Fabrication and Performance of Hybrid ReRAM-CMOS Circuit Elements for Dynamic Neural Networks. - Maximilian Liehr; Jubin Hazra; Karsten Beckmann; Wilkie Olin-Ammentorp; Nathaniel C. Cady; Ryan Weiss; Sagarvarma Sayyaparaju; Garrett S. Rose; Joseph Van Nostrand (ICONS, 2019)

Training Spiking Networks via Natural Evolution Strategies. - Wilkie Olin-Ammentorp; Nathaniel C. Cady (ICONS, 2019)

Cellular Memristive-Output Reservoir (CMOR). - Wilkie Olin-Ammentorp; Karsten Beckmann; Nathaniel C. Cady (CoRR, 2019)

Towards Synaptic Behavior of Nanoscale ReRAM Devices for Neuromorphic Computing Applications. - Karsten Beckmann; Wilkie Olin-Ammentorp; Gangotree Chakma; Sherif Amer; Garrett S. Rose; Chris Hobbs; Joseph Van Nostrand; Martin Rodgers; Nathaniel C. Cady (ACM J. Emerg. Technol. Comput. Syst., 2020)

Bridge Networks: Relating Inputs through Vector-Symbolic Manipulations. - Wilkie Olin-Ammentorp; Maxim Bazhenov (ICONS, 2021)

Bridge Networks. - Wilkie Olin-Ammentorp; Maxim Bazhenov (CoRR, 2021)

Stochasticity and robustness in spiking neural networks. - Wilkie Olin-Ammentorp; Karsten Beckmann; Catherine D. Schuman; James S. Plank; Nathaniel C. Cady (Neurocomputing, 2021)

A dual-memory architecture for reinforcement learning on neuromorphic platforms. - Wilkie Olin-Ammentorp; Yury Sokolov; Maxim Bazhenov (Neuromorph. Comput. Eng., 2021)

Deep Phasor Networks: Connecting Conventional and Spiking Neural Networks. - Wilkie Olin-Ammentorp; Maxim Bazhenov (IJCNN, 2022)

Residual and Attentional Architectures for Vector-Symbols. - Wilkie Olin-Ammentorp; Maxim Bazhenov (CoRR, 2022)

Sparsifying Spiking Networks through Local Rhythms. - Wilkie Olin-Ammentorp (ICONS, 2023)

Hyperdimensional Computing Provides Computational Paradigms for Oscillatory Systems. - Wilkie Olin-Ammentorp (ICRC, 2024)

Modeling Performance of Data Collection Systems for High-Energy Physics. - Wilkie Olin-Ammentorp; Xingfu Wu; Andrew A. Chien (CoRR, 2024)