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

这个EAGER项目的目标是建立和应用一个计算工具箱来研究和模拟大脑中的幂律动力学。传统上，神经科学中的任何复杂行为都被分解为多个组成部分的相互作用，每个组成部分都在自己特有的时间框架中工作。然而，有越来越多的例子，如在脑活动记录的脑电图（EEG），发射率适应，和突触权重动态，其中的特征过程遵循幂律动力学，这表明在一个尺度上的机制的时间常数是高度相关的系统在多个尺度上的活动。因此，系统的整体行为不能被分成很大程度上独立的组件，传统的分析技术不能提供系统如何工作的适当描述。为了理解神经元在多个尺度上的信息处理，有必要建立一个框架来分析和模拟生物组织各个层次的幂律动力学。该项目计划广泛提供一个统一的平台，以检测，分析，验证和建模在神经系统中的幂律行为在多个尺度的组织。为了扩大影响力，该团队将为公众提供产品，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。研究机会将提供给学生，特别是在德克萨斯大学圣安东尼奥（UTSA），少数民族服务机构代表性不足的群体。合作团队将分析和建模大规模大脑活动和复杂行为中的幂律关系。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，并对幂律动力系统进行建模。为此，国家的最先进的算法将被用来表征实验数据和分数阶微分方程模型幂律动力系统。该建模平台将允许从亚细胞到行为尺度的幂律过程的研究。该工具箱将应用于两个非常不同的问题，处理复杂的模式生成（鸟鸣生产）和人类语言理解。这两个应用程序都需要分析大数据流，并对非线性序列生产或决策进行建模。虽然最初的研究重点将集中在这两个项目上，但框架将被构建为适用于广泛的神经科学项目，这些项目可能会影响在BRAIN计划下进行的研究。交互式示例将使用Mathematica和Matlab平台实现，该团队还将更新并编写有关此资助主题的新维基百科页面。在所有项目中，研究生和本科生将参与研究和教育部分，不仅提供做研究的机会，而且提高他们的沟通能力。

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.