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About

Garrick Orchard is a leading figure in neuromorphic engineering, whose work bridges the gap between biology-inspired sensing and efficient, event-driven computation. His primary research areas include event-based vision, spiking neural networks, and neuromorphic robotics. Orchard’s most impactful contribution is his co-authorship of the seminal survey "Event-Based Vision: A Survey" (2020), which has garnered over 630 citations and serves as a definitive reference for researchers entering the field. This work systematically explains how event cameras—bio-inspired sensors that asynchronously report pixel-level brightness changes—revolutionize visual processing by offering high temporal resolution and low latency. Beyond surveys, Orchard has made foundational contributions to configuring spiking neural networks for real-world tasks, including bipedal walking using central pattern generators and a neuromorphic self-driving robot that integrates retinomorphic vision with IBM’s TrueNorth processor for closed-loop control. His earlier work on Hebbian learning for visually directed reaching demonstrates his long-standing commitment to autonomous, brain-inspired systems. With a career spanning from genetic algorithm optimization of silicon neural networks to pioneering event-based sensing, Orchard’s research continues to shape how machines perceive and interact with dynamic environments.