4 citations · 5 across the 6 of their papers we have counts for
6 papers
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…
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…
DaCapo: a modular deep learning framework for scalable 3D image segmentation
William Patton, Jeff L. Rhoades, Marwan Zouinkhi +9
DaCapo is a specialized deep learning library tailored to expedite the training and application of existing machine learning approaches on large, near-isotropic image data. In this…
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…
Synaptic Cleft Segmentation in Non-Isotropic Volume Electron Microscopy of the Complete Drosophila Brain
Larissa Heinrich, Jan Funke, Constantin Pape +2
Neural circuit reconstruction at single synapse resolution is increasingly recognized as crucially important to decipher the function of biological nervous systems. Volume electron…
Deep Learning for Isotropic Super-Resolution from Non-Isotropic 3D Electron Microscopy
Larissa Heinrich, John A. Bogovic, Stephan Saalfeld
The most sophisticated existing methods to generate 3D isotropic super-resolution (SR) from non-isotropic electron microscopy (EM) are based on learned dictionaries. Unfortunately,…