神经动力计算在智能系统的信息边界处
Joseph D Monaco1, Grace M Hwang2
1Dept of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD USA.
Cognitive computation
|August 12, 2024
概括
生物智能对人工智能来说仍然是难以捉摸的. 这种观点建议从脑即计算机模型转向动态系统方法,整合体现认知和感知控制理论,以获得对神经计算的新理解.
科学领域:
- 神经科学是一个神经科学.
- 认知科学 认知科学
- 人工智能的人工智能
背景情况:
- 当前的人工智能 (AI) 模型缺乏生物智能的关键特征.
- 认知主义的大脑即计算机理论存在方法论和认识论方面的偏见.
- 现有的框架无法完全捕捉神经机制的复杂性.
研究的目的:
- 挑战现有的AI和认知科学范式.
- 为理解生物智能提出一个替代框架.
- 为了弥合计算神经科学中的理论差距.
主要方法:
- 合成智能系统的历史方法.
- 集成动态系统理论和感知控制理论.
- 将细胞组件重新构想为重新进入的动态流.
主要成果:
- 在认知论和脑为计算机理论中发现了局限性.
- 提出的细胞组合作为最小的超神经元组织水平.
- 突出了体现和情境嵌入在神经计算中的作用.
结论:
- 对人工智能和神经科学来说,转向体内认知和动态系统是必要的.
- 回流动的动态流 (细胞组件) 为计算提供了一个神经动力学基础层.
- 这种综合方法可以克服人工智能研究中的神经象征性局.
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