Bryan Tripp

Professor Tripp uses computational models to study how the brain processes information. He integrates neurobiological models and deep learning to study visuomotor processes. He is also interested in applying these models in challenging robotics tasks, to better understand how the brain deals with the complex physical world. Recent progress in his lab includes: The first deep-network architecture that is based quantitatively on a large cortical network (Tripp, 2019); the most comprehensive model of a higher cortical representation (Rezai et al., 2018); the largest dataset of human-demonstrated robotic grasps (Iyegar et al., 2018); the only robotic head that has movement capabilities on par with humans (including saccade velocity, stereo baseline, and range of motion) (Huber et al., 2018); and the first spiking neural network model of the planning of complex actions (Blouw et al., 2016).

• Computational neuroscience
• Deep learning
• Robotics

• SYDE 750 - Topics in Systems Modelling
  • Taught in 2014, 2015, 2016
• SYDE 552 - Computational Neuroscience
  • Taught in 2014, 2015, 2016, 2017, 2018
• BME 261 - Prototyping, Simulation and Design
  • Taught in 2015, 2016, 2017, 2018
• SYDE 202 - Seminar
  • Taught in 2015
• SYDE 544 - Biomedical Measurement and Signal Processing
  • Taught in 2014, 2015, 2016
• BME 201 - Seminar
  • Taught in 2016
• SYDE 770 - Selected Topics in Communication and Information Systems
  • Taught in 2017, 2018
• BME 301 - Seminar
  • Taught in 2018
• BME 355 - Anatomical Systems Modelling
  • Taught in 2017, 2018

• Tripp, BP, Approximating the Architecture of Visual Cortex in a Convolutional Network, Neural Computation, 31(8), 2019, 1551 - 1591
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• Omid Rezai, Pinar Boyraz Jentsch, Bryan Tripp, A video-driven model of response statistics in the primate middle temporal area, Neural Networks, 108, 2018, 424 - 444
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• Huber, Scott and Selby, Ben and Tripp, Bryan, OREO: An open-hardware robotic head that supports practical saccades and accommodation,, IEEE Robotics Automation Letters, 3(3), 2018, 2640 - 2645
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• 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)
• Tripp, Bryan and Eliasmith, Chris, Function approximation in inhibitory networks, Neural Networks, 77, 2016, 95 - 106
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• 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 P, Surrogate population models for large-scale neural simulations, Neural computation, 2015
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• Tripp, Bryan, Similarities and differences between stimulus tuning in the inferotemporal visual cortex and convolutional networks, IJCNN, 2017 (Accepted in 2017)
• S Dutta, B Tripp, GW Taylor, Convolutional Neural Networks Regularized by Correlated Noise, 2018, 15th Conference on Computer and Robot Vision (CRV), 375-382 (Accepted in 2018)
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• Peter Blouw Chris Eliasmith Bryan Tripp, Proceedings of the 38th Annual Conference of the Cognitive Science Society, 1588 manuscript pages (Accepted in 2016)
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• Rajan Iyengar, Victor Reyes Osorio, Presish Bhattachan, Adrian Ragobar, Bryan Tripp, A dataset of 40K naturalistic 6-degree-of-freedom robotic grasp demonstrations, 7 manuscript pages
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