Symmetry and Geometry in Neural Representations
A gathering of mathematicians, machine learning researchers, and neuroscientists working to uncover the geometric principles shared by brains and machines.
The fields of biological and artificial intelligence are converging on a shared principle: the geometry and topology of real-world structure play a central role in building efficient, robust, and interpretable representations.
In neuroscience, mounting evidence suggests that neural circuits encode task and environmental structure through low-dimensional manifolds, conserved symmetries, and structured transformations. In deep learning, sparsity, equivariance, and compositionality are guiding the development of more generalizable and interpretable models.
NeurReps brings these threads together — fostering dialogue among machine learning researchers, neuroscientists, and mathematicians working to uncover unifying geometric principles of neural computation.
Just as geometry and symmetry once unified the models of twentieth-century physics, we believe they will now illuminate the computational foundations of intelligence.
Invited Speakers
Five talks spanning the geometry of brain computation, mechanistic interpretability, and foundation models.
Organizers
Area Chairs
Call for Papers
Two tracks, one poster session — a subset of submissions will be selected for spotlight talks. Full details and deadlines coming soon.
Proceedings Track
Self-contained, highly-developed research papers. Archivally published in a dedicated PMLR volume.
Extended Abstract Track
Early-stage results, negative findings, opinion pieces, or novel datasets. Non-archival — may be posted to arXiv.
Sponsors
Past Editions
A running history of NeurReps at NeurIPS — organizers, speakers, and proceedings from each year.