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  1. 力学的情報処理を用いた自己組織的記号獲得と運動生成.中村 大介 岡田 昌史 - 2005 - Transactions of the Japanese Society for Artificial Intelligence 20:177-187.
    The symbol acquisition and manipulation abilities are one of the inherent characteristics of human beings comparing with other creatures. In this paper, based on recurrent self-organizing map and dynamics-based information processing system, we propose a dynamics based self-organizing map. This method enables designing a topological map using time sequence data, which causes recognition and generation of the robot motion. Using this method, we design the self-organizing symbol acquisition system and robot motion generation system for a humanoid robot. By implementing DBSOM (...)
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  • Reply to Touretzky and Pomerleau: Reconstructing Physical Symbol Systems.Alonso H. Vera & Herbert A. Simon - 1994 - Cognitive Science 18 (2):355-360.
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  • On the subsymbolic nature of a PDP architecture that uses a nonmonotonic activation function.Michael R. W. Dawson & C. Darren Piercey - 2001 - Minds and Machines 11 (2):197-218.
    PDP networks that use nonmonotonic activation functions often produce hidden unit regularities that permit the internal structure of these networks to be interpreted (Berkeley et al., 1995; McCaughan, 1997; Dawson, 1998). In particular, when the responses of hidden units to a set of patterns are graphed using jittered density plots, these plots organize themselves into a set of discrete stripes or bands. In some cases, each band is associated with a local interpretation. On the basis of these observations, Berkeley (2000) (...)
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  • Episodic Indexing: A Model of Memory for Attention Events.Erik M. Altmann & Bonnie E. John - 1999 - Cognitive Science 23 (2):117-156.
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