4 citations · 6 across the 6 of their papers we have counts for
6 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…
EM and XRM Connectomics Imaging and Experimental Metadata Standards
Miguel E. Wimbish, Nicole K. Guittari, Victoria A. Rose +8
High resolution volumetric neuroimaging datasets from electron microscopy (EM) and x-ray micro and holographic-nano tomography (XRM/XHN) are being generated at an increasing rate a…
SciOps: Achieving Productivity and Reliability in Data-Intensive Research
Erik C. Johnson, Thinh T. Nguyen, Benjamin K. Dichter +16
Scientists are increasingly leveraging advances in instruments, automation, and collaborative tools to scale up their experiments and research goals, leading to new bursts of disco…
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…
A Community-Developed Open-Source Computational Ecosystem for Big Neuro Data
Randal Burns, Eric Perlman, Alex Baden +27
Big imaging data is becoming more prominent in brain sciences across spatiotemporal scales and phylogenies. We have developed a computational ecosystem that enables storage, visual…
Neural Reconstruction Integrity: A metric for assessing the connectivity of reconstructed neural networks
Elizabeth P. Reilly, Jeffrey S. Garretson, William Gray Roncal +4
Neuroscientists are actively pursuing high-precision maps, or graphs, consisting of networks of neurons and connecting synapses in mammalian and non-mammalian brains. Such graphs,…