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Habituation Based on Spectrogram Analysis

  • Javier Lorenzo
  • Mario Hernández
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2527)

Abstract

In this paper we present a habituation mechanism which includes a modification of the Stanley’s habituation model with the addition of a stage based on spectrogram to detect temporal patterns in a signal and to obtain a measure of habituation to these patterns. This means that this measure shows a saturation process as the pattern is perceived by the system and when it disappears the measure drops. The use of the spectrogram simplifies the detection of the temporal patterns which can be detected with naive techniques. We have carried on some experiments both a synthetic signal and real signals like readings of a sonar in a mobile robot.

Keywords

Mobile Robot Temporal Pattern Novelty Detection Multimodal User Yellow Card 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Stiles, B., Ghosh, J.: A habituation based neural network for spatio-temporal classi.cation. In: Proceedings of the 1995 IEEE Workshop In Neural Networks for Signal Processing, Cambridge, MA (1995) 135–144Google Scholar
  2. 2.
    Marsland, S., Nehmzow, U., Shapiro, J.: A model of habituation applied to mobile robots. In: Proceedings of TIMR 1999— Towards Intelligent Mobile Robots, Bristol (1999)Google Scholar
  3. 3.
    Castellucci, V., Pinsker, H., Kupfermann, I., Kandel, E.R.: Neuronal mechanisms of habituation and dishabituation of the gill-withdrawal reflex in Aplysia. Science 167 (1970), 1745–1748CrossRefGoogle Scholar
  4. 4.
    Crook, P., Hayes, G.: A robot implementation of a biologically inspired method ofr novelty detection. In: Proceedings of TIMR 2001— Towards Intelligent Mobile Robots, Manchester (2001)Google Scholar
  5. 5.
    Stanley, J.: Computer simulation of a model of habituation. Nature 261 (1976) 146–148CrossRefGoogle Scholar
  6. 6.
    Wang, D.: Habituation. In Arbib, M.A., ed.: The Handbook of Brain Theory and Neural Networks. MIT Press (1995) 441–444Google Scholar
  7. 7.
    Marsland, S., Nehmzow, U., Shapiro, J.: Detecting novel features of an environment using habituation. In: From Animals to Animats, The Sixth International Conference on Simulation of Adaptive Behaviour, Paris (2000)Google Scholar
  8. 8.
    Marsland, S., Nehmzow, U., Shapiro, J.: Novelty detection in large enviroments. In: Proceedings of TIMR 2001—Towards Intelligent Mobile Robots, Manchester (2001)Google Scholar
  9. 9.
    Ypma, A., Duin, R.P.W.: Novelty detection using self-organizing maps. In Kasabov, N., Kozma, R., Ko, K., O’shea, R., Coghill, G., Gedeon, T., eds.: Progress in Connectionist-Based Information Systems. Volume 2. Springer, London (1997) 1322–1325Google Scholar
  10. 10.
    Dasgupta, D., Forrest, S.: Novelty detection in time series data using ideas from immunology. In: Proceedings of the 5th International Conference on Intelligent Systems, Reno (1996)Google Scholar
  11. 11.
    Chang, C.: Improving hallway navigation in mobile robots with sensory habituation. In: Proc. of the Int. Joint Conference on Neural Networks (IJCNN2000). Volume V., Como, Italy (2000) 143–147Google Scholar
  12. 12.
    Holland, S., Kosel, Tadej amd Waver, R., Sachse, W.: Determination of plate source, detector separation fron one signal. Ultrasonics 38 (2000) 620–623CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2002

Authors and Affiliations

  • Javier Lorenzo
    • 1
  • Mario Hernández
    • 1
  1. 1.IUSIANI Edificio del Parque TecnológicoUniv. of Las Palmas de G.C.Spain

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