Orangutan: A Multiscale Brain Emulation-Based Artificial Intelligence Framework for Dynamic Environments
<|reference_start|>Orangutan: A Multiscale Brain Emulation-Based Artificial Intelligence Framework for Dynamic Environments: 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.<|reference_end|>
@article{xie2024orangutan:,
    title={Orangutan: A Multiscale Brain Emulation-Based Artificial Intelligence
  Framework for Dynamic Environments},
    author={Yong Xie},
    journal={arXiv preprint arXiv:2406.15488},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.15488},
    primaryClass={q-bio.NC cs.AI}
}