Schuyler Eldridge
Papers
1
Total Citations
2
H-Index
1
About
Schuyler Eldridge is a researcher whose work bridges computational neuroscience and artificial intelligence, with a particular focus on how biological vision systems can inspire machine navigation. His most-cited paper, "Learning to navigate in a virtual world using optic flow and stereo disparity signals" (2014), explores the integration of optic flow and stereo disparity—two key visual cues used by animals—to enable autonomous agents to learn navigation in virtual environments. This contribution stands out for its biologically grounded approach, demonstrating how principles of neural computation can be applied to create more efficient and robust AI systems. While his citation count is modest, with this work accumulating 2 citations, its interdisciplinary nature highlights Eldridge's commitment to understanding the neural mechanisms underlying spatial perception and decision-making. His research sits at the intersection of robotics, computer vision, and cognitive science, offering insights that could inform the development of more adaptive and perceptive autonomous systems. Eldridge's work is particularly relevant for students and researchers interested in neuromorphic computing, embodied cognition, and the translation of biological algorithms into artificial agents.
Research Focus
Key Achievements
Top Papers
- 1