Liu Shizhao, Grosmark Andres D, Chen Zhe
Departments of Psychiatry and of Department of Neuroscience and Physiology, New York University School of Medicine, New York, NY 10016, U.S.A., and Department of Biomedical Engineering, Tsinghua University, Beijing, China
Department of Neuroscience, Columbia University Medical Center, New York, NY 10019, U.S.A.
Neural Comput. 2018 Aug;30(8):2175-2209. doi: 10.1162/neco_a_01090. Epub 2018 Apr 13.
It has been suggested that reactivation of previously acquired experiences or stored information in declarative memories in the hippocampus and neocortex contributes to memory consolidation and learning. Understanding memory consolidation depends crucially on the development of robust statistical methods for assessing memory reactivation. To date, several statistical methods have seen established for assessing memory reactivation based on bursts of ensemble neural spike activity during offline states. Using population-decoding methods, we propose a new statistical metric, the weighted distance correlation, to assess hippocampal memory reactivation (i.e., spatial memory replay) during quiet wakefulness and slow-wave sleep. The new metric can be combined with an unsupervised population decoding analysis, which is invariant to latent state labeling and allows us to detect statistical dependency beyond linearity in memory traces. We validate the new metric using two rat hippocampal recordings in spatial navigation tasks. Our proposed analysis framework may have a broader impact on assessing memory reactivations in other brain regions under different behavioral tasks.
有人提出，海马体和新皮层中陈述性记忆中先前获得的经验或存储信息的重新激活有助于记忆巩固和学习。理解记忆巩固关键取决于开发用于评估记忆重新激活的强大统计方法。迄今为止，已经建立了几种统计方法来基于离线状态下的群体神经尖峰活动爆发来评估记忆重新激活。使用群体解码方法，我们提出了一种新的统计指标，即加权距离相关性，以评估安静清醒和慢波睡眠期间海马体记忆重新激活（即空间记忆重演）。新指标可以与无监督群体解码分析相结合，该分析对潜在状态标记不变，并允许我们检测记忆痕迹中超出线性的统计依赖性。我们使用空间导航任务中的两个大鼠海马体记录来验证新指标。我们提出的分析框架可能对评估不同行为任务下其他脑区的记忆重新激活有更广泛的影响。
