17 citations · 19 across the 4 of their papers we have counts for
6 papers
Efficient Connectivity-Preserving Instance Segmentation with Supervoxel-Based Loss Function
Anna Grim, Jayaram Chandrashekar, Uygar Sumbul
Reconstructing the intricate local morphology of neurons and their long-range projecting axons can address many connectivity related questions in neuroscience. The main bottleneck…
Learning Time-Invariant Representations for Individual Neurons from Population Dynamics
Lu Mi, Trung Le, Tianxing He +2
Neurons can display highly variable dynamics. While such variability presumably supports the wide range of behaviors generated by the organism, their gene expressions are relativel…
Mixture Representation Learning with Coupled Autoencoders
Yeganeh M. Marghi, Rohan Gala, Uygar Sümbül
Jointly identifying a mixture of discrete and continuous factors of variability without supervision is a key problem in unraveling complex phenomena. Variational inference has emer…
New Light on Cortical Neuropeptides and Synaptic Network Plasticity
Stephen J. Smith, Michael Hawrylycz, Jean Rossier +1
Neuropeptides, members of a large and evolutionarily ancient family of proteinaceous cell-cell signaling molecules, are widely recognized as extremely potent regulators of brain fu…
A coupled autoencoder approach for multi-modal analysis of cell types
Rohan Gala, Nathan Gouwens, Zizhen Yao +6
Recent developments in high throughput profiling of individual neurons have spurred data driven exploration of the idea that there exist natural groupings of neurons referred to as…
Reconstructing neuronal anatomy from whole-brain images
James Gornet, Kannan Umadevi Venkataraju, Arun Narasimhan +5
Reconstructing multiple molecularly defined neurons from individual brains and across multiple brain regions can reveal organizational principles of the nervous system. However, hi…