Travis DeWolf
PhD Student
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.
Publications
Theses
- (2014) A neural model of the motor control system. PhD thesis, University of Waterloo. Abstract External link
- (2010) NOCH: A framework for biologically plausible models of neural motor control. Master's thesis, University of Waterloo, Waterloo, ON. Abstract PDF External link
Journal Articles
- (2023) Neuromorphic control of a simulated 7-DOF arm using Loihi. Neuromorphic Computing and Engineering. Abstract External link
- (2020) Nengo and Low-Power AI Hardware for Robust Embedded Neurorobotics. Frontiers in Neurorobotics. Abstract External link
- (2017) Trajectory generation using a spiking neuron implementation of dynamic movement primitives. 27th Annual Meeting for the Society for the Neural Control of Movement. Abstract External link
- (2016) A spiking neural model of adaptive arm control. Proceedings of the Royal Society B. Abstract DOI
- (2015) Closed-Loop Neuromorphic Benchmarks. Frontiers in neuroscience. Abstract PDF
- (2014) Nengo: A Python tool for building large-scale functional brain models. Frontiers in Neuroinformatics. Abstract PDF DOI External link
- (2012) A large-scale model of the functioning brain. Science, 338:1202-1205. Abstract PDF Poster DOI External link
- (2011) The neural optimal control hierarchy for motor control. The Journal of Neural Engineering, 8:21. Abstract PDF External link
Conference and Workshop Papers
- (2022) BatSLAM: Neuromorphic Spatial Reasoning in 3D Environments. In 41st Digital Avionics Systems Conference. Portsmouth, VA, USA. DASC. Abstract PDF
- (2015) Spaun: A biologically realistic large-scale functional brain model. In Ontario and Canada Research Chairs Symposium. Council of Ontario Universities. Abstract PDF
- (2013) A neural model of the development of expertise. In Robert L. West & Terrence C. Stewart, editor, The 12th International Conference on Cognitive Modelling, 119-124. Ottawa, Ontario. Carleton University. Abstract PDF Poster
- (2013) Spaun: A Large-Scale Model of the Functioning Brain. In Cheriton Symposium. David R. Cheriton School of Computer Science. Abstract PDF
- (2011) A spiking neuron model of movement and pre-movement activity in M1. In Cognitive and Systems Neuroscience. Salt Lake City, UT. Abstract
- (2010) NOCH: A framework for biologically plausible models of neural motor control. In 20th Annual Neural Control of Movement Conference. Naples, FL. Abstract
Technical Reports and Preprints
- (2010) Dynamic scaling for efficient, low-cost control of high-precision movements in large environments. Technical Report, Center for Theoretical Neuroscience, Waterloo, ON. Abstract PDF
- (2009) NEF implementation of the Visuomotor Transformation Model. Technical Report, Centre for Theoretical Neuroscience. Abstract
- (2008) Motor control in the brain. Technical Report, Center for Theoretical Neuroscience, Waterloo, ON. Abstract PDF