Seminar Title: Finding low-dimensional structure in large-scale neural recordings

Abstract:
Improvements in neural recording technologies have rapidly increased the number of neurons that it is now possible to record from. Along with these improvements, analyses of neural information processing are moving from single neuron to population-level analyses. One promising approach for understanding information processing across large populations of neurons is to use methods for dimensionality reduction; such approaches aim to find low-dimensional structure in the joint activity of many neurons over time. In this talk, I will describe my lab's efforts to learn low-dimensional structure present in large-scale neural recordings, both from electrophysiology recordings in motor cortex and from two-photon calcium movies in primary visual cortex. Our findings suggest that dimensionality reduction techniques can be used to pull out structure from neural activity to solve a range of decoding and classification problems.

Speaker’s Bio:
Eva Dyer is currently an Assistant Professor in the Wallace H. Coulter Department of Biomedical Engineering at the Georgia Institute of Technology and Emory University. Eva runs the Neural Data Science (NerDS) Lab, where she and her team develop new machine learning and data science approaches for making sense of large-scale neural datasets. Before joining Georgia Tech, she was a Research Scientist in the Bayesian Behavior Lab at Northwestern University, where she worked with Konrad Kording. Eva completed all of her degrees in Electrical & Computer Engineering, including a Ph.D. and M.S. from Rice University, and a B.S. from University of Miami. While at Rice, she worked in the Digital Signal Processing Group with Richard Baraniuk and had the opportunity to co-develop the edX MOOC Discrete-Time Signals and Systems. During her undergraduate studies at the University of Miami, she worked as a sound designer for the award-winning documentary One Water: A collaborative effort for a sustainable future.