Orangutan: A Multiscale Brain Emulation-Based Artificial Intelligence Framework for Dynamic Environments
This work addresses the problem of developing AGI for dynamic environments, but it appears incremental as it builds on existing brain-inspired computing approaches without clear broad breakthroughs.
The paper tackles the challenge of achieving AGI by introducing Orangutan, a brain-inspired AI framework that simulates biological brains across multiple scales, and demonstrates its efficacy with a sensorimotor model for saccadic eye movements tested on handwritten digit images.
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.