brAIn Seminar - SueYeon Chung

— 4:30pm

Location:
In Person and Virtual - ET - Group Viewing Baker Hall 340A and Zoom

Speaker:
SUEYEON CHUNG, Assistant Professor, Center for Neural Science, New York University, and, Project Leader, Center for Computational Neuroscience, Flatiron Institute, Simons Foundation
https://sites.google.com/site/sueyeonchung/

Recent breakthroughs in experimental neuroscience and machine learning have opened new frontiers in understanding the computational principles governing neural circuits and artificial neural networks (ANNs). Both biological and artificial systems exhibit an astonishing degree of orchestrated information processing capabilities across multiple scales - from the microscopic responses of individual neurons to the emergent macroscopic phenomena of cognition and task functions. At the mesoscopic scale, the structures of neuron population activities manifest themselves as neural representations. Neural computation can be viewed as a series of transformations of these representations through various processing stages of the brain. The primary focus of my lab's research is to develop theories of neural representations that describe the principles of neural coding and, importantly, capture the complex structure of real data from both biological and artificial systems. 

In this talk, I will present three related approaches that leverage techniques from statistical physics, machine learning, and geometry to study the multi-scale nature of neural computation. First, I will introduce new statistical mechanical theories that connect geometric structures that arise from neural responses (i.e., neural manifolds) to the efficiency of neural representations in implementing a task. Second, I will employ these theories to analyze how these representations evolve across scales, shaped by the properties of single neurons and the transformations across distinct brain regions. Finally, I will demonstrate how insights from the theories of neural representations can elucidate why certain ANN models better predict neural data, facilitating model comparison and selection. 

— 

SueYeon Chung is an Assistant Professor in the Center for Neural Science at NYU, with a joint appointment in the Center for Computational Neuroscience at the Flatiron Institute, an internal research division of the Simons Foundation. She is also an affiliated faculty member at the Center for Data Science and Cognition & Perception Program at NYU. Prior to joining NYU, she was a Postdoctoral Fellow in the Center for Theoretical Neuroscience at Columbia University, and BCS Fellow in Computation at MIT. Before that, she received a Ph.D. in applied physics at Harvard University, and a B.A. in mathematics and physics at Cornell University. She received the Klingenstein-Simons Fellowship Award in Neuroscience in 2023, and the Sloan Research Fellowship in 2024. Her main research interests lie at the intersection between statistical physics, neuroscience and machine learning, with a particular focus on understanding and interpreting neural computation in biological and artificial neural networks by employing methods from neural network theory, statistical physics, and high-dimensional statistics. 

Group Viewing and Zoom Participation. See announcement.

Event Website:
https://brain.andrew.cmu.edu/seminar

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