Dr. Uygar Sümbül will give a seminar on “Multimodal characterization of neuronal cell types” on December 4 at 10 am. The abstract of the talk and a short bio is shared below.

Bio:
Uygar Sümbül is an Assistant Investigator at Allen Institute, Seattle, where he focuses on the intersection of neuroscience and machine learning. He obtained a Ph.D. in Electrical Engineering and a Ph.D. minor in Mathematics from Stanford University in 2009. Prior to joining the Allen Institute, Uygar held postdoctoral researcher positions at the Dept. of Brain and Cognitive Sciences at MIT, and the Dept. of Statistics & Grossman Center for the Statistics of Mind at Columbia University. He completed a BS degree in Electrical Engineering at Bilkent University, Turkey, in 2003.

Abstract:
Consistent identification of neurons in different experimental modalities is a key problem in neuroscience. While paired multimodal measurements to simultaneously characterize single neurons have become available, parsing complex relationships across different modalities to uncover neuronal identity is a growing challenge. We present an optimization framework to learn coordinated representations of multimodal data and apply it to a large multimodal dataset profiling mouse cortical interneurons. Our approach reveals strong alignment between transcriptomic and electrophysiological characterizations, enables accurate cross-modal data prediction and identifies cell types that are consistent across modalities.