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.
