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INRC Forum: Jeff Orchard
2 May @ 08:00 - 09:00 PDT
Hyperdimensional Algorithms using Spiking Phasors
Abstract: Hyperdimensional (HD) computing offers a powerful framework for representing compositional reasoning. Such algorithms lend themselves to neural-network implementations, allowing us to create neural networks that can perform cognitive functions, like spatial reasoning, arithmetic, and symbolic logic. But the vectors involved can be quite large. Advances in neuromorphic hardware hold the promise of reducing the running time and energy footprint of neural networks by orders of magnitude. In this talk, I will extend some pioneering work to run HD algorithms on a substrate of spiking neurons, implementing examples in spatial memory, function representation, and temporal memory.
Bio: Jeff Orchard received degrees in applied mathematics from the University of Waterloo (BMath) and the University of British Columbia (MSc), and received his PhD in Computing Science from Simon Fraser University in 2003. Since then, he has been a faculty member at the Cheriton School of Computer Science at the University of Waterloo in Canada. Prof. Orchard’s research focuses on computational neuroscience, using mathematical models and computer simulations of neural networks in an effort to understand how the brain works. Guided by both theory and anatomy, he is building neural networks based on computational theories of the brain — such as predictive coding — to uncover the way we perceive the world. His research also includes Vector Symbolic Architectures and Algebras, spatial navigation, and population coding. He is a core member of the Centre for Theoretical Neuroscience.
For the meeting link, see the full INRC Forum Spring 2023 Schedule (accessible only to INRC Affiliates and Fully Engaged Members).