This talk explores the potential of Hyperdimensional Computing (HDC) as a framework for efficient neuromorphic processing. We introduce Hierarchical Resonator Networks (HRNs), a novel architecture for scene understanding. HRNs leverage HDC to identify objects and their generative factors directly from visual input. The network computes with complex-valued vectors implemented as spike-timing phasor neurons. This enables implementation on low-power neuromorphic hardware like Intel's Loihi.

Additionally, we demonstrate the HRN’s capability in visual odometry, accurately estimating robot self-motion from event-based data. This approach is a step towards robust, low-power computer vision and robotics.