Leenoy Meshulam is a Swartz postdoctoral fellow at the University of Washington. Her primary focus is on how complex interactions in large neuronal populations are coordinated to give rise to function and computation. Her research draws on theoretical frameworks from statistical physics and dynamical systems to uncover principles of brain function. Additionally, she uses analytical and computational methods to study how anatomical connections constrain network interactions. She has been serving as junior chair of the International Brain Laboratory theory group since 2019, after joining the collaboration while working at the Massachusetts Institute of Technology. As part of this project, she also serves as a mentor in the SCGB SURF program, whose goal is to spark interest in computational neuroscience among undergraduate students from diverse backgrounds. Meshulam obtained her Ph.D. from Princeton University under the supervision of Bill Bialek, David Tank and Carlos Brody. Prior to that she completed her master’s degree in physics and biology at Tel Aviv University in Israel.
Principal Investigator: Ila Fiete
Fellow: Patrick Udeh
“Modular multi-scale information flow in large neural networks”
Every decision, action or thought we make involves millions of neurons rapidly interacting with each other all across the brain. How this incredibly complex network of communication gives rise to our behavior is still largely unknown. We draw on the theoretical richness of network theory and control theory to develop a set of scalable computational and analytical tools that will elucidate brain-wide information flow patterns. We will analyze hundreds of large-scale electrophysiology recordings of neural data from laboratories across the world as part of the theory effort of the International Brain Laboratory, an international consortium of neuroscientists investigating how the brain executes decisions. We will investigate how different network elements in the brain facilitate information flow across regions, how network organization changes during a decision-making process, and what the role of different spatial and temporal scales is in creating meaningful sub-networks. Together, these investigations will decipher how sensory, motor and cognitive information flows between regions, and how it gets incorporated into large-scale network dynamics that give rise to a decision. As a bonus, this project will provide the field with a set of network-level analysis tools that are deeply rooted in theory and developed for large-scale cellular-resolution datasets.