Matthieu Lagarde

Centre National de la Recherche Scientifique, CY Cergy Paris Université

Papers

Total Citations

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About

Matthieu Lagarde’s research lies at the intersection of neural networks, real-time distributed systems, and autonomous robotics. His work focuses on enabling complex, adaptive behaviors in robots through biologically inspired computational models. In his most-cited paper, “Distributed real time neural networks in interactive complex systems” (2008, 14 citations), Lagarde introduced two graphical software tools that facilitate the modeling and simulation of real-time, distributed neural networks. These tools allow for real-time control and online learning, directly applied to developing control architectures for autonomous robots—a significant contribution to bridging neural computation with practical robotic systems. His earlier work, “The Role of Internal Oscillators for the One-Shot Learning of Complex Temporal Sequences” (2007, 6 citations), explores how internal oscillatory mechanisms enable rapid learning of temporal patterns, offering insights into efficient, one-shot learning in neural systems. Though his citation counts are modest, Lagarde’s contributions are notable for their practical impact on real-time robotic control and their integration of neural dynamics with distributed computing. His research continues to inspire students and researchers interested in the intersection of neural computation, robotics, and adaptive systems.