Dr. Xuan Choo

Xuan has a rather wide set of research interests. First, he is interested in working and long-term memory – how does the brain remember things, and the type of representations used to do so. Of related interest are the mechanisms that the brain uses to integrate different sensory inputs within working memory, as well as the underlying structure of working memory and the control systems necessary to ensure that information arrives at the right place at the right time. Xuan is also interested in general cognition and large-scale model design. He studies and models how the brain performs tasks like concept generalization and problem solving. He is also interested in the problem of integration: how does one build a system that seamlessly melds sensory input, with decision making and problem solving, with motor output, in order to perform highly complex tasks? Xuan received his undergraduate in Computer Engineering from the University of British Columbia. There, he focused on the integration between computer hardware and software systems, as well as FPGA and VLSI chip design. For his Master’s degree, he worked under the supervision of Dr. Chris Eliasmith at the University of Waterloo, and developed a spiking neural model of serial-order working memory. Xuan took a year between his Master’s and PhD degrees to develop Spaun, a proof-of-concept fully-self- enclosed spiking neural model capable of performing 8 basic cognitive tasks ranging from digit recognition, to list memorization and counting, and even simple induction tasks. He is currently pursuing his PhD, focusing on further developing and improving Spaun, with the goal of making the underlying architecture of Spaun more general. This will allow external instructions to guide Spaun’s actions, expanding its repertoire of tasks beyond the original 8.