NeurIPS 2025
December 6th or 7th, 2025
San Diego, USA
An emerging set of findings in sensory and motor neuroscience is beginning to illuminate a new paradigm for understanding the neural code. Across sensory and motor regions of the brain, neural circuits are found to mirror the geometric and topological structure of the systems they represent—either in their synaptic structure, or in the implicit manifold generated by their activity. This phenomenon can be observed in the circuit of neurons representing head direction in the fly (Kim et al. (2017); Wolff et al. (2015); Chaudhuri et al. (2019)), in the activities of grid cells (Gardner et al. (2022)), and in the low-dimensional manifold structure observed in motor cortex (Gallego et al. (2017)). This suggests a general computational strategy that is employed throughout the brain to preserve the geometric structure of data throughout stages of information processing.
Independently but convergently, this very same computational strategy has emerged in the field of deep learning. The nascent sub-field of Geometric Deep Learning (Bronstein et al. (2021)) incorporates geometric priors into artificial neural networks to preserve the geometry of signals as they are passed through layers of the network. This approach provably demonstrates gains in the computational efficiency, robustness, and generalization performance of these models.
The convergence of these findings suggests deep, substrate-agnostic principles for information processing. Symmetry and geometry were instrumental in unifying the models of 20th-century physics. Likewise, they have the potential to illuminate unifying principles for how neural systems form useful representations of the world.
The NeurReps Workshop brings 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. 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, 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 the role of dynamics in shaping neural representations.
University of Fribourg
Francisco Acosta
UC Santa Barbara
Rishi Sonthalia (Boston University)
Thomas Gebhart (University of Minnesota)
Paxon Frady (Intel)
Maghesree Chakraborty (Intel)
Mustafa Hajij (UCSF)
Jacob Zavatone-Veth (Harvard)
Sjoerd van Steenkiste (Google)
Eric Qu (UC Berkeley)
Arjun Karuvally (UMass Amherst)
Alex Williams (NYU & Flatiron Institute)
Valentino Maiorca (Sapienza University & ISTA)
Bo Zhao (UCSD)
Jianke Yang (UCSD)
Brian Bell (Los Alamos National Lab)
Salvish Goomanee (CNRS)
Rana Muhammad Shahroz Khan (UNC Chapel Hill)
Charlie Godfrey (Thomson Reuters Labs)
Binxu Wang (Harvard)
Derek Lim (MIT)
Yanan Long (University of Chicago)
Manos Theodosis (Harvard)
Nikos Kanakaris (USC)
Adrish Dey (Boston University)
Abhinav Kumar (Michigan State University)
Dehong Xu (UCLA)
Qingsong Wang (University of Utah)
Aslan Satary Dizaji (AutocurriculaLab)
Johan Mathe (Atmo, Inc.)
Vinayak Abrol (IIT Delhi)
Kijung Yoon (Hanyang University)
Nirupama Tiwari (IISc Bangalore)
Arif Dönmez (IUF Leibniz)
Daniel Apraez (IIT, Genova)
Stephan Chalup (The University of Newcastle)
Marco Pegoraro (Sapienza University)
Sharvaree Vadgama (UvA)
Aishwarya Balwani (Georgia Tech)
Santiago Velasco-Forero (Mines Paris)
Julian Suk (University of Twente)
Emanuele Rodola’ (Sapienza University)
Shreya Kapoor (FAU Erlangen-Nüremberg)
Yu Tian (Nordita)
Tao Hu (LMU)
Tomas Karella (Czech Academy of Sciences)
Tobias Cheung (University of Edinburgh)
Marco Piangerelli (Camerino University)
Irene Cannistraci (Sapienza University)
Maksim Zhdanov (UvA)
Paul Samuel Ignacio (University of the Philippines Baguio)
Daniel Platt (Imperial College)
Dongmian Zou (Duke Kunshan University)
Wolfgang Polonik (UC Davis)
Alpha Renner (Forschungszentrum Jülich)
Behrooz Tahmasebi (MIT)
Yu (Demi) Qin (Tulane University)
Wenhao Zhang (UT Southwestern)
Samuele Papa (UvA)
Congyue Deng (Stanford)
Jinen Setpal (DagsHub)
Ruqi Zhang (Purdue University)
Ruchira Dhar (University of Copenhagen)
Jens Agerberg (KTH)
Vasco Portilheiro (UCL)
Barbaresco Frédéric (THALES)