BRAIN EAGER: Analyzing and modeling power-law behaviors in neuroscience

BRAIN EAGER：神经科学中幂律行为的分析和建模

• 批准号: 1451032
• 负责人: Fidel Santamaria
• 金额: $ 30万
• 依托单位: University of Texas at San Antonio
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-09-01 至 2018-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1451032&HistoricalAwards=false
• 关键词: BRAIN; EAGER; Analyzing; modeling; power

The objective of this EAGER project is to build and apply a computational toolbox to study and model power-law dynamics in the brain. Traditionally, any complex behavior in neuroscience is broken into the interactions of multiple components, each working in its own characteristic temporal framework. However, there is an increasing number of examples, such as in brain activity recording by electroencephalography (EEG), firing rate adaptation, and synaptic weight dynamics, in which the characteristic process follows power-law dynamics, which indicate that the time constant of a mechanism at one scale is highly correlated to the activity of the system at multiple scales. Therefore, the overall behavior of the system cannot be separated into largely independent components and traditional analysis techniques cannot provide an appropriate description of how the system works. In order to understand neuronal information processing at multiple scales it is necessary to develop a framework to analyze and model power-law dynamics at all levels of biological organization. This project plans to make widely available a unified platform to detect, analyze, validate, and model power-law behavior in the nervous system at multiple scales of organization. To broaden impact the team will generate products for the public that will explain the differences between power-law and exponential processes and their importance in neuroscience research. Research opportunities will be provided for students, especially underrepresented group at the University of Texas at San Antonio (UTSA), a minority serving institution. The collaborative team will analyze and model power-law relationships in large-scale brain activity and complex behavior. The project aims to build and validate a toolbox to test and characterize power-laws in data streams and to model power-law dynamical systems. For this purpose state-of-the art algorithms will be used to characterize experimental data and fractional differential equations to model power-law dynamical systems. This modeling platform will allow the study of power-law processes from the sub-cellular to the behavior scales. The toolbox will be applied to two very different problems dealing with complex pattern generation (birdsong production) and human language comprehension. Both applications will require the analysis of Big Data streams and model non-linear sequence production or decision-making. Although initially the focus of research will be in these two projects the framework will be built to be applicable to a wide range of neuroscience projects that can impact the research done under the BRAIN initiative. Interactive examples will be implemented using Mathematica and Matlab platforms and the team will also update and write new Wikipedia pages on the topics of this grant. In all projects, graduate and undergraduate students will be involved in both the research and educational components, providing opportunities not only to do research but to enhance their communication skills.

这个渴望的项目的目的是构建和应用计算工具箱来研究和模拟大脑中的幂律动态。传统上，神经科学中的任何复杂行为都被分解为多个组件的相互作用，每个组件都以其自身的时间框架起作用。但是，示例数量越来越多，例如在脑活动中通过脑活动记录（EEG），发射速率适应和突触重量动态，其中特征过程遵循幂律动态，这表明一种机制的时间常数一个尺度与系统活性在多个尺度上高度相关。因此，系统的整体行为不能分为很大程度上独立的组件，传统分析技术不能对系统的工作方式提供适当的描述。为了了解多个尺度的神经元信息处理，有必要开发一个框架来分析和建模生物组织的各个级别的幂律动态。该项目计划在多个组织的多个尺度上，在神经系统中检测，分析，验证和模型的幂律行为成为统一的平台。为了扩大影响，团队将为公众生成产品，以解释幂律和指数过程之间的差异及其在神经科学研究中的重要性。将为学生提供研究机会，尤其是在得克萨斯大学圣安东尼奥分校（UTSA）的学生，少数派服务机构的研究机会。协作团队将在大规模的大脑活动和复杂行为中分析和建模电力法关系。该项目旨在构建和验证一个工具箱，以测试和表征数据流中的幂律并建模幂律动态系统。为此，最先进的算法将用于表征实验数据和分数微分方程以建模幂律动态系统。这个建模平台将允许研究从细胞到行为量表的幂律过程。该工具箱将应用于两个截然不同的问题，该问题处理复杂的模式产生（鸟鸣生产）和人类语言理解。这两种应用都将需要分析大数据流和模型非线性序列产生或决策。尽管最初的研究重点将在这两个项目中，但该框架将建立，以适用于可能影响大脑计划下研究的各种神经科学项目。交互式示例将使用Mathematica和Matlab平台实施，团队还将在该赠款的主题上更新和编写新的Wikipedia页面。在所有项目中，毕业生和本科生都将参与研究和教育组成部分，不仅提供了进行研究的机会，还提供了提高他们的沟通能力。