Travis’ research focuses on studying the brain’s motor control system. Using modern control theoretic methods, such as operational space control, nonlinear adaptive control, and dynamic movement primitives, he has worked to develop biologically plausible spiking neural networks that model the brain, capable of generating the same diversity of behavioural phenomena and robust adaptation / learning seen in primates.
He received his undergraduate degree in computer science at Acadia University, with a thesis discussing the algebraic properties of template-guided DNA recombination. His masters degree was in computer science at the University of Waterloo, and focused on the development of the Neural Optimal Control Hierarchy (NOCH); a biologically plausible framework for large-scale models of the motor control system. His Ph.D. was in systems design engineering at the University of Waterloo, where he presented the Recurrent Error-driven Adaptive Control Hierarchy (REACH) model; a large-scale, fully spiking neural model of the motor cortices and cerebellum able to account for data from 19 studies from a behavioural level down to the level of single spiking neurons.