Filtered Point Process Inference Framework for Modeling Neural Data

用于神经数据建模的过滤点过程推理框架

• 批准号: 9170395
• 负责人: EMERY N BROWN
• 金额: $ 35万
• 依托单位: MASSACHUSETTS GENERAL HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-09-30 至 2019-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9170395
• 关键词: Adrenal Glands; Algorithms; Animals; Architecture; Basic Science; Behavioral; Biological Process; Brain; Calcium; Central obesity; Clinical; Code; Collaborations; Communities; Complex; Computer software; Confidence Intervals; Corticotropin; Coupled; Cushing Syndrome; Data; Data Analyses; Diabetes Mellitus; Differential Equation; Disease; Endocrine system; Etiology; Event; Functional disorder; Goals; Gonadal Steroid Hormones; Hormonal; Hormones; Human; Hydrocortisone; Image; Insulin; Intervention; Least-Squares Analysis; Life; Light; Linear Models; Link; Machine Learning; Measurement; Medical; Memory; Methodology; Methods; Modeling; Morphologic artifacts; Mus; Nature; Neuraxis; Neurons; Neurosciences; Neurosciences Research; Neurosecretory Systems; Noise; Osteoporosis; Parietal Lobe; Physiologic pulse; Physiology; Pituitary Gland; Population; Population Analysis; Procedures; Process; Recovery; Research; Resolution; Rodent; Series; Serum; Signal Transduction; Software Tools; Somatotropin; Statistical Methods; Statistical Models; Stimulus; Structure; System; Techniques; Testing; Thyroid Hormones; Time; Time Series Analysis; Tissues; Training; United States National Institutes of Health; V1 neuron; Visual; Visual Perception; area striata; awake; base; computational neuroscience; computerized data processing; design; drug efficacy; dynamic system; human data; improved; in vivo; mathematical algorithm; movie; neural model; novel; open source; relating to nervous system; research study; signal processing; spatiotemporal; temporal measurement; tool; two-photon; visual cognition; visual motor

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.

抽象的 中心神经系统中的神经元尖峰训练和其他各种信号具有离散的， 在几种神经科学中，脉络过程统计模型的脉冲性质都很好 应用，这种冲动信号在与生物过程相互作用或 测量伪像，因此被观察到该项目的尖锐过程数据。 是通过模型和 用于估计和推理的算法，并将这些甲基甲化学应用于来自 啮齿动物脑钙成像数据和人类神经内分泌数据。 用于稀疏压抑和动态系统建模的框架，该模型在 神经科学分析。 这些方法是在基本神经科学和临床中产生的实验数据的应用 问题，既可以通过真实数据来验证新方法，又要调查与 中心神经系统结构和功能组织。 与尖峰训练反卷积的结合将使我们能够研究一千多个鉴定的活动 神经元同时与行为动物中的单个尖峰分辨率。 高准确性的神经元信号和噪声相关性的幅度和空间结构如何变化 使用刺激或行为任务。 视觉渗透和认知为基础的结构，以未经预言的时空尺度。 我们对脉冲激素分泌的建模将使皮质醇，性腺类固醇，胰岛素，胰岛素的释放。 甲状腺和生长激素与异常皮质醇分泌有关的疾病包括糖尿病 以及骨质疏松症，记忆形成干扰和威胁生命的加法危机。 并建模正常激素释放的潜在冲动性质将有助于我们的理解 病理神经内分泌状态并提高药物的功效和其他干预措施 荷尔蒙障碍。 具有SUR LAB在神经元钙成像方面的经验专业知识的过程模型，扩展了我们正在进行的 在NIH大脑倡议下的合作与开发新型神经个人事分析技术与 单神经元的未经预言的细节，我们的研究旨在改善我们的研究。 显着的艺术状态和计算机系统神经科学工具并桥接在一起 来自统计学习，信号处理和计算神经科学社区的组成部分 为神经数据分析产生厌食的分析框架。