Papers
7
Total Citations
29
H-Index
3
About
Peter Stratton is a leading researcher in neuromorphic computing and spiking neural networks (SNNs), with a focus on their application to robotics and real-time sensory processing. His work bridges the gap between biological neural computation and practical robotic systems, particularly in visual place recognition, anomaly detection, and spatial orientation. Stratton’s most cited paper, “VPRTempo” (2024, 8 citations), introduces a fast, temporally encoded SNN for visual place recognition, demonstrating the potential of SNNs for energy-efficient, low-latency robotic navigation. He has also developed a spiking neural network-based auto-encoder for anomaly detection in streaming data (2020, 5 citations), addressing critical needs in cybersecurity and health analytics. His earlier contributions include calibrating spiking head-direction networks for robot orientation (2009, 3 citations) and exploring how robotic systems can unlock neural complexity (2016, 3 citations). With a career spanning foundational work in spike-time robotics (2010, 6 citations) and strategic movement calibration (2011, 2 citations), Stratton’s research consistently pushes the boundaries of neuromorphic engineering, offering innovative solutions for autonomous systems that learn and adapt in real-time.
Research Focus
Key Achievements
Top Papers
- 1
- 2Spike-time robotics: A rapid response circuit for a robot that seeks temporally varying stimuli6 citations · 2010
- 3
- 4Unlocking neural complexity with a robotic key3 citations · 2016
- 5
- 6
- 7