Vision
Extract and translate detailed information about neural connectivity and function across different species to create the next generation of robust, efficient intelligent systems operating in the real world.
Research
Connectomics-Derived Neural Networks
Exploiting large scale datasets (e.g. such FlyEM dataset, IARPA MICrONS dataset) to discover novel patterns of connectivity for the next generation recurrent neural networks.
Matelsky, J.K., Reilly, E.P., Johnson, E.C., Stiso, J., Bassett, D.S., Wester, B.A. and Gray-Roncal, W., 2021 "DotMotif: an open-source tool for connectome subgraph isomorphism search and graph queries." Scientific reports 11, no. 1 (2021): 1-14.
Neuroscience-Inspired Agents
Developing increasingly complex autonomous agents with neurally-inspired control policies.
Monaco, J.D., Hwang, G.M., Schultz, K.M. and Zhang, K., 2020. Cognitive swarming in complex environments with attractor dynamics and oscillatory computing. Biological cybernetics, 114(2), pp.269-284.
Robinson, B.S., Norman-Tenazas, R., Cervantes, M., Symonette, D., Johnson, E.C., Joyce, J., Rivlin, P.K., Hwang, G., Zhang, K. and Gray-Roncal, W., 2022. Online learning for orientation estimation during translation in an insect ring attractor network. Scientific reports, 12(1), pp. 1-15.
For more information or to join our team, please contact us at ISC@jhuapl.edu