在体现的体外神经网络中,结构化信息呈现过程中出现了关键的动态
Forough Habibollahi1,2,3, Brett J Kagan4, Anthony N Burkitt2
1Cortical Labs Pty Ltd, Melbourne, 3056, VIC, Australia.
Nature communications
|August 30, 2023
概括
神经网络在处理结构化的感官输入时表现出关键动态,提高任务性能. 然而,学习需要额外的反,而不仅仅是批判性.
科学领域:
- 神经科学是一个神经科学.
- 计算神经科学是一种神经科学.
- 人工智能的人工智能
背景情况:
- 神经关键性,大脑功能中的近关键状态,与认知和意识有关.
- 之前的研究在人类和动物模型中面临研究神经关键性的局限性.
- 了解神经关键性的出现和作用仍然是一个关键的挑战.
研究的目的:
- 研究神经网络中关键动态的出现.
- 探索关键动态和任务执行之间的关系.
- 为了确定神经网络中的学习是否有足够的批判性.
主要方法:
- 利用皮质神经元的体外神经网络.
- 训练网络玩一个简化的"Pong"游戏来演示合成生物智能 (SBI).
- 分析了在响应结构化的感官输入时出现的关键动态.
主要成果:
- 当神经网络接收与任务相关的结构化感官输入时,出现了关键的动态.
- 系统在收到结构化输入时重组到接近临界的状态.
- 提高任务性能与接近关键动态相关.
- 仅仅是批判性是不够的,没有后果的学习信息.
结论:
- 神经关键性成为结构化信息处理的基本特征.
- 结构化的感官输入驱动神经网络向关键状态.
- 在神经网络中学习需要的不仅仅是关键动态;它需要对行动的反.
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