A Top-Down Approach for a Synthetic Autobiographical Memory System

  • Andreas Damianou
  • Carl Henrik Ek
  • Luke Boorman
  • Neil D. Lawrence
  • Tony J. Prescott
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9222)

Abstract

Autobiographical memory (AM) refers to the organisation of one’s experience into a coherent narrative. The exact neural mechanisms responsible for the manifestation of AM in humans are unknown. On the other hand, the field of psychology has provided us with useful understanding about the functionality of a bio-inspired synthetic AM (SAM) system, in a higher level of description. This paper is concerned with a top-down approach to SAM, where known components and organisation guide the architecture but the unknown details of each module are abstracted. By using Bayesian latent variable models we obtain a transparent SAM system with which we can interact in a structured way. This allows us to reveal the properties of specific sub-modules and map them to functionality observed in biological systems. The top-down approach can cope well with the high performance requirements of a bio-inspired cognitive system. This is demonstrated in experiments using faces data.

Keywords

Synthetic autobiographical memory Hippocampus Robotics Deep Gaussian process MRD 

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References

  1. 1.
    Damianou, A., Lawrence, N.: Deep Gaussian processes. Proceedings of the 16th International Workshop on A.I. and Statistics (AISTATS), pp. 207–215 (2013)Google Scholar
  2. 2.
    Evans, M.H., Fox, C.W., Prescott, T.J.: Machines learning - towards a new synthetic autobiographical memory. In: Duff, A., Lepora, N.F., Mura, A., Prescott, T.J., Verschure, P.F.M.J. (eds.) Living Machines 2014. LNCS, vol. 8608, pp. 84–96. Springer, Heidelberg (2014) Google Scholar
  3. 3.
    Pouget, A., Beck, J.M., Ma, W.J., Latham, P.E.: Probabilistic brains: knowns and unknowns. Nature Neuroscience 16(9), 1170–1178 (2013)CrossRefGoogle Scholar
  4. 4.
    Rojas, R.: Neural networks: a systematic introduction. Springer Science & Business Media (1996)Google Scholar
  5. 5.
    Rasmussen, C.E., Williams, C.K.I.: Gaussian processes for machine learning. MIT Press, Cambridge (2006) MATHGoogle Scholar
  6. 6.
    Lawrence, N.D.: Probabilistic non-linear principal component analysis with Gaussian process latent variable models. Journal of Machine Learning Research 6, 1783–1816 (2005)MathSciNetMATHGoogle Scholar
  7. 7.
    Bengio, Y., LeCun, Y.: Tutorial on Learning Deep Architectures. Videlectures.net, June 2009. http://videolectures.net/icml09_bengio_lecun_tldar/
  8. 8.
    Nielsen, M. A.: Neural Networks and Deep Learning. Determination Press (2015)Google Scholar
  9. 9.
    Bishop, C. M.: Pattern Recognition and Machine Learning. Springer-Verlag (2006). ISBN 0387310738Google Scholar
  10. 10.
    Damianou, A., Ek, C.H., Titsias, M., Lawrence, N.: Manifold relevance determination. Proceedings of the 29th International Conference on Machine Learning (ICML), pp. 145–152. omnipress, New York (2012)Google Scholar
  11. 11.
    Bay, H., Tuytelaars, T., Van Gool, L.: SURF: speeded up robust features. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006, Part I. LNCS, vol. 3951, pp. 404–417. Springer, Heidelberg (2006) CrossRefGoogle Scholar
  12. 12.
    Pointeau, G., Petit, M., Dominey, P.F.: Embodied simulation based on autobiographical memory. In: Lepora, N.F., Mura, A., Krapp, H.G., Verschure, P.F.M.J., Prescott, T.J. (eds.) Living Machines 2013. LNCS, vol. 8064, pp. 240–250. Springer, Heidelberg (2013) CrossRefGoogle Scholar
  13. 13.
    Pointeau, G., Petit, M., Dominey, P.F.: Successive Developmental Levels of Autobiographical Memory for Learning Through Social Interaction. IEEE Transactions on Autonomous Mental Development 6, 200–212 (2014)CrossRefGoogle Scholar
  14. 14.
    Damianou, A., Titsias, M., Lawrence, N.: Variational inference for uncertainty on the inputs of Gaussian process models. arXiv preprint, arXiv:1409.2287 (2014)
  15. 15.
    Georghiades, A.S., Belhumeur, P.N., Kriegman, D.J.: From few to many: Illumination cone models for face recognition under variable lighting and pose. IEEE Trans. Pattern Anal. Mach. Intelligence 23(6) (2001)Google Scholar
  16. 16.
    Turk, M., Pentland, A.: Eigenfaces for recognition. Journal of Cognitive Neuroscience 3(1), 71–86 (1991)CrossRefGoogle Scholar
  17. 17.
    Luvizotto, A., Renn-Costa, C., Verschure, P.: A Framework for Mobile Robot Navigation Using a Temporal Population Code. Biomimetic & Biohybrid Systems (2012)Google Scholar
  18. 18.
    Fukushima, K.: Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position. Biological Cybernetics 36(4), 193–202 (1980)CrossRefMATHGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Andreas Damianou
    • 1
    • 2
  • Carl Henrik Ek
    • 3
  • Luke Boorman
    • 1
  • Neil D. Lawrence
    • 2
  • Tony J. Prescott
    • 1
  1. 1.Sheffield Centre for Robotics (SCentRo)University of SheffieldSheffieldUK
  2. 2.Department of Computer ScienceUniversity of SheffieldSheffieldUK
  3. 3.CVAP LabKTHStockholmSweden

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