Papers
23
Total Citations
395
H-Index
10
About
Mathias Quoy is a computational neuroscience and robotics researcher whose work bridges biological neural modeling and autonomous robotic systems. His research spans neural architectures for imitation learning, spatial navigation, hippocampal modeling, and dynamical neural networks — areas in which he has made lasting contributions to both cognitive robotics and neuroscience. Quoy's most influential work, cited over 133 times, demonstrated that imitation learning in robots need not be explicitly programmed but can emerge naturally from misinterpreted perceptual states — a groundbreaking bottom-up approach that influenced subsequent decades of work in developmental robotics. His hippocampo-cortical modeling research (57 citations) offered important insights into how the brain constructs cognitive maps and supports episodic memory through cascading associative processes. Further contributions include biologically inspired neural field models for robot navigation and path planning, and investigations into how the entorhinal cortex integrates multi-modal spatial information — work with direct implications for both neuroscience and autonomous systems design. Across his career, Quoy has consistently pursued a dual agenda: using robots as experimental platforms to test and refine neural theories, while drawing on biological principles to advance machine intelligence. His body of work represents a productive and ongoing dialogue between artificial and natural cognition.
Research Focus
Key Achievements
Top Papers
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- 3Dynamical neural networks for planning and low-level robot control40 citations · 2003
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- 5Resonant spatiotemporal learning in large random recurrent networks19 citations · 2002
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- 7Animal and robot learning: experiments and models about visual navigation14 citations · 1998
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- 10Neural model for learning-to-learn of novel task sets in the motor domain11 citations · 2013