Chen Zhe, Wilson Matthew A
Department of Psychiatry, Department of Neuroscience and Physiology, New York University School of Medicine, New York, NY 10016, USA.
Department of Brain and Cognitive Sciences, Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.
Trends Neurosci. 2017 May;40(5):260-275. doi: 10.1016/j.tins.2017.03.005. Epub 2017 Apr 5.
Memories of experiences are stored in the cerebral cortex. Sleep is critical for the consolidation of hippocampal memory of wake experiences into the neocortex. Understanding representations of neural codes of hippocampal-neocortical networks during sleep would reveal important circuit mechanisms in memory consolidation and provide novel insights into memory and dreams. Although sleep-associated ensemble spike activity has been investigated, identifying the content of memory in sleep remains challenging. Here we revisit important experimental findings on sleep-associated memory (i.e., neural activity patterns in sleep that reflect memory processing) and review computational approaches to the analysis of sleep-associated neural codes (SANCs). We focus on two analysis paradigms for sleep-associated memory and propose a new unsupervised learning framework ('memory first, meaning later') for unbiased assessment of SANCs.
经历的记忆存储在大脑皮层中。睡眠对于将海马体对清醒经历的记忆巩固到新皮层至关重要。了解睡眠期间海马体-新皮层网络神经编码的表征将揭示记忆巩固中的重要神经回路机制，并为记忆和梦境提供新的见解。尽管已经对与睡眠相关的神经元集群放电活动进行了研究，但识别睡眠中的记忆内容仍然具有挑战性。在这里，我们重新审视关于与睡眠相关记忆的重要实验发现（即睡眠中反映记忆处理的神经活动模式），并回顾分析与睡眠相关神经编码（SANC）的计算方法。我们专注于两种与睡眠相关记忆的分析范式，并提出一种新的无监督学习框架（“先记忆，后意义”），用于对SANC进行无偏评估。
