Scene Memory on Competitively Growing Neural Network Using Temporal Coding: Self-organized Learning and Glance Recognizability

  • Masayasu Atsumi
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3316)


We have been building the competitively growing neural network using temporal coding for quick one-shot object learning and glance object recognition, which is the core of our saliency-based scene memory model. This neural network represents objects using latency-based temporal coding and grows size and recognizability through learning and self-organization. This paper shows that self-organized learning is quickly performed and glance recognition is successfully performed by our model through simulation experiments of a robot equipped with a camera.


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  1. 1.
    Atsumi, M.: Scene Learning and Glance Recognizability based on Competitively Growing Spiking Neural Network. In: Proc. 2004 IEEE IJCNN, pp. 2859–2864 (2004)Google Scholar
  2. 2.
    Atsumi, M.: Saliency-based Scene Recognition based on Growing Competitive Neural Network. In: SMC 2003 Conf. Proc., pp. 2863–2870 (2003)Google Scholar
  3. 3.
    Breazeal, C., Scassellati, B.: A Context-dependent Attention System for a Social Robot. In: Proc. of 16th Int. Joint Conf. on Artificial Intelligence, pp. 1146–1151 (1999)Google Scholar
  4. 4.
    Itti, L., Koch, C., Niebur, E.: A Model of Saliency-based Visual Attention for Rapid Scene Analysis. IEEE Trans. on Pattern Analysis and Machine Intelligence 20(11), 1254–1259 (1998)CrossRefGoogle Scholar
  5. 5.
    Kohonen, T.: Self-Organizing Maps. Springer, Heidelberg (1995)Google Scholar
  6. 6.
    Thorpe, S., Delorme, A., Rullen, R.V.: Spike-based Strategies for Rapid Processing. Neural Networks 14, 715–725 (2001)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Masayasu Atsumi
    • 1
  1. 1.Dept. of Information Systems Sci, Faculty of Eng.Soka UniversityTokyoJapan

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