CRCNS: Computational Approaches to Uncover Neural Representation of Population Codes in Rodent Hippocampal-Cortical Circuits

CRCNS：揭示啮齿动物海马皮质回路中群体代码神经表征的计算方法

• 批准号: 1443032
• 负责人: Zhe Chen
• 金额: $ 86.8万
• 依托单位: New York University Medical Center
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-04-01 至 2018-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1443032&HistoricalAwards=false
• 关键词: CRCNS; Computational; Approaches; Uncover; Neural

Spatial navigation and episodic memory are important for daily activity and survival in rodents and primates. Episodic memory consists of collections of past experiences that occurred at a particular time and space, expressed in the form of sequences of temporal or spatial events. Spatial (topographical or topological) representation of the environment is pivotal for navigation. The hippocampus plays a significant role in both spatial representations and episodic memory. However, it remains unclear how the spikes of hippocampal neurons might be used by downstream structures in order to reconstruct the spatial environment without the a priori information of the place receptive fields. Little is known how the hippocampal neuronal representation might be affected by experimental manipulation. Furthermore, cortico-hippocampal interplay and communications are critical for memory consolidation, but many questions about their temporal coordination during sleep remains unresolved. This project proposes a collaborative proposal for studying the neural representation of population codes in rodent hippocampal-cortical circuits. The investigators and collaborators at MGH, MIT and Boston University will integrate innovative computational and experimental approaches to explore the neural codes during various spatial navigation and spatial/temporal memory tasks as well as during post-behavior sleep---as sleep is critical to hippocampal-dependent memory consolidation. Notably, due to the lack of measured behavior, it remains a great challenge to analyze or interpret sleep-associated hippocampal or cortical spike data. The important questions central to this project are: how do hippocampal (or hippocampal-cortical) neuronal representations vary with respect to species (rat vs. mouse), animal (healthy vs. diseased), experience (novel vs. familiar), environment (one vs. two-dimensional), behavioral state (awake vs. sleep), and task (active vs. passive navigation; spatial working memory vs. temporal sequence memory). The investigators will simultaneously record ensemble spike activity from two or multiple areas of the rodent brain (hippocampus, primary visual cortex, prefrontal cortex, and retrosplenial cortex) under different experimental conditions, and will decipher the population codes using a coherent statistical framework. In light of Bayesian inference (variational Bayes or nonparametric Bayes), innovative unsupervised or semi-supervised learning approaches are developed for mining and visualizing sparse (in terms of both sample size and low firing rate) neuronal ensemble spike data. The outcome of this investigation will improve the understanding of neural mechanisms of hippocampal (or hippocampal-cortical) population coding and its implications in learning, sleep and memory. The derived findings will shed light on the links between the variability of neural responses and the animal behavior (or other external factors), and will provide further insight into memory dysfunction (such as in Alzheimer's disease). Furthermore, this project has broader impacts in developing efficient algorithms to decipher neuronal population spike activity during behavior or sleep, as well as in discovering invariant topological representation of population codes in other cortical areas. In addition to the scientific significance, this proposal bears an educational component for training researchers on advanced quantitative skills in ensemble spike data analysis as well as for disseminating scientific resources (by sharing data and software) to a broad neuroscience community.

空间导航和情景记忆对啮齿动物和灵长类动物的日常活动和生存至关重要。情景记忆包括在特定时间和空间发生的过去经历的集合，以时间或空间事件序列的形式表达。环境的空间（地形或拓扑）表示对导航至关重要。海马体在空间表征和情景记忆中都起着重要作用。然而，在没有位置感受野的先验信息的情况下，下游结构如何利用海马神经元的尖峰来重建空间环境尚不清楚。实验操作对海马神经元表征的影响尚不清楚。此外，皮质-海马体的相互作用和交流对记忆巩固至关重要，但关于它们在睡眠期间的时间协调的许多问题仍未解决。本项目提出了一项合作建议，用于研究啮齿动物海马-皮层回路中种群密码的神经表征。麻省总医院、麻省理工学院和波士顿大学的研究人员和合作者将整合创新的计算和实验方法，探索各种空间导航和空间/时间记忆任务以及行为后睡眠期间的神经编码，因为睡眠对海马体依赖的记忆巩固至关重要。值得注意的是，由于缺乏可测量的行为，分析或解释与睡眠相关的海马或皮质峰数据仍然是一个巨大的挑战。这个项目的核心问题是：海马体（或海马体-皮层）神经元表征如何随着物种（大鼠vs小鼠）、动物（健康vs患病）、经验（新奇vs熟悉）、环境（一维vs二维）、行为状态（清醒vs睡眠）和任务（主动vs被动导航、空间工作记忆vs时间序列记忆）而变化。研究者将在不同的实验条件下同时记录啮齿类动物大脑的两个或多个区域（海马、初级视觉皮层、前额叶皮层和脾脏后皮层）的集合峰活动，并将使用一个一致的统计框架来破译种群密码。根据贝叶斯推理（变分贝叶斯或非参数贝叶斯），开发了创新的无监督或半监督学习方法，用于挖掘和可视化稀疏（在样本量和低发射率方面）神经元集合峰值数据。本研究结果将有助于进一步了解海马（或海马皮质）群体编码的神经机制及其在学习、睡眠和记忆中的意义。衍生的发现将阐明神经反应的可变性与动物行为（或其他外部因素）之间的联系，并将为记忆功能障碍（如阿尔茨海默病）提供进一步的见解。此外，该项目在开发有效的算法来破译行为或睡眠期间的神经元群体峰值活动方面具有更广泛的影响，以及在发现其他皮质区域群体代码的不变拓扑表示方面具有更广泛的影响。除了科学意义之外，该提案还具有教育意义，用于培训研究人员在集成峰值数据分析方面的高级定量技能，以及向广泛的神经科学社区传播科学资源（通过共享数据和软件）。