用计算模型证明做梦能随机激活记忆，促进学习与巩固。
A computational account of dreaming: learning and memory consolidation
- 构建脑内随机信号的计算模型，模拟梦境中记忆处理过程。
- 模拟结果显示随机信号可有效实现学习与记忆巩固。
- 适合对认知科学、神经机制感兴趣的读者。
许多研究认为做梦主要由随机内源信号引起，称其内容为‘随机冲动’，并认为梦眠对智力能力影响甚微。然而，大量功能研究显示，梦眠在学习及其他认知功能中扮演重要角色。近期研究指出，海马体在清醒后重现神经活动模式，支持梦眠在记忆巩固中的作用。随机性成为功能论与无用论之争的分歧点。本研究提出一个认知与计算模型，模拟梦境在学习与记忆巩固中的功能。模拟结果表明，随机信号可促成学习与记忆巩固。因此，做梦被解释为大脑清醒活动的延续，通过海马体自发、随机激活的信号进行处理。该模型特征与多项实证研究结论相符。
原文摘要 · Abstract (English)
A number of studies have concluded that dreaming is mostly caused by randomly arriving internal signals because "dream contents are random impulses", and argued that dream sleep is unlikely to play an important part in our intellectual capacity. On the contrary, numerous functional studies have revealed that dream sleep does play an important role in our learning and other intellectual functions. Specifically, recent studies have suggested the importance of dream sleep in memory consolidation, following the findings of neural replaying of recent waking patterns in the hippocampus. The randomness has been the hurdle that divides dream theories into either functional or functionless. This study presents a cognitive and computational model of dream process. This model is simulated to perform the functions of learning and memory consolidation, which are two most popular dream functions that have been proposed. The simulations demonstrate that random signals may result in learning and memory consolidation. Thus, dreaming is proposed as a continuation of brain's waking activities that processes signals activated spontaneously and randomly from the hippocampus. The characteristics of the model are discussed and found in agreement with many characteristics concluded from various empirical studies.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。