Transfer Learning Analysis of Image Processing Workflows for Electron Microscopy Datasets. - Erik C. Johnson; Luis M. Rodriguez; Raphael Norman-Tenazas; Daniel Xenes; William R. Gray Roncal (ACSSC, 2019)

Continuous State Estimation With Synapse-constrained Connectivity. - Raphael Norman-Tenazas; Brian S. Robinson; Justin Joyce; Isaac Western; Erik C. Johnson; William R. Gray Roncal; Joan A. Hoffmann (IJCNN, 2022)

Using Evolutionary Computation to Optimize Task Performance of Unclocked, Recurrent Boolean Circuits in FPGAs. - Raphael Norman-Tenazas; David Kleinberg; Erik C. Johnson; Daniel P. Lathrop; Matthew J. Roos (ACSSC, 2023)

Enabling local learning for generative-replay-based continual learning with a recurrent model of the insect memory center. - Raphael Norman-Tenazas; Isaac Western; Gautam K. Vallabha; Matthew J. Roos; Erik C. Johnson; Brian S. Robinson (ICONS, 2023)

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; Justin Joyce; Jordan K. Matelsky; Raphael Norman-Tenazas; Isaac Western; Marisel Villafañe-Delgado; Martha Cervantes; Michael S. Robinette; Arun V. Reddy; Lindsey Kitchell; et al. (CoRR, 2023)

Towards Trustworthy Distributed AI Demand Response. - Raphael Norman-Tenazas; Cameron Hickert; Tamim Sookoor; Aurora C. Schmidt; Edward Staley; Joseph Maurio; Jesse Silverberg; Paul Wood; Yair Amir (ICAA, 2024)

Large Language Models are Highly Aligned with Human Ratings of Emotional Stimuli. - Mattson Ogg; Chace Ashcraft; Ritwik Bose; Raphael Norman-Tenazas; Michael Wolmetz (CoRR, 2025)