计算噪音促进了在人工神经网络中的决策过程中对不确定性的零射击适应
Charles Findling1,2, Valentin Wyart1,3,4
1Laboratoire de Neurosciences Cognitives et Computationnelles, Institut National de la Santé et de la Recherche Médicale (Inserm), Paris, France.
Science advances
|October 30, 2024
概括
将噪音引入人工神经网络,提高了它们在不确定性下概括和做出决策的能力,反映了人类的认知适应能力. 这一发现表明神经可变性可能是智能行为的关键.
科学领域:
- 计算神经科学是一种计算神经科学.
- 人工智能的人工智能是人工智能.
- 认知科学是一种认知科学.
背景情况:
- 神经噪声通常被认为是对信息处理有害的.
- 人类在以目标为导向的行为中表现出了显著的适应能力,尽管神经的变化.
- 在不确定性下这种适应能力的认知基础尚未完全理解.
研究的目的:
- 调查计算噪声在人工神经网络 (ANN) 中的作用.
- 确定噪音是否可以促进ANN中不确定性下的概括和决策.
- 将ANN行为与人类在类似任务中的表现进行比较.
主要方法:
- 在ANN中实施中等水平的计算噪声.
- 在决策任务上培训ANN.
- 评估ANN在涉及不确定性,概率推理和逆向学习的未见问题上的表现.
- 将ANN行为与人类参与者的表现进行比较.
主要成果:
- 在ANN中适度的噪音显著改善了在不确定性下决策的零射击概括.
- 杂的ANN在不确定的条件下表现出类似于人类参与者的行为模式.
- 噪声在训练期间充当"结构"调节器,在训练后充当网络动态的"功能"调节器.
结论:
- 在ANN中的计算噪声可以在不确定性下促进适应性决策.
- 神经变异性可能是人类一般智力和适应能力的关键机制.
- 这项研究挑战了将噪音视为纯粹有害的观点,突出了其在认知系统中的潜在益处.
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