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: 1307645
• 负责人: Zhe Chen
• 金额: $ 97.41万
• 依托单位: Massachusetts Institute of Technology
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-10-01 至 2014-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1307645&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于啮齿动物和灵长类动物的日常活动和生存很重要。情景记忆由在特定时间和空间发生的过去经历的集合组成，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）表示对于导航至关重要。海马体在空间表征和情景记忆中发挥着重要作用。然而，目前尚不清楚下游结构如何使用海马神经元的尖峰来在没有位置感受野的先验信息的情况下重建空间环境。目前尚不清楚海马神经元表征如何受到实验操作的影响。此外，皮质-海马的相互作用和通信对于记忆巩固至关重要，但关于睡眠期间它们的时间协调的许多问题仍未解决。该项目提出了一项研究啮齿动物海马皮质回路中群体代码的神经表征的合作提案。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——因为睡眠对于海马依赖性记忆巩固至关重要。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尖峰数据仍然是一个巨大的挑战。该项目的核心问题是：海马（或海马皮质）神经元表征如何随物种（大鼠与小鼠）、动物（健康与患病）、经验（新奇与熟悉）、环境（一维与二维）、行为状态（清醒与睡眠）和任务（主动与被动导航；空间工作记忆与时间序列记忆）而变化。研究人员将在不同的实验条件下同时记录啮齿动物大脑两个或多个区域（海马体、初级视觉皮层、前额叶皮层和压后皮层）的整体尖峰活动，并使用连贯的统计框架破译群体密码。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（在样本大小和低放电率方面）神经元集合尖峰数据。这项研究的结果将增进对海马（或海马皮质）群体编码的神经机制及其对学习、睡眠和记忆的影响的理解。得出的发现将揭示神经反应的变异性与动物行为（或其他外部因素）之间的联系，并将进一步深入了解记忆功能障碍（例如阿尔茨海默病）。此外，该项目在开发有效算法来破译行为或睡眠期间神经元群体尖峰活动以及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。除了科学意义外，该提案还具有教育意义，可以培训研究人员在集合尖峰数据分析方面的高级定量技能，以及向广泛的神经科学界传播科学资源（通过共享数据和软件）。