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
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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.

摘要 中枢神经系统中的神经元尖峰序列和各种其他信号具有离散的， 脉冲性质，用点过程统计模型很好地表征。在几个神经科学 在一些应用中，这样的脉冲信号在与生物过程相互作用时被变换，或者 测量伪影，并且因此被观察为过滤的点过程数据。这个项目的目标 是为具有模型的过滤点过程开发原则性统计信号处理框架， 算法的估计和推理，并将这些新的方法，以实验数据从 啮齿动物脑钙成像数据和人类神经内分泌数据。我们的方法集中在统一的 产生的标记点过程数据的稀疏表示和动态系统建模的框架 神经科学分析除了新颖的统计方法外，我们的建议的另一个主要优点是 是将这些方法应用于基础神经科学和临床 问题，既要用真实的数据验证新方法，又要研究与以下问题有关的基础科学问题： 中枢神经系统的结构和功能组织。大规模双光子钙成像，在 结合尖峰列车反卷积，将使我们能够研究超过一千个确定的活动， 神经元同时与单穗分辨率在一个行为的动物。这将使我们能够阐明 神经元之间信号和噪声相关性的幅度和空间结构如何变化的高精度 刺激或行为任务。它将揭示啮齿动物大脑中的视觉编码，以及神经元 在前所未有的时空尺度上，视觉感知和认知的基础架构。此外，本发明还 我们对脉动激素分泌的建模将适用于皮质醇、性腺类固醇、胰岛素 甲状腺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 以及骨质疏松症、记忆形成障碍和危及生命的艾迪生氏危象。因此，理解 对正常激素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，提高药物和其他干预治疗的疗效 荷尔蒙失调此外，该项目将结合联合收割机布朗实验室的计算专业知识， 利用苏尔实验室在神经元钙成像方面的实验专业知识， 在NIH脑计划下合作开发新的神经群体分析技术， 单神经元单脉冲分辨率下前所未有的细节。我们的研究已经做好准备， 最先进的计算和系统神经科学工具和桥梁 来自统计学习、信号处理和计算神经科学社区的组件， 为神经数据分析提供统一的分析框架。