Orangutan: A Multiscale Brain Emulation-Based Artificial Intelligence Framework for Dynamic Environments
Fuente:
arXiv
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866917701471961088 |
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| author | Xie, Yong |
| author_facet | Xie, Yong |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_15488 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Orangutan: A Multiscale Brain Emulation-Based Artificial Intelligence Framework for Dynamic Environments Xie, Yong Neurons and Cognition Artificial Intelligence 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. |
| title | Orangutan: A Multiscale Brain Emulation-Based Artificial Intelligence Framework for Dynamic Environments |
| topic | Neurons and Cognition Artificial Intelligence |
| url | https://arxiv.org/abs/2406.15488 |