1 citations · 1 across the 4 of their papers we have counts for
5 papers
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…
Detecting Anomalous Swarming Agents with Graph Signal Processing
Kevin Schultz, Anshu Saksena, Elizabeth P. Reilly +2
Collective motion among biological organisms such as insects, fish, and birds has motivated considerable interest not only in biology but also in distributed robotic systems. In a…
Analyzing Collective Motion Using Graph Fourier Analysis
Kevin Schultz, Marisel Villafane-Delgado, Elizabeth P. Reilly +2
Collective motion in animal groups, such as swarms of insects, flocks of birds, and schools of fish, are some of the most visually striking examples of emergent behavior. Empirical…
Graph Signal Processing for Infrastructure Resilience: Suitability and Future Directions
Kevin Schultz, Marisel Villafane-Delgado, Elizabeth P. Reilly +2
Graph signal processing (GSP) is an emerging field developed for analyzing signals defined on irregular spatial structures modeled as graphs. Given the considerable literature rega…
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,…