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: 2019
• 资助国家: 加拿大
• 起止时间: 2019-01-01 至 2020-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=682725
• 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至关重要的大脑功能。它已被广泛地分为声明性记忆（例如，您可以在语言上描述的一种记忆，例如，您今天早上吃的早餐）和非确定的记忆（一种您无法口头描述的一种记忆，例如，例如骑自行车所需的运动技能）。内存还提供了其他高阶认知功能（例如决策）的基础。因此，了解大脑的学习方式和记忆对于理解许多大脑功能至关重要。我的研究计划的目的是阐明如何存储和集成不同形式的内存以允许有效访问。为了解释记忆采集所涉及的机制，我提出了一种静止的记忆巩固理论：当关闭电动机输出和感觉输入时，在睡眠期间巩固内存。当大脑网络不受外部干扰的情况下，睡眠的静止，为记忆巩固提供了一个宽松的环境。哺乳动物中的睡眠归类为快速的眼睛运动睡眠（REM睡眠）和非比型眼动睡眠（非REM睡眠）。我们的其他实验室的研究和研究表明，非REM睡眠期间海马和前额叶皮层中的神经活动对于声明性记忆的巩固很重要，而在REM睡眠期间，REM睡眠期间的神经活动对于非降低记忆的巩固很重要。但是，尚不清楚参与声明记忆的大脑区域如何影响涉及非定义记忆的大脑区域的活动，反之亦然，尤其是当记忆任务涉及声明性和非定义成分时。我的实验室处于最前沿的多电极记录的演变，现在允许大规模的多站点录音来解决这些问题。使用此技术，当动物学习声明类型的内存任务，非定位类型的内存任务和组合内存任务时，我将研究这些大脑区域的相互作用。为了阐明潜在的数学原则，我还将构建感兴趣的大脑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醒来和睡眠期间神经可塑性的理解，记忆巩固的生物学机制以及记忆形成的基本数学原理。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执行功能（例如决策）的了解。