Simple mathematical expressions can often precisely describe animal behaviors during simple laboratory tasks; however, the specific inner workings of the brain reflected by its complex activity patterns have remained elusive. This project aims to transform the mathematical and computational analysis paradigm for understanding the fundamental organizational principles of neural systems through the development of novel neural data analysis tools. In particular, the computational strategies used by the animals for two behaviors: to perceive if a random visual pattern is moving toward left or right, and to combine information from visual and auditory senses to determine if they are from a potential predator, will be analyzed and visualized as hidden forces acting on the internal brain states from moment to moment. These tools will also be applied to studying disorders of consciousness in coma patients. Successful achievement of these goals will provide new analytical tools to accelerate systems-level understanding of cognition and inspire development of new treatment devices for neurological disorders. The outreach goal of this project is to increase the awareness of the general public on how the nervous system functions through short-form online educational videos that will explore misconceptions and inaccurate analogies made between computer systems and the brain, and thereby stimulate their curiosity and desire to understand how the nervous system functions at different levels. The educational goal of this project is to enrich the computational neuroscience community through curriculum development and engaging high school, undergraduate, and graduate students in the cutting-edge research and trained in advanced quantitative methodologies in computational neuroscience and neural engineering.
The goals of this project are to develop both real-time and offline analysis methods to infer the normal and abnormal cognitive dynamics, that is, the internal mental processes that intelligently link sensory and motor function, manifested within the neural systems from streaming neural signals. The neural data analysis techniques will be tailored for extracting interpretable dynamics from neural time series by utilizing the low-dimensional dynamical system structure and recent developments in nonlinear state-space modeling and recurrent neural networks in machine learning. The extracted dynamics and phase portraits are expected to serve as an integral part of human understandable description of how the system operates at an intermediate scale where population state rather than individual neurons describe neural computation. The outcomes of this project will aid the development of translational medicine applications of closed-loop brain stimulation, and the education of the next generation computational neuroscientists.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
