Filtered Point Process Inference Framework for Modeling Neural Data

摘要 中枢神经系统中的神经元棘波和各种其他信号具有离散的， 冲动性，这是点过程统计模型很好的特征。在几个神经科学中 应用，这样的脉冲信号在与生物过程或 测量伪影，并因此被观察为滤波点过程数据。这个项目的目标是 是建立一个原则性的统计信号处理框架，用于滤波点过程的模型和 用于估计和推断的算法，并将这些新方法应用于来自 啮齿类动物脑钙成像数据和人类神经内分泌数据。我们的方法以统一的 中出现的标记点过程数据的稀疏表示和动态系统建模框架 神经科学分析。除了其新颖的统计方法外，我们提案的另一个主要优势 是否将这些方法应用于基础神经科学和临床的实验数据 问题，既要用真实数据验证新方法，也要调查与 中枢神经系统的结构和功能组织。大规模双光子钙成像 与脉冲序列反卷积相结合，将使我们能够研究1000多个已识别的活动 在行为动物中，神经元同时具有单棘波分辨率。这将使我们能够澄清 高精度神经元间信号和噪声相关性的大小和空间结构如何变化 有刺激或行为任务。它将阐明啮齿动物大脑和神经元中的视觉编码 建筑是视觉感知和认知的基础，具有前所未有的时空尺度。此外， 我们的脉动激素分泌模型将适用于皮质醇、性腺类固醇、胰岛素、 甲状腺激素和生长激素。与皮质醇分泌异常有关的疾病包括糖尿病、内脏肥胖 骨质疏松症、记忆形成障碍和危及生命的艾迪生危机。因此，理解 对正常荷尔蒙释放的潜在冲动性质进行建模将有助于我们理解 病理性神经内分泌状态及提高药物和其他干预治疗的疗效 荷尔蒙失调。此外，该项目将结合Brown Lab的计算专业知识 利用Sur Lab在神经细胞钙成像方面的实验专业知识建立过程模型，扩展我们正在进行的 在NIH大脑倡议下的合作，以开发新的神经种群分析技术 单神经元、单尖峰分辨率的前所未有的细节。我们的研究很有可能会有所改进 值得注意的是，在计算和系统中，最先进的神经科学工具和桥梁连接在一起 来自统计学习、信号处理和计算神经科学界的组件 为神经数据分析制定统一的分析框架。

ABSTRACT Neuronal spike-trains and various other signals in the central nervous system have a discrete, impulsive nature that is well characterized with point process statistical models. In several neuroscience applications, such impulsive signals are transformed upon interaction with biological processes or measurement artifacts, and are consequently observed as filtered point process data. The goal of this project is to develop a principled statistical signal processing framework for filtered point processes with models and algorithms for estimation and inference, and to apply these novel methodologies to experimental data from rodent brain calcium imaging data and human neuroendocrine data. Our approach centers on a unified framework for sparse representation and dynamical systems modeling of marked point process data arising in neuroscience analyses. In addition to its novel statistical methodology, another major strength of our proposal is the application of these methods to experimental data arising in fundamental neuroscience and clinical problems, both to validate the new methods with real data and to investigate basic science questions related to the central nervous system structural and functional organization. Large-scale two-photon calcium imaging, in conjunction with spike-train deconvolution, will allow us to study the activity of over a thousand identified neurons simultaneously with single-spike resolution in a behaving animal. This will allow us to elucidate with high accuracy how the magnitude and spatial structure of signal and noise correlations across neurons vary with stimuli or behavioral tasks. It will shed light on visual encoding in the rodent brain, and neuronal architectures underlying visual perception and cognition, at an unprecedented spatiotemporal scale. Further, our modeling of pulsatile hormone secretion will apply to the release of cortisol, gonadal steroids, insulin, thyroid and growth hormones. Diseases linked to abnormal cortisol secretion include diabetes, visceral obesity and osteoporosis, disturbed memory formation and life-threatening Addisonian crisis. Hence, understanding and modeling the underlying impulsive nature of normal hormone release will aid our understanding of pathological neuroendocrine states and improve the efficacy of drugs and other interventions for treatment of hormonal disorders. Additionally, this project will combine Brown Lab’s computational expertise in point process models with Sur Lab’s experimental expertise in neuronal calcium imaging, extending our ongoing collaboration under the NIH Brain Initiative to developing novel neural population analysis techniques with unprecedented detail at single-neuron, single-spike resolution. Our research is well poised to improve significantly the state of the art and in computational and systems neuroscience tools and bridge together components from the statistical learning, signal processing and computational neuroscience communities to produce a unifying analytical framework for neural data analysis.