most citedGraph Signal Processing for Infrastructure Resilience: Suitability and Future Directions

1 citations · 1 across the 5 of their papers we have counts for

collaborators

5 papers

cs.MA2021

Bayesian optimization of distributed neurodynamical controller models for spatial navigation

Armin Hadzic, Grace M. Hwang, Kechen Zhang +2

Dynamical systems models for controlling multi-agent swarms have demonstrated advances toward resilient, decentralized navigation algorithms. We previously introduced the NeuroSwar…

q-bio.NC2021

An interdisciplinary approach to high school curriculum development: Swarming Powered by Neuroscience

Elise Buckley, Joseph D. Monaco, Kevin M. Schultz +5

This article discusses how to create an interactive virtual training program at the intersection of neuroscience, robotics, and computer science for high school students. A four-da…

q-bio.NC2021

A brain basis of dynamical intelligence for AI and computational neuroscience

Joseph D. Monaco, Kanaka Rajan, Grace M. Hwang

The deep neural nets of modern artificial intelligence (AI) have not achieved defining features of biological intelligence, including abstraction, causal learning, and energy-effic…

cond-mat.soft2021

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…

eess.SP20201 cited

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…