CRCNS: Neural computations underlying sequence memory consolidation in sleep

CRCNS：睡眠中序列记忆巩固的神经计算

基本信息

批准号：
10646435
负责人：
MAKSIM V BAZHENOV
金额：
$ 35.24万
依托单位：
UNIVERSITY OF CALIFORNIA, SAN DIEGO
依托单位国家：
美国
项目类别：
财政年份：
2020
资助国家：
美国
起止时间：
2020-08-10 至 2025-05-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10646435
关键词：
Address Animals Behavior Behavioral Biophysical Process Brain Childhood Collaborations Communication Complex Computer Models Coupled Data Data Set Development Electroencephalography Elements Event Exhibits Foundations Generations Goals Hippocampus Human Individual Instruction Intelligence Intervention Knowledge Learning Measurement Mediating Medicine Memory Mental Depression Mental disorders Methods Modeling Motor Mus Neocortex Neurobiology Neurons Outcome Patients Performance Phase Play Post-Traumatic Stress Disorders Principal Investigator Property Psyche structure Research Personnel Retrieval Role Running Schizophrenia Sensory Shapes Sleep Slow-Wave Sleep Synapses Techniques Testing Texture Time Training Travel Weight Whole-Genome Shotgun Sequencing Work awake cognitive performance data modeling density executive function experience experimental study fascinate flexibility genetic manipulation improved in vivo insight memory consolidation neocortical neural neural network novel outcome prediction place fields preservation programs receptive field response sequence learning simulation verbal

项目摘要

The ability to store and retrieve sequentially related information is arguably the foundation of intelligent behavior. It allows us to predict the outcomes of sensory situations, to achieve goals by generating sequences of motor actions, to 'mentally' explore the possible outcomes of different navigational or motor choices, and ultimately to communicate through complex verbal sequences generated by flexibly chaining simpler elemental sequences learned in childhood. Sleep extracts invariant features from the learned information, leading to the generation of explicit knowledge and insight. Despite remarkable progress, including work by PI and co-PI of this project, many critical questions remain about role of sleep in memory and learning. Here we propose to address these questions through the development of computational models that are probed and validated through in vivo experiments in mice. We will explore the hippocampal (HC) and neocortical (NC) mechanisms underlying how sequences are acquired and subsequently consolidated through off-line replay during Slow Wave Sleep (SWS) in a manner that minimizes interference between overlapping and/or reversed sequences and how NC may chain sequence fragments together. We combine computer modelling (Bazhenov) of spiking neural networks that mimic awake and SWS brain dynamics, including NC slow oscillations and HC Sharp Wave Ripples (SWR), with high density neural ensemble recordings (McNaughton) in mice, in a controlled behavioral setting including sequence learning and subsequent, chemogenetically induced SWS, which makes it possible to observe how learned sequence representations in NC evolve spontaneously over prolonged periods of SWS. The PIs have been collaborating on and discussing this topic for the past several years, resulting in specific hypotheses that can be explored in real brains. The project outcome will provide a better understanding of how knowledge is extracted from experience, what brain circuits are involved and how brain dynamics are shaped by the development of a rich internal model of the world, including the ability to predict the outcomes of current situations and one's own actions in that context. RELEVANCE (See instructions): The ability to store and retrieve sequentially related information is the foundation of intelligent behavior and brain executive function. Deficits in this ability, resulting from disruption of brain circuits, are seen in depression, schizophrenia and PTSD. Better understanding of the mechanisms and brain dynamics underlying the acquisition, consolidation and retrieval of sequential information will lead to interventions to improve cognitive performance, memory and learning in healthy subjects and patients with mental illness.

存储和检索顺序相关信息的能力可以说是智能的基础行为。它使我们能够预测感官情况的结果，通过生成来实现目标运动动作的序列，以“精神上”探索不同导航或运动的可能结果选择，并最终通过灵活链接生成的复杂语言序列进行交流童年时学到的更简单的元素序列。睡眠从学习中提取不变特征信息，从而产生显性知识和洞察力。尽管取得了显着的进步，包括该项目的 PI 和 Co-PI 的工作，关于睡眠在记忆中的作用仍然存在许多关键问题和学习。在这里，我们建议通过发展计算技术来解决这些问题通过小鼠体内实验探索和验证的模型。我们将探索海马体 (HC) 和新皮质 (NC) 机制是序列获取和随后的基础通过慢波睡眠 (SWS) 期间的离线重播进行整合，以最大限度地减少重叠和/或反向序列之间的干扰以及 NC 如何链接序列片段一起。我们结合模拟清醒和清醒状态的尖峰神经网络的计算机建模（Bazhenov） SWS 大脑动力学，包括 NC 慢振荡和 HC 尖波波纹 (SWR)，具有高密度小鼠神经整体记录（麦克诺顿），在受控行为环境中，包括序列学习和随后的化学遗传学诱导的 SWS，这使得观察学习的过程成为可能 NC 中的序列表示会在 SWS 的长时间内自发演化。 PI 已在过去的几年里，我们就这个话题进行了合作和讨论，得出了具体的假设：可以在真实的大脑中进行探索。该项目的成果将让人们更好地理解知识如何是从经验中提取的，涉及哪些大脑回路以及大脑动态是如何被塑造的开发丰富的世界内部模型，包括预测当前结果的能力情况以及在这种情况下自己的行动。相关性（参见说明）：存储和检索顺序相关信息的能力是智能行为和行为的基础大脑的执行功能。由于大脑回路中断而导致这种能力的缺陷，见于抑郁症、精神分裂症和创伤后应激障碍。更好地理解机制和大脑动力学顺序信息的获取、整合和检索的基础将导致干预改善健康受试者和精神疾病患者的认知表现、记忆力和学习能力。