Engineering 5, 6th Floor
519-888-4567, Extension 32600
- BSc - University of Waterloo
- MSc - University of Toronto
- PhD - University of Waterloo
My research develops simulations of the parts of the brain that are responsible for seeing, and for controlling movement. About half the human brain is dedicated to seeing and moving, and this half has very much in common with the other half. So understanding vision and movement will bring us much closer to understanding intelligence more
generally. My group also integrates neural simulations with robots, because this forces our models to confront the same kinds of physical complications as real brains (e.g. low-contrast regions in depth perception; uncertain friction in grasping). We are currently using
deep convolutional networks as a starting point, and working to make them more like real brains in various ways. The goal is to develop neural simulations that have similar internal activity to real brains, while controlling robots effectively in a variety of complex tasks.
Tripp BP (submitted) Similarities and differences between stimulus tuning in the inferotemporal visual cortex and convolutional networks. https://arxiv.org/abs/1612.06975
Tripp BP, Singh S & Selby B (2016) Optimization and Rapid Prototyping of Catadioptric Omnidirectional Stereo Sensors. J Intelligent and Robotic Systems http://link.springer.com/article/10.1007/s10846-016-0462-9?wt_mc=Internal.Event.1.SEM.ArticleAuthorOnlineFirst
Rezai O, Boyraz-Jentsch P, Tripp B (2016) A Rich Source of Labels for Deep Network Models of the Primate Dorsal Visual Stream NIPS Brains & Bits Workshop https://arxiv.org/abs/1610.04962
Hunsberger E, Reyes V, Orchard J & Tripp BP (accepted) Feature-based resource allocation for real-time stereo disparity estimation, IEEE Access.
Blouw P, Eliasmith C, Tripp BP (2016) A scaleable spiking neural model of action planning. Cognitive Science Society (CogSci 2016, Philadelphia).
Tripp BP (2016) A convolutional model of the macaque middle temporal area. International Conference on Artificial Neural Networks (ICANN 2016, Barcelona).
Huber S, Selby B, Tripp BP (2016) Design of a saccading and accommodating robot vision system. Computer and Robot Vision (CRV 2016, Victoria).
Tripp BP & Eliasmith C (2016) Function approximation in inhibitory networks. Neural Networks 77: 95-106.
Tripp BP (2015) Surrogate population models for large-scale neural simulations, Neural Computation 27(6): 1186-1222.
Rezai O, Kleinhans A, Matallanas E, Selby B & Tripp BP (2014) Modelling the shape hierarchy for visually guided grasping. Frontiers in Computational Neuroscience 8: 132.
Tripp BP (2012) Decorrelation of spiking variability and improved information transfer through feedforward divisive normalization. Neural Computation 24: 867-894.
Tripp BP, Eliasmith C (2007) Neural populations can induce reliable post-synaptic currents without observable spike rate changes or precise spike timing. Cerebral Cortex 17(8): 1830-1840.