Symmetry and Geometry in Neural Representations NeurIPS Workshops 2026

The fields of biological and artificial intelligence are increasingly converging on a shared principle: the mathematical structure of real-world tasks plays a central role in building efficient, robust, and interpretable representations. In neuroscience, mounting evidence suggests that neural circuits encode structure through low-dimensional manifolds, conserved symmetries, and structured transformations. In deep learning, principles such as sparsity, equivariance, and compositionality are guiding the development of more generalizable and interpretable models. The NeurReps workshop brings these threads together, fostering dialogue among machine learning researchers, neuroscientists, and mathematicians to uncover unifying geometric principles of neural representation. Following successful editions in 2022–2025 with over 160 submissions and 500 attendees last year, NeurReps 2026 expands into emerging frontiers including geometric representation steering and geometric mechanistic interpretability, and introduces a new Findings track to foster collaboration between experimentalists and theorists.