5 papers
Sheaf Neural Networks on SPD Manifolds: Second-Order Geometric Representation Learning
Yuhan Peng, Junwen Dong, Yuzhi Zeng +6
Graph neural networks face two fundamental challenges rooted in the linear structure of Euclidean vector spaces: (1) Current architectures represent geometry through vectors (direc…
Cross-Session Decoding of Neural Spiking Data via Task-Conditioned Latent Alignment
Canyang Zhao, Bolin Peng, J. Patrick Mayo +2
Training a high-performing neural decoder can be difficult when only limited data are available from a recording session. To address this challenge, we propose a Task-Conditioned L…
SPD Learn: A Geometric Deep Learning Python Library for Neural Decoding Through Trivialization
Bruno Aristimunha, Ce Ju, Antoine Collas +5
Implementations of symmetric positive definite (SPD) matrix-based neural networks for neural decoding remain fragmented across research codebases and Python packages. Existing impl…
SPD Matrix Learning for Neuroimaging Analysis: Perspectives, Methods, and Challenges
Ce Ju, Reinmar Kobler, Antoine Collas +3
Neuroimaging provides essential tools for characterizing brain activity, structure, and connectivity through modalities that capture complementary aspects of brain organization. Ac…
Riemannian Flow Matching for Brain Connectivity Matrices via Pullback Geometry
Antoine Collas, Ce Ju, Nicolas Salvy +1
Generating realistic brain connectivity matrices is key to analyzing population heterogeneity in brain organization, understanding disease, and augmenting data in challenging class…