Papers
2
Total Citations
19
H-Index
2
About
Trevor Bihl is a researcher at the intersection of neuromorphic computing, autonomous systems, and artificial intelligence. His work focuses on implementing cognitive algorithms in hardware, particularly through spiking neural networks on neuromorphic platforms—a key step toward realizing truly autonomous artificial agents. His most-cited paper, "Associative Memory in Spiking Neural Network Form Implemented on Neuromorphic Hardware" (2020), has garnered 15 citations and explores how brain-inspired architectures can enable robots to process information more efficiently. Bihl also contributes to the broader research ecosystem through his 2019 paper "From Lab to Internship and Back Again," which advocates for interdisciplinary R&D approaches that bridge artificial intelligence, biology, psychology, and modeling and simulation. This work reflects his commitment to training the next generation of autonomous systems researchers by creating hands-on learning environments. Bihl's research is notable for its practical orientation—moving theoretical cognitive algorithms from the lab bench toward real-world robotic applications—and for its emphasis on the collaborative, cross-domain thinking essential for advancing intelligent systems.
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