Graph Learning for Functional Neuronal Connectivity

Abstract: Understanding how large populations of neurons communicate in the brain at rest, in response to stimuli, or to produce behavior as well as how brain function relates to structure are fundamental open questions in neuroscience.  Many approach this by estimating the intrinsic functional neuronal connectivity using probabilistic graphical models.  But there remain major statistical and computational hurdles to estimating graphical models from new large-scale calcium imaging technologies and from huge projects which image up to one hundred thousand neurons across multiple sessions in the active mouse brain.  In this talk, I will highlight a number of new graph learning strategies my group has developed to address many critical unsolved challenges arising with large-scale neuroscience data.  Specifically, we will focus on Graph Quilting, in which we derive a method and theoretical guarantees for graph learning from non-simultaneously recorded neurons.  We will also highlight theory and methods for graph learning with latent variables induced by unrecorded neurons via thresholding, graph learning for spikey neuronal activity data via Subbotin graphical models, and computational approaches for graph learning from enormous numbers of neurons via minipatch learning.  Finally, we will demonstrate the utility of all approaches on synthetic data as well as real calcium imaging data for the task of estimating functional neuronal connectivity.  

 

Bio: Genevera Allen is a Professor of Statistics at Columbia University. She is also a member of the Center for Theoretical Neuroscience, the Zuckerman Institute for Mind, Brain, and Behavior, and the Irving Institute for Cancer Dynamics. Prior to joining Columbia, Dr. Allen spent fourteen years at Rice University in the Departments of Electrical and Computer Engineering, Statistics, and Computer Science; she was also the Founder and served as the Faculty Director of Rice’s data science education center, informally known as the Rice D2K Lab.

Dr. Allen’s research develops new statistical machine learning tools to help people make reliable discoveries from data. She is known for her methods and theory work in the areas of unsupervised learning, interpretable machine learning, data integration, graphical models, and high-dimensional statistics. Her work is motivated by solving real scientific problems, especially in the areas of neuroscience and bioinformatics.

Dr. Allen is the recipient of several honors including a National Science Foundation Career Award, Rice University’s Duncan Achievement Award for Outstanding Faculty, and in 2014, she was named to the “Forbes ’30 under 30′: Science and Healthcare” list. She is also an elected fellow of the American Statistical Association, Institute of Mathematical Statistics, and International Statistics Institute.

Dr. Allen serves as an Action Editor for the Journal of Machine Learning Research, an Associate Editor for the Journal of the American Statistical Association: Theory and Methods and the Journal of the Royal Statistical Society: Series B, on the editorial board of Foundations and Trends in Machine Learning and Annual Reviews of Statistics and Its Application, and finally as a Series Editor for Springer Texts in Statistics.

Dr. Allen received her Ph.D. in statistics from Stanford University, under the mentorship of Prof. Robert Tibshirani, and her bachelors, also in statistics, from Rice University.