Ren Ozen is a researcher and graduate student at the Department of Cognitive Science at Carleton University.
Ren’s work concerns creating simple memory and learning based AI agents that can learn to perform reinforcement learning tasks such as navigating a maze or keeping a pencil balanced on the tip of a finger. Ren is also exploring how to build a programmable Turing machine in realistic, simulated neural circuits, and how to store and recover information in neural memory efficiently using the principles of holography.
Ozen, R., West, R. L., & Kelly, M. A. Minerva-Q: A Multiple-Trace Memory System for Reinforcement Learning. Learning, 1(1), 3.