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F. Lin?ker and L. Niklasson, “Time Series Segmentation Using an Adaptive Resource Allocating Vector Quantization Network Based on Change Detection,” Proceedings of the International Joint Conference on Neural Networks, IEEE Computer Society, Vol. 6, 24-27 July 2000, pp. 323328.

has been cited by the following article:

• TITLE: A Tree-Type Memory Formation by Sensorimotor Feedback: A Possible Approach to the Development of Robotic Cognition

  AUTHORS: Fady Alnajjar, Indra M. Zin, Abdl R. Hafiz, Kazuyuki Murase

  KEYWORDS: Visiomotor Abstraction; Memory Based Learning; Artificial Cognition; Internal Representation

  JOURNAL NAME: Intelligent Control and Automation, Vol.4 No.2, May 24, 2013

  ABSTRACT: Based on indications from neuroscience and psychology, both perception and action can be internally simulated in or- ganisms by activating sensory and/or motor areas in the brain without actual external sensory input and/or without any resulting behavior (a phenomenon called Thinking). This phenomenon is usually used by the organisms to cope with missing external inputs. Applying such phenomenon in a real robot recently has taken the attention of many researchers. Although some work has been reported on this issue, none of this work has so far considered the potential of the robot’s vision at the sensorimotor abstraction level, where extracting data from the environment takes place. In this study, a novel visiomotor abstraction is presented into a physical robot through a memory-based learning algorithm. Experi- mental results indicate that our robot with its vision could develop a kind of simple anticipation mechanism into its tree-type memory structure through interacting with the environment which would guide its behavior in the absence of external inputs.