NeurIPS 2026 Workshop: Symmetry and Geometry in Neural Representations

General Info

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.

Call for Papers

Important dates:

• Submission Deadline: August 22, 2026 · AoE
• Accept / Reject Notification: September 29, 2026

Types of submission:

• Proceedings Track: Self-contained, highly-developed research papers. Archivally published in a dedicated PMLR volume. Double-blind review via OpenReview.
• Extended Abstract Track: Early-stage results, negative findings, opinion pieces, or novel datasets. Non-archival — may be posted to arXiv. Double-blind review via OpenReview.
• Findings Track (New this year): high-impact collaborative work between experimentalists and theorists, in any standard preprint format. Single-blind, editorially reviewed by an advisory panel of experts in the field.

We invite submissions of novel research at the intersection of applied mathematics, deep learning, and computational neuroscience — work that incorporates symmetry and geometry into neural network design, the mechanistic interpretability of neural systems (biological or artificial), or theories of neural computation. We welcome contributions spanning geometric and topological deep learning, computational and theoretical neuroscience, geometric statistics, and topological data analysis.

Particularly relevant themes:

• Theory and methods for learning invariant and equivariant representations
• Statistical learning theory in the context of topology, geometry, and symmetry
• Representational geometry in neural data
• Learning and leveraging group structure in data
• Equivariant world models for robotics
• Dynamics of neural representations
• Topological deep learning and topological data analysis
• Geometric structure in language
• Geometric and topological analysis of generative models
• Symmetries, dynamical systems, and learning

We hope to see both theoretical contributions and applied results in domains including vision, motor control, navigation, and language, as well as the use of diverse mathematical objects such as quotient spaces, fiber bundles, Lie groups, Riemannian manifolds, graphs, topological domains, and group representations. We also welcome benchmark datasets and software. This list is guidance, not exhaustive — if you are unsure whether your work is in scope, please reach out to the organizers.

Source and more details: https://neurreps.org/#cfp