Helen Kang

Estimating Integrated Information from Non-Gaussian Multivariate Time Series

Information theory has been key to our understanding of the brain, by elucidating mechanisms of neural coding in the brains sensory periphery. Recent mathematical work has derived a new measure called integrated information, which can quantify how much information emerges at the level of an entire network. Integrated information has already been used to study how the structure of brain networks might underpin large-scale information flow and may help explain mechanisms of information processing in both healthy and pathological brain states. Currently, integrated information can only be calculated for neural time-series data if those data are multivariate Gaussian. This limits the application of integrated information to most types of neural data, which are typically non-Gaussian. I will verify a method I have developed to estimate integrated information from non-Gaussian time series simulated from brain-like networks. I will also use this method to test the long-held but unverified hypothesis that large-scale cortical information integration should decrease during unconscious states, such as anesthesia, seizures, and slow-wave sleep.

Message to Sponsor

Thank you Rose Hills Foundation, for this valuable opportunity and generous support! The fellowship enabled me to conduct independent, full-time research for the first time, and it reassured me that I do want to pursue a career in research. Through the fellowship, I realized being a researcher would be a great career path, and that neuroscience will continue to be my primary interest of research. I wouldnt have been able to come to this realization so early on in my career if it wasnt for the Rose Hills Foundation. I am grateful that I had this opportunity!
  • Major: MCB-Neurobiology and Mathematics
  • Sponsor: Rose Hills Independent
  • Mentor: Friedrich Sommer