David Bowes
Papers
1
Total Citations
2
H-Index
1
About
David Bowes is a researcher whose work lies at the intersection of computational neuroscience and robotics, with a particular focus on how biological sensory processing principles can be translated into artificial neural controllers. His key research area centers on visual adaptation mechanisms, specifically exploring how lateral inhibition—a fundamental neural process in animal retinas that enables vision across varying light levels—can be implemented in simulated spiking neural networks for robotic systems. His most-cited paper, "The role of lateral inhibition in the sensory processing in a simulated spiking neural controller for a robot" (2009), demonstrates how these biologically inspired controllers can enable robots to perform phototaxis (turning toward or away from light) with greater robustness and efficiency. While his citation count remains modest, Bowes' contribution is notable for bridging theoretical neuroscience and practical robotics, offering a principled approach to designing adaptive sensory-motor systems. His work provides a valuable foundation for researchers interested in neuromorphic engineering and the application of neural computation principles to autonomous systems.
Research Focus
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Top Papers
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