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Dynamic Memory for Robot Control Using Delay-Based Coincidence Detection Neurones

  • Francis Jeanson
  • Tony White
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8131)

Abstract

This paper demonstrates the feasibility of dynamic memory in transmission delay coincidence detection networks. We present a low complexity, procedural algorithm for determining delay connectivity for the control of a simulated e-puck robot to solve the t-maze memory task. This work shows that dynamic memory modules need not undergo structural change during learning but that peripheral structures could be alternate candidates for this. Overall, this supports the view that delay coincidence detection networks can be effectively coupled to produce embodied adaptive behaviours.

Keywords

Dynamic Memory Transmission Delays Coincidence Detection Spiking Neural Networks Embodied Cognition 

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References

  1. 1.
    Abeles, M.: Corticonics: Neural circuits of the cerebral cortex. Cambridge University Press (1991)Google Scholar
  2. 2.
    Arik, S.: Stability analysis of delayed neural networks. IEEE Transactions on Circuits and Systems 47, 1089–1092 (2000)MathSciNetCrossRefMATHGoogle Scholar
  3. 3.
    Bernander, O., Douglas, R., Martin, K., Koch, C.: Synaptic background activity deter- mines spatio-temporal integration in single pyramidal cells. Proceedings of the National Acedemy of Sciences 88, 1569–1573 (1991)Google Scholar
  4. 4.
    Braitenberg, V.: Vehicles: Experiments in synthetic psychology. MIT Press, Cambridge (1984)Google Scholar
  5. 5.
    Carr, C., Konishi, M.: A circuit for detection of interaural time differences in the brain stem of the barn owl. Journal of Neuroscience 10, 3227–3246 (1990)Google Scholar
  6. 6.
    Cowan, J.: Stochastic models of neuroelectric activity. In: Ricce, S., Fread, K., Light, J. (eds.) Statistical Mechanics, pp. 181–182. University of Chicago (1972)Google Scholar
  7. 7.
    Fernando, C.: Symbol manipulation and rule learning in spiking neural networks. Journal of Theoretical Biology 275, 29–41 (2011)MathSciNetCrossRefGoogle Scholar
  8. 8.
    Izhikevich, E.M.: Polychronization: Computation with spikes. Neural Computation 18, 245–282 (2006)MathSciNetCrossRefMATHGoogle Scholar
  9. 9.
    Jeanson, F., White, A.: Evolving axonal delay neural networks for robot control. In: Soule, T. (ed.) Proceedings of the Fourteenth International Conference on Genetic and Evolutionary Computation Conference (GECCO 2012), pp. 121–128. ACM, New York (2012)CrossRefGoogle Scholar
  10. 10.
    Magnenat, S., Weibel, M., Bayeler, A.: Enki: The fast 2d simulator (2007), http://home.gna.org/enki/ (last accessed: January 28, 2013)
  11. 11.
    Sperling, G.: The information available in brief visual presentations. Psychological Monographs 74, 1–29 (1960)Google Scholar
  12. 12.
    Stevens, F., Wesseling, F.: Augmentation is a potentiation of the exocytotic process. Neuron 22, 139–146 (1999)CrossRefGoogle Scholar
  13. 13.
    Thorpe, S., Imbert, M.: Biological constraints on connectionist models. In: Connectionism in Perspective, pp. 63–92. Elsevier Science Publishers (1989)Google Scholar
  14. 14.
    Ziemke, T., Thieme, M.: Neuromodulation of reactive sensorimotor mappings as a short-term memory mechanism in delayed response tasks. Adaptive Behavior 10(3), 185–199 (2002)CrossRefGoogle Scholar
  15. 15.
    Zipser, D., Kehoe, B., Littlewort, G., Fuster, J.: A spiking network model of short-term active memory. The Journal of Neuroscience 12(8), 3406–3420 (1993)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Francis Jeanson
    • 1
  • Tony White
    • 1
  1. 1.Springer-Verlag, Computer Science EditorialHeidelbergGermany

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