Scalable Bayesian Stochastic Process Models for Neural Data Analysis

摘要 这项建议的总体目标是整合一类新的统计方法的开发 通过在大鼠身上进行独特的电生理实验来解决基本的和未解决的问题 海马体的功能，并在随后的研究中提供对神经的前所未有的洞察 记忆损伤的潜在机制。开发新的统计工具用于分析 神经数据是促进我们对基本记忆机制和记忆的理解的关键 精神错乱。然而，现有的许多统计方法都无法处理这样的数据密集型数据 在理论基础、计算复杂性和可伸缩性方面的问题。为了解决这个问题， 我们将使用灵活的多变量高斯过程(GP)设计一个健壮的框架来分析神经数据 模型(目标1)。这一新颖的框架将允许集成多个数据模式，尤其是多个 神经元棘波序列和多结点局部场电位(LFP)，同时识别其联合低维 表示和底层结构。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)。我们的建议 该方法基于一种新的快速变分逼近方法的组合，在计算上 高效的马尔可夫链蒙特卡罗算法。我们将把我们的分析方法应用于独特的 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，这是情节记忆的一个决定性特征 (目标3)。在这些数据集中，我们使用高密度电生理技术来记录 当大鼠执行气味序列记忆任务时，海马区CA1区(棘波和LFP)。重要的是，这 非空间方法允许我们确定空间编码属性(被认为是 海马记忆功能)延伸到非空间域，包括序列重新激活(重新激活 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 在遍历细胞的位场期间逐渐进入theta循环的早期阶段)。把这些小说结合起来 具有复杂的行为、电生理和DREADDS失活方法的分析工具 在这里提出的建议将为我们提供一个无与伦比的机会来解决关于 海马体功能。

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.