CRCNS: Neural computations underlying sequence memory consolidation in sleep

CRCNS：睡眠中序列记忆巩固的神经计算

• 批准号: 10447795
• 负责人: MAKSIM V BAZHENOV
• 金额: $ 35.53万
• 依托单位: UNIVERSITY OF CALIFORNIA, SAN DIEGO
• 依托单位国家: 美国
• 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/10447795
• 关键词: Address; Animals; Behavior; Behavioral; Biophysical Process; Brain; Childhood; Collaborations; Complex; Computer Models; Coupled; Data; Data Set; Development; Electroencephalogram; Elements; Event; Exhibits; Foundations; Generations; Goals; Hippocampus (Brain); Human; Individual; Instruction; Intelligence; Intervention; Knowledge; Learning; Light; Measurement; Mediating; Medicine; Memory; Mental Depression; Mental disorders; Methods; Modeling; Motor; Mus; Neocortex; Neurobiology; Neurons; Outcome; Patients; Performance; Phase; Post-Traumatic Stress Disorders; Principal Investigator; Property; Psyche structure; Research Personnel; Retrieval; Role; Running; Schizophrenia; Sensory; 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; improved; in vivo; insight; memory consolidation; neocortical; neural network; novel; outcome prediction; place fields; preservation; receptive field; relating to nervous system; response; sequence learning; simulation

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状况的结果，通过产生 运动动作序列，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产生的复杂语言序列进行交流 在儿童时期学到的简单的元素序列。Sleep从学习到的 信息，导致产生明确的知识和洞察力。尽管取得了显著进展， 包括这个项目的主要研究者和共同研究者的工作，许多关于睡眠在记忆中的作用的关键问题仍然存在 和学习在这里，我们建议通过发展计算 通过小鼠体内实验探测和验证的模型。我们将探索海马体 (HC)和新皮层（NC）机制，这些机制是如何获得序列的， 在慢波睡眠（SWS）期间通过离线重放进行巩固， 重叠和/或反向序列之间的干扰以及NC如何链化序列片段 一起我们结合联合收割机计算机建模（巴热诺夫）的尖峰神经网络，模仿清醒和 SWS脑动力学，包括NC慢振荡和HC尖波波纹（SWR），具有高密度 在受控行为环境中，包括序列 学习和随后的，化学遗传诱导的SWS，这使得有可能观察如何学习 NC中的序列表征在SWS的延长时期内自发地演变。私家侦探们 在过去的几年里，我们一直在合作和讨论这个话题，产生了一些具体的假设， 可以在真实的大脑中进行探索。项目成果将使人们更好地了解知识 是从经验中提取出来的，大脑回路涉及什么，以及大脑动力学是如何被大脑所塑造的。 开发一个丰富的世界内部模型，包括预测当前世界的结果的能力。 在这种情况下，自己的行为。 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的基础， 大脑执行功能这种能力的缺陷是由于大脑回路的中断， 抑郁症精神分裂症和创伤后应激障碍更好地理解大脑的机制和动力学 获取、整合和检索连续信息的基础将导致干预措施， 改善健康受试者和精神疾病患者的认知表现、记忆和学习。