SueYeon Chung

My lab focuses on developing theories of neural representations to understand neural coding and the relationship between neural geometry and computation in biological and artificial neural networks. This involves creating theories, methods, and models that connect multiple scales and levels of abstraction.   

We are developing new theoretical frameworks for evaluating the structure of high-dimensional neural data, using the geometry of neural population activities to understand the computational processing within the representation.   

Our research includes:

Theory of Neural Manifolds: Connecting geometric structures from neural population responses to the efficiency of neural representation in performing a task.   

Multi-Level Population Coding Frameworks: Connecting neural manifold geometry and coding capacity to single neuron measures, like tuning curve properties.   

Neuro-Inspired Deep Network Models: Using insights from neuroscience to develop new brain models, particularly artificial neural networks.   

Our research aims to bridge the gap between microscopic responses of individual neurons and the macroscopic phenomena of cognitive and task functions.