Learning to Perceive the World as Articulated: An Approach for Hierarchical Learning in Sensory-Motor Systems

By Rolf Pfeifer, B. Blumberg, Jean-Arcady Meyer, Stewart W. Wilson. 1998

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Lifelong learning of human actions with deep neural network self-organization (2017, background): “Tani (2003) and Tani and Nolfi (1999) proposed that sensory-motor patterns for on-line planning in a robot learning scenario can be generated and recognized by using recurrent neural network models trained through prediction error minimization.”
The challenges of joint attention (2006, background): “Tani and Nolfi presented a system capable of combining local experts using gated modules (Tani and Nolfi, 1999).”
Combining Self-organizing Maps with Mixtures of Experts: Application to an Actor-Critic Model of Reinforcement Learning in the Basal Ganglia (2006, background): “In [21], the robot's behavior is a priori set and stabilized, and co stitutes one of the inputs to a mixture of experts having to categorize the sens ory-motor flow perceived by a robot.”