Linear Readout of Neural Manifolds with Continuous Variables
Brains and artificial neural networks compute with continuous variables such as object position or stimulus orientation. However, the complex variability…
arXiv:2603.10956v1NeuroAI and Geometric Data Analysis
Our group develops mathematical theories for understanding how neurons collectively give rise to behavior in biological and artificial neural networks. Our current focus is on addressing this question through two broad approaches at the intersection of computational neuroscience and deep learning:
(1) analyzing geometries underlying neural or feature representations, embedding and transferring information, and (2) building neural network models and learning rules guided by neuroscience. To do this, we combine computational tools from theoretical physics, applied math, and machine learning. Alongside this theoretical work, we develop close collaborations with experimentalists to be inspired by and to test ideas on neural data.
NeuroAI & Geometric Data Analysis Lab
Current Papers
Diagnosing Generalization Failures from Representational Geometry Markers
Generalization, the ability to perform well beyond the training context, is a hallmark of biological and artificial intelligence, yet anticipating…
The Fourteenth International Conference on Learning RepresentationsEstimating Dimensionality of Neural Representations from Finite Samples
C. Chun, A. Canatar, S. Chung , Daniel Lee
The global dimensionality of a neural representation manifold provides rich insight into the computational process underlying both artificial and biological…
arXiv:2509.26560v2
