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A multiscale brain emulation-based artificial intelligence framework for dynamic environments
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AI
DOI：10.1038/s41598-025-01431-2.png)
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Abstract
En 中文
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.
Keywords:
NEURAL-NETWORKS
FUNCTIONAL ARCHITECTURE
RECEPTIVE-FIELDS
VISUAL-CORTEX
SHIFTS
INFORMATION
SELECTIVITY
ATTENTION
SYSTEMS
SEARCH