3 papers
q-bio.NC2026
Graph neural networks uncover structure and functions underlying the activity of simulated neural assemblies
Cédric Allier, Larissa Heinrich, Magdalena Schneider +1
Graph neural networks trained to predict observable dynamics can be used to decompose the temporal activity of complex heterogeneous systems into simple, interpretable representati…
cs.LG2025
Decomposing heterogeneous dynamical systems with graph neural networks
Cédric Allier, Magdalena C. Schneider, Michael Innerberger +3
Natural physical, chemical, and biological dynamical systems are often complex, with heterogeneous components interacting in diverse ways. We show how simple graph neural networks…
physics.optics2025
DeepPD: Joint Phase and Object Estimation from Phase Diversity with Neural Calibration of a Deformable Mirror
Magdalena C. Schneider, Courtney Johnson, Cedric Allier +6
Sample-induced aberrations and optical imperfections limit the resolution of fluorescence microscopy. Phase diversity is a powerful technique that leverages complementary phase inf…