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.

记忆是人类和其他动物的重要大脑功能，它大致分为陈述性记忆（一种可以口头描述的记忆，例如，今天早上吃的早餐）和非陈述性记忆（一种可以口头描述的记忆）和非陈述性记忆（一种可以口头描述的记忆）。无法用语言描述的记忆，例如骑自行车所需的运动技能）。记忆还为其他高阶认知功能（例如决策）提供了基础。因此，了解大脑如何学习和记忆对于理解至关重要。我的许多大脑功能的目标。研究计划是阐明不同形式的记忆是如何存储和整合的，以实现有效的访问。为了解释记忆获取的机制，我提出了一种静态依赖记忆巩固理论：当两个运动时，记忆在睡眠期间得到巩固。当大脑网络不受外界干扰时，睡眠的输出和感觉输入被关闭，为哺乳动物的记忆巩固提供了一个宽松的环境，睡眠分为快速眼动睡眠（REM 睡眠）和非快速眼动睡眠。运动睡眠（非快速眼动睡眠）。我们的调查和其他实验室的研究表明，非快速眼动睡眠期间海马和前额皮质的神经活动对于巩固陈述性记忆很重要，而运动皮层（可能还有背侧纹状体）的神经活动也很重要。快速眼动睡眠期间对于巩固非陈述性记忆很重要但是，尚不清楚参与陈述性记忆的大脑区域如何影响参与非陈述性记忆的大脑区域的活动。反之亦然，特别是当记忆任务涉及声明性和非声明性组件时，我的实验室处于前沿，现在允许进行大规模、多站点记录，这是解决问题所必需的。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大脑区域的相互作用，以阐明基本的数学原理。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期间的神经可塑性、记忆巩固的生物机制以及基本数学原理的理解。由于记忆是其他高级大脑功能的基础，因此我的研究计划将加深我们对决策等执行功能的了解。