Terrence C. Stewart
Papers
13
Total Citations
226
H-Index
7
About
Terrence C. Stewart is a leading researcher in neuromorphic computing and neurorobotics, specializing in developing spiking neural networks (SNNs) for real-time, low-power sensorimotor control and adaptive robotic systems. His major contributions include pioneering hybrid learning paradigms that combine hard-coded reflexes with trainable neural mappings, enabling robots to autonomously adapt to dynamic environments—as demonstrated in his work on mobile platforms and insect-scale flapping robots. Stewart has also advanced neuromorphic hardware benchmarking, notably comparing the Intel Loihi and SpiNNaker 2 prototypes for low-latency keyword spotting and adaptive control, with his 2021 study garnering 50 citations. His research extends to human-robot interaction, where he developed SNNs that classify sEMG signals to trigger finger reflexes on robotic hands, achieving 20 citations. With over 200 total citations across his top papers, Stewart’s work bridges theoretical neuroscience and practical robotics, exemplified by his award-winning demonstration of serendipitous offline learning in a neuromorphic robot. His contributions are instrumental in creating energy-efficient, biologically-inspired systems for autonomous navigation, prosthetic control, and social robotics.
Research Focus
Key Achievements
Top Papers
- 1
- 2Event-based neural computing on an autonomous mobile platform44 citations · 2014
- 3Serendipitous Offline Learning in a Neuromorphic Robot36 citations · 2016
- 4Trainable sensorimotor mapping in a neuromorphic robot25 citations · 2014
- 5
- 6Triggering Robot Hand Reflexes with Human EMG Data Using Spiking Neurons18 citations · 2018
- 7
- 8An adaptive spiking neural controller for flapping insect-scale robots7 citations · 2017
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- 10