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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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Self-Organization of Modules and Their Hierarchy in Robot Learning Problems: A Dynamical Systems Approach, and . SCSL-TR-97-008. Sony Computer Science Laboratory, Inc., (1997)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)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)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)Self-Organization of Symbolic Processes through Interaction with the Physical World.. IJCAI, page 112-118. Morgan Kaufmann, (1995)Static and dynamic memory to simulate higher-order cognitive tasks., , and . IJCNN, page 1-6. IEEE, (2012)Integrative Learning between Language and Action: A Neuro-Robotics Experiment., , , , , and . ICANN (2), volume 6353 of Lecture Notes in Computer Science, page 256-265. Springer, (2010)Self-Organization and Compositionality in Cognitive Brains Further Thoughts., , and . Proc. IEEE, 102 (3): 606-607 (2014)Initialization of latent space coordinates via random linear projections for learning robotic sensory-motor sequences., and . Frontiers Neurorobotics, (September 2022)