We introduce copresheaf topological neural networks (CTNNs), a powerful and unifying framework that encapsulates a wide spectrum of deep learning architectures, designed to operate on structured data: including images, point clouds, graphs, meshes, and topological manifolds. While deep learning has profoundly impacted domains ranging from digital assistants to autonomous systems, the principled design of neural architectures tailored to specific tasks and data types remains one of the field's most persistent open challenges. CTNNs address this gap by grounding model design in the language of copresheaves, a concept from algebraic topology that generalizes and subsumes most practical deep learning models in use today. This abstract yet constructive formulation yields a rich design space from which theoretically sound and practically effective solutions can be derived to tackle core challenges in representation learning: long-range dependencies, oversmoothing, heterophily, and non-Euclidean domains. Our empirical results on structured data benchmarks demonstrate that CTNNs consistently outperform conventional baselines, particularly in tasks requiring hierarchical or localized sensitivity. These results underscore CTNNs as a principled, multi-scale foundation for the next generation of deep learning architectures.
View on arXiv@article{hajij2025_2505.21251,
title={ Copresheaf Topological Neural Networks: A Generalized Deep Learning Framework },
author={ Mustafa Hajij and Lennart Bastian and Sarah Osentoski and Hardik Kabaria and John L. Davenport and Sheik Dawood and Balaji Cherukuri and Joseph G. Kocheemoolayil and Nastaran Shahmansouri and Adrian Lew and Theodore Papamarkou and Tolga Birdal },
journal={arXiv preprint arXiv:2505.21251},
year={ 2025 }
}