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Self-organization of object features representing motion using Multiple Timescales Recurrent Neural Network.

, , , and . IJCNN, page 1-8. IEEE, (2012)

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Learning Semantic Combinatoriality from the Interaction between Linguistic and Behavioral Processes., and . Adaptive Behaviour, 13 (1): 33-52 (2005)Predictive Coding for Dynamic Visual Processing: Development of Functional Hierarchy in a Multiple Spatio-Temporal Scales RNN Model., and . CoRR, (2017)Recognition of Visually Perceived Compositional Human Actions by Multiple Spatio-Temporal Scales Recurrent Neural Networks., , and . IEEE Trans. Cognitive and Developmental Systems, 10 (4): 1058-1069 (2018)Evolved Motor Primitives and Sequences in a Hierarchical Recurrent Neural Network., and . GECCO (1), volume 3102 of Lecture Notes in Computer Science, page 603-614. Springer, (2004)On the Dynamics of Robot Exploration Learning., and . ECAL, volume 1674 of Lecture Notes in Computer Science, page 279-288. Springer, (1999)Acquiring a Functionally Compositional System of Goal-Directed Actions of a Simulated Agent., and . SAB, volume 5040 of Lecture Notes in Computer Science, page 331-341. Springer, (2008)Cognitive and motor compliance in intentional human-robot interaction., and . ICRA, page 11291-11297. IEEE, (2020)Dynamical Systems Account for Meta-level Cognition., and . SAB, volume 5040 of Lecture Notes in Computer Science, page 311-320. Springer, (2008)Variational Recurrent Models for Solving Partially Observable Control Tasks., , and . ICLR, OpenReview.net, (2020)Self-Organization of Symbolic Processes through Interaction with the Physical World.. IJCAI, page 112-118. Morgan Kaufmann, (1995)