239 citations · 428 across the 13 of their papers we have counts for
4 papers · 1 filter
Extracting low-dimensional dynamics from multiple large-scale neural population recordings by learning to predict correlations
Marcel Nonnenmacher, Srinivas C. Turaga, Jakob H. Macke
A powerful approach for understanding neural population dynamics is to extract low-dimensional trajectories from population recordings using dimensionality reduction methods. Curre…
Fast amortized inference of neural activity from calcium imaging data with variational autoencoders
Artur Speiser, Jinyao Yan, Evan Archer +3
Calcium imaging permits optical measurement of neural activity. Since intracellular calcium concentration is an indirect measurement of neural activity, computational tools are nec…
Large Scale Image Segmentation with Structured Loss based Deep Learning for Connectome Reconstruction
Jan Funke, Fabian David Tschopp, William Grisaitis +4
We present a method combining affinity prediction with region agglomeration, which improves significantly upon the state of the art of neuron segmentation from electron microscopy…
Anisotropic EM Segmentation by 3D Affinity Learning and Agglomeration
Toufiq Parag, Fabian Tschopp, William Grisaitis +6
The field of connectomics has recently produced neuron wiring diagrams from relatively large brain regions from multiple animals. Most of these neural reconstructions were computed…