Orangutan: A Multiscale Brain Emulation-Based Artificial Intelligence Framework for Dynamic Environments

• URL: http://arxiv.org/abs/2406.15488v1
• Date: Tue, 18 Jun 2024 01:41:57 GMT
• Title: Orangutan: A Multiscale Brain Emulation-Based Artificial Intelligence Framework for Dynamic Environments
• Authors: Yong Xie,
• Abstract summary: This paper introduces a novel brain-inspired AI framework, Orangutan. It simulates the structure and computational mechanisms of biological brains on multiple scales. I have developed a sensorimotor model that simulates human saccadic eye movements during object observation.
• Score: 2.8137865669570297
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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