Bryan Patrick Tripp

Associate Professor

Contact InformationBryan Patrick Tripp

Phone: 519-888-4567 x31382
Location: DWE 2501

Website

Biography Summary

Bryan Tripp is an Assistant Professor with the Department of Systems Design Engineering and the Centre for Theoretical Neuroscience at the University of Waterloo.

He is interested in understanding how the brain processes information. Professor Tripp is currently studying the visual and motor systems of primates. About half the human brain is dedicated to seeing and moving. We have constant experience with these things, but we only vaguely understand how they work. Professor Tripp believes that to understand these things clearly, we must build computational systems that see and move like humans.

Professor Tripp aims to develop a realistic large-scale model of the visual-motor networks of the primate brain. Biological vision and motor systems outperform artificial systems in many ways, so a better understanding of these systems may lead to technological advances.

Research Interests

  • Computational Neuroscience
  • Deep Learning
  • Advanced Robotics

Courses

  • SYDE 544 - Biomedical Measurement and Signal Processing
  • SYDE 750 - Topics in Systems Modelling
  • SYDE 552 - Computational Neuroscience
  • BME 261 - Prototyping, Simulation and Design
  • BME 201 - Seminar
  • BME 355 - Anatomical Systems Modelling
  • SYDE 770 - Selected Topics in Communication and Information Systems

Selected/Recent Publications

  • Huber, Scott and Selby, Ben and Tripp, Bryan, OREO: An open-hardware robotic head that supports practical saccades and accommodation, IEEE Robotics Automation Letters, in press. (Accepted in 2018)
  • Selby, Ben and Tripp, Bryan, Extending the Stabilized Supralinear Network model of V1 for binocular image processing, 2017, Neural Networks 90: 29-41. (Accepted in 2017)
  • Nicola, Wilten and Tripp, Bryan and Scott, Matthew, Obtaining Arbitrary Prescribed Mean Field Dynamics for Recurrently Coupled Networks of Type-I Spiking Neurons with Analytically Determined Weights, Frontiers in computational neuroscience, 10, 2016
  • Tripp, Bryan and Eliasmith, Chris, Function approximation in inhibitory networks, Neural Networks, 77, 2016, 95 - 106
  • Tripp, Bryan P, Surrogate population models for large-scale neural simulations, Neural computation, 2015
  • Tripp, Bryan, Similarities and differences between stimulus tuning in the inferotemporal visual cortex and convolutional networks, IJCNN, 2017 (Accepted in 2017)
  • Dutta, Shamak and Tripp, Bryan and Taylor, Graham. Convolutional neural networks regularized by correlated noise, in press, Computer & Robot Vision, 2018 (Accepted in 2018)
  • Blouw, Peter and Eliasmith, Chris and Tripp, Bryan, A scaleable spiking neural model of action planning, CogSci, 2016 (Accepted in 2016)