Quiescent dependent consolidation of spatial and motor-skill memory: investigation of the neural mechanisms with animal models and computational modelling

空间和运动技能记忆的静态依赖巩固：用动物模型和计算模型研究神经机制

• 批准号: RGPIN-2015-06109
• 负责人: Tatsuno, Masami
• 金额: $ 2.04万
• 依托单位: University of Lethbridge
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2016
• 资助国家: 加拿大
• 起止时间: 2016-01-01 至 2017-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=613949
• 关键词: Quiescent; dependent; consolidation; spatial; motor

Memory is a crucial brain function for humans and other animals. It has been broadly categorized into declarative memory (a type of memory that you can verbally describe, for example, the breakfast you had this morning) and non-declarative memory (a type of memory that you cannot verbally describe, for example, the motor skill required to ride a bike). Memory also provides the basis of other higher-order cognitive functions such as decision making. Thus, understanding how the brain learns and remembers is crucial to understanding many brain functions. The goal of my research program is to clarify how different forms of memory are stored and integrated to allow efficient access. In order to explain the mechanisms involved in the acquisition of memory, I propose a theory of Quiescent Dependent Memory Consolidation: Memory is consolidated during sleep when both motor output and sensory input are shut down. The quiescence of sleep, when the brain’s network is free from external interference, provides a permissive environment for memory consolidation. Sleep in mammals is categorized into Rapid Eye Movement sleep (REM sleep) and non-Rapid Eye Movement sleep (non-REM sleep). Our investigation and studies from other laboratories suggest that neural activity in the hippocampus and prefrontal cortex during non-REM sleep is important for consolidation of declarative memory, whereas neural activity in the motor cortex (and possibly dorsal striatum) during REM sleep is important for consolidation of non-declarative memory. However, it is not known how the brain areas that are involved in declarative memory influence the activity of the brain areas that are involved in non-declarative memory, and vice versa, particularly when the memory task involves both declarative and non-declarative components. The evolution of multi-electrode recording, which my lab is at the forefront of, now permits large-scale, multi-site recordings that are necessary to address these questions. Using this technology, I will investigate the interaction of these brain areas when animals learn a declarative type memory task, a non-declarative type memory task and a combined memory task. To elucidate the underlying mathematical principles, I will also construct an artificial neural network model of the brain areas of interest. Mathematical analysis and computer simulation will be performed and the results will be compared with electrophysiological data. My research will improve our understanding of neural plasticity during waking and sleeping, biological mechanisms of memory consolidation and the underlying mathematical principles of memory formation. Because memory is the basis of other higher-order brain functions, my research program will deepen our knowledge of the executive functions such as decision making.

对于人类和其他动物来说，记忆是一项至关重要的大脑功能。它被大致分为陈述性记忆(一种你可以口头描述的记忆，例如，你今天早上吃的早餐)和非陈述性记忆(一种你不能口头描述的记忆，例如，骑自行车所需的运动技能)。记忆也为决策等其他高级认知功能提供了基础。因此，了解大脑是如何学习和记忆的，对于了解许多大脑功能至关重要。我的研究项目的目标是阐明不同形式的内存是如何存储和集成的，以实现高效的访问。为了解释记忆获得的机制，我提出了一种静态依赖记忆巩固理论：当运动输出和感觉输入都被关闭时，记忆在睡眠中被巩固。当大脑的网络不受外界干扰时，睡眠的静止为记忆巩固提供了一个宽松的环境。哺乳动物的睡眠分为快速眼动睡眠(REM睡眠)和非快速眼动睡眠(非REM睡眠)。我们的调查和其他实验室的研究表明，非REM睡眠期间海马区和前额叶皮质的神经活动对于巩固陈述性记忆是重要的，而REM睡眠期间运动皮质(可能还有背侧纹状体)的神经活动对于巩固非陈述性记忆是重要的。然而，目前尚不清楚涉及陈述性记忆的大脑区域如何影响涉及非陈述性记忆的大脑区域的活动，反之亦然，特别是当记忆任务同时涉及陈述性和非陈述性成分时。多电极记录的发展，我的实验室走在了前列，现在可以进行大规模、多地点的记录，这是解决这些问题所必需的。利用这项技术，我将研究当动物学习陈述性记忆任务、非陈述性记忆任务和组合记忆任务时，这些脑区的相互作用。为了阐明基本的数学原理，我还将构建一个大脑感兴趣区域的人工神经网络模型。将进行数学分析和计算机模拟，并将结果与电生理数据进行比较。我的研究将提高我们对清醒和睡眠中的神经可塑性、记忆巩固的生物学机制以及记忆形成的潜在数学原理的理解。因为记忆是其他高级大脑功能的基础，我的研究计划将加深我们对决策等执行功能的了解。