14 citations · 17 across the 3 of their papers we have counts for
4 papers
Nanoscale Connectomics Annotation Standards Framework
Nicole K. Guittari, Miguel E. Wimbish, Patricia K. Rivlin +8
The promise of large-scale, high-resolution datasets from Electron Microscopy (EM) and X-ray Microtomography (XRM) lies in their ability to reveal neural structures and synaptic co…
Exploiting Large Neuroimaging Datasets to Create Connectome-Constrained Approaches for more Robust, Efficient, and Adaptable Artificial Intelligence
Erik C. Johnson, Brian S. Robinson, Gautam K. Vallabha +17
Despite the progress in deep learning networks, efficient learning at the edge (enabling adaptable, low-complexity machine learning solutions) remains a critical need for defense a…
Latent Feature Representation via Unsupervised Learning for Pattern Discovery in Massive Electron Microscopy Image Volumes
Gary B Huang, Huei-Fang Yang, Shin-ya Takemura +2
We propose a method to facilitate exploration and analysis of new large data sets. In particular, we give an unsupervised deep learning approach to learning a latent representation…
Annotating Synapses in Large EM Datasets
Stephen M. Plaza, Toufiq Parag, Gary B. Huang +3
Reconstructing neuronal circuits at the level of synapses is a central problem in neuroscience and becoming a focus of the emerging field of connectomics. To date, electron microsc…