Scalable Bayesian Stochastic Process Models for Neural Data Analysis

总结 该提案的总体目标是整合一类新的统计方法的发展 在大鼠身上进行独特的电生理学实验，以解决基本的和未解决的问题， 海马功能，并在随后的研究中，提供前所未有的洞察神经 记忆受损的潜在机制开发新的统计工具， 神经数据是推进我们对基本记忆机制和记忆的理解的关键 紊乱然而，许多现有的统计方法无法处理这种数据密集型 在理论基础、计算复杂性和可扩展性方面存在问题。为了解决这个问题， 我们将设计一个强大的框架，用于使用灵活的多元高斯过程（GP）分析神经数据 模型（目标1）。这种新的框架将允许集成多种数据模式，特别是多个数据模式。 神经元锋电位序列和多节点局部场电位（LFP），同时识别它们的联合低维 表征和底层结构。为了使我们的方法适用于大数据分析，我们将 开发计算效率高的算法，以实现快速而准确的统计推断（目标2）。我们提出的 方法是基于一种新的组合快速变分近似方法和计算 马尔可夫链蒙特卡罗算法。我们将运用我们的分析方法， 作为研究计划的一部分收集的电生理数据集，旨在阐明 事件序列记忆的神经机制，情景记忆的一个定义特征 (Aim 3）。在这些数据集中，我们使用高密度电生理技术记录神经活动， 海马CA1区（尖峰和LFP）作为大鼠执行气味序列记忆任务。重要的是这 非空间方法允许我们确定空间编码特性（被认为是 海马记忆功能）扩展到非空间域，包括序列再激活（再激活 先前遍历的或即将到来的位置序列）和相位进动（尖峰出现在 在遍历单元的位置字段期间，θ循环的逐渐较早的阶段）。结合这些小说 分析工具与复杂的行为，电生理，和DREADDs灭活方法 在这里提出的建议将为我们提供一个前所未有的机会，以解决以下基本问题： 海马功能

Summary The overarching goal of this proposal is to integrate the development of a novel class of statistical methods with unique electrophysiological experiments in rats to address fundamental and unresolved questions about hippocampal function and, in subsequent studies, to provide unprecedented insight into the neural mechanisms underlying memory impairments. The development of novel statistical tools for the analysis of neural data is key to advance our understanding of fundamental memory mechanisms and of memory disorders. However, many existing statistical methods are not capable of handling such data-intensive problems in terms of theoretical foundation, computational complexity, and scalability. To address this issue, we will design a robust framework for analyzing neural data using flexible multivariate Gaussian process (GP) models (Aim 1). This novel framework will allow the integration of multiple data modalities, in particular multi- neuronal spike trains and multi-node local field potentials (LFP), while identifying their joint low-dimensional representations and underlying structures. To make our approach practical for big data analysis, we will develop computationally efficient algorithms for fast, yet accurate statistical inference (Aim 2). Our proposed approach is based on a novel combination of fast variational approximation methods and computationally efficient Markov Chain Monte Carlo algorithms. We will apply our analytical methods to unique electrophysiological datasets collected as part of a research program aimed at elucidating the fundamental neural mechanisms underlying the memory for sequences of events, a defining feature of episodic memory (Aim 3). In these datasets, we use high-density electrophysiological techniques to record neural activity in hippocampal region CA1 (spikes and LFP) as rats perform an odor sequence memory task. Importantly, this nonspatial approach allows us to determine whether spatial coding properties (thought to be fundamental to hippocampal memory function) extend to the nonspatial domain, including sequence reactivation (reactivation of previously traversed or upcoming sequences of locations) and phase precession (spikes occurring at progressively earlier phases of the theta cycle during traversal of the cell's place field). Combining these novel analytical tools with the sophisticated behavioral, electrophysiological, and DREADDs inactivation approaches proposed here will provide us with an unparalleled opportunity to address fundamental questions about hippocampal function.