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Mathias Quoy: [Publications] [Author Rank by year] [Co-authors] [Prefers] [Cites] [Cited by]

Publications of Author

  1. Philippe Gaussier, Pierre Andry, Jean-Paul Banquet, Mathias Quoy, Jacqueline Nadel, Arnaud Revel
    Robots as Models of the Brain: What Can We Learn from Modelling Rat Navigation and Infant Imitation Games? [Citation Graph (0, 0)][DBLP]
    AIME, 2003, pp:377-386 [Conf]
  2. Mathias Quoy, Philippe Gaussier, Sacha Leprêtre, Arnaud Revel
    A Neural Model for the Visual Navigation and Planning of a Mobile Robot. [Citation Graph (0, 0)][DBLP]
    ECAL, 1999, pp:319-323 [Conf]
  3. Mathias Quoy, Philippe Gaussier, Sacha Leprêtre, Arnaud Revel, Jean-Paul Banquet
    A Planning Map for Mobile Robots: Speed Control and Paths Finding in a Changing Environment. [Citation Graph (0, 0)][DBLP]
    EWLR, 1999, pp:103-119 [Conf]
  4. Sorin Moga, Philippe Gaussier, Mathias Quoy
    Investigating Active Pattern Recognition in an Imitative Game. [Citation Graph (0, 0)][DBLP]
    IWANN (2), 2001, pp:516-523 [Conf]
  5. Nicolas Cuperlier, Mathias Quoy, Philippe Laroque, Philippe Gaussier
    Transition Cells and Neural Fields for Navigation and Planning. [Citation Graph (0, 0)][DBLP]
    IWINAC (1), 2005, pp:346-355 [Conf]
  6. Bernard Doyon, Bruno Cessac, Mathias Quoy, Manuel Samuelides
    Destabilization and Route to Chaos in Neural Networks with Random Connectivity. [Citation Graph (0, 0)][DBLP]
    NIPS, 1992, pp:549-555 [Conf]
  7. Mathias Quoy, Sorin Moga, Philippe Gaussier, Arnaud Revel
    Parallelization of Neural Networks Using PVM. [Citation Graph (0, 0)][DBLP]
    PVM/MPI, 2000, pp:289-296 [Conf]
  8. Nicolas Cuperlier, Mathias Quoy, C. Giovannangeli, Philippe Gaussier, Philippe Laroque
    Transition Cells for Navigation and Planning in an Unknown Environment. [Citation Graph (0, 0)][DBLP]
    SAB, 2006, pp:286-297 [Conf]
  9. Philippe Gaussier, Sorin Moga, Mathias Quoy, Jean-Paul Banquet
    From Perception-Action Loops to Imitation Processes: A Bottom-Up Approach of Learning by Imitation. [Citation Graph (0, 0)][DBLP]
    Applied Artificial Intelligence, 1998, v:12, n:7-8, pp:701-727 [Journal]
  10. Emmanuel Daucé, Mathias Quoy, Bernard Doyon
    Resonant spatiotemporal learning in large random recurrent networks. [Citation Graph (0, 0)][DBLP]
    Biological Cybernetics, 2002, v:87, n:3, pp:185-198 [Journal]
  11. Emmanuel Daucé, Mathias Quoy, Bernard Doyon
    Resonant spatiotemporal learning in large random recurrent networks. [Citation Graph (0, 0)][DBLP]
    Biological Cybernetics, 2002, v:87, n:4, pp:315- [Journal]
  12. Jean-Paul Banquet, Philippe Gaussier, Mathias Quoy, Arnaud Revel, Yves Burnod
    A Hierarchy of Associations in Hippocampo-Cortical Systems: Cognitive Maps and Navigation Strategies. [Citation Graph (0, 0)][DBLP]
    Neural Computation, 2005, v:17, n:6, pp:1339-1384 [Journal]
  13. Emmanuel Daucé, Mathias Quoy, Bruno Cessac, Bernard Doyon, Manuel Samuelides
    Self-organization and dynamics reduction in recurrent networks: stimulus presentation and learning. [Citation Graph (0, 0)][DBLP]
    Neural Networks, 1998, v:11, n:3, pp:521-533 [Journal]
  14. Mathias Quoy, Philippe Laroque, Philippe Gaussier
    Learning and motivational couplings promote smarter behaviors of an animat in an unknown world. [Citation Graph (0, 0)][DBLP]
    Robotics and Autonomous Systems, 2002, v:38, n:3-4, pp:149-156 [Journal]
  15. Jianwei Zhang, Mathias Quoy
    Advances in robot skill learning. [Citation Graph (0, 0)][DBLP]
    Robotics and Autonomous Systems, 2002, v:38, n:3-4, pp:135-136 [Journal]
  16. Mathias Quoy, Sorin Moga, Philippe Gaussier
    Dynamical neural networks for planning and low-level robot control. [Citation Graph (0, 0)][DBLP]
    IEEE Transactions on Systems, Man, and Cybernetics, Part A, 2003, v:33, n:4, pp:523-532 [Journal]

  17. Model of the Hippocampal Learning of Spatio-temporal Sequences. [Citation Graph (, )][DBLP]


  18. Interest of Spatial Context for a Place Cell Based Navigation Model. [Citation Graph (, )][DBLP]


  19. Why and How Hippocampal Transition Cells Can Be Used in Reinforcement Learning. [Citation Graph (, )][DBLP]


  20. Navigation and Planning in an Unknown Environment Using Vision and a Cognitive Map. [Citation Graph (, )][DBLP]


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