment neuron architectures, diverse synaptic
connection modalities, neural microcircuits, cortical columns, and brain
regions, as well as biochemical processes including facilitation, feedforward
inhibition, short-term potentiation, and short-term depression, all grounded in
solid neuroscience. Building upon these highly integrated brain-like
mechanisms, I have developed a sensorimotor model that simulates human saccadic
eye movements during object observation. The model's algorithmic efficacy was
validated through testing with the observation of handwritten digit images.
