The fields of biological and artificial intelligence are increasingly 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, principles such as sparsity, equivariance, and compositionality are guiding the development of more generalizable and interpretable models, including new approaches to foundation model distillation. 

The NeurReps workshop brings these threads together, fostering dialogue among machine learning researchers, neuroscientists, and mathematicians to uncover unifying geometric principles of neural representation. Just as geometry and symmetry once unified the models of 20th-century physics, we believe they may now illuminate the computational foundations of intelligence. We aim to bring together researchers from applied mathematics and deep learning with neuroscientists whose work reveals the elegant implementation of mathematical structure in biological neural circuitry. 

We are excited to announce our 4th annual NeurIPS workshop this December. This year’s workshop expands into emerging frontiers, including geometric mechanistic interpretability, geometry of representations in foundation models of brain activity, and improvements of LLM design guided by geometric understanding of transformers. Our past invited and contributed talks drew exciting connections between trends in geometric deep learning and neuroscience, emphasizing parallels between equivariant structures in brains and machines. This year's workshop will feature five invited talks covering emerging topics on the geometry underlying the brains computations, the role of topology in deep learning and the brain,  how we can use insights from classic geometric deep learning to improve foundation models, the forefront in foundation models of the brain as well as how we can extract the brains computational structure with mechanistic interpretability,  and, finally,  the role of dynamics in shaping neural representations.