1. Alex Pitti, Raphael Braud, Sylvain Mahé, Mathias Quoy & Philippe Gaussier (2013). Neural Model for Learning-to-Learn of Novel Task Sets in the Motor Domain. Frontiers in Psychology 4.
    During development, infants learn to differentiate their motor behaviors relative to various contexts by exploring and identifying the correct structures of causes and effects that they can perform; these structures of actions are called task sets or internal models. The ability to detect the structure of new actions, to learn them and to select on the fly the proper one given the current task set is one great leap in infants cognition. This behavior is an important component of the child's (...)
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  2. Jean-Paul Banquet, Philippe Gaussier, Mathias Quoy & Arnaud Revel (2001). From Reflex to Planning: Multimodal Versatile Complex Systems in Biorobotics. Behavioral and Brain Sciences 24 (6):1051-1053.
    As models of living beings acting in a real world biorobots undergo an accelerated “philogenic” complexification. The first efficient robots performed simple animal behaviours (e.g., those of ants, crickets) and later on isolated elementary behaviours of complex beings. The increasing complexity of the tasks robots are dedicated to is matched by an increasing complexity and versatility of the architectures now supporting conditioning or even elementary planning.
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  3. Mathias Quoy, Jean-Paul Banquet & Emmanuel Daucé (2001). Learning and Control with Chaos: From Biology to Robotics. Behavioral and Brain Sciences 24 (5):824-825.
    After critical appraisal of mathematical and biological characteristics of the model, we discuss how a classical hippocampal neural network expresses functions similar to those of the chaotic model, and then present an alternative stimulus-driven chaotic random recurrent neural network (RRNN) that learns patterns as well as sequences, and controls the navigation of a mobile robot.
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