Skip to main content

A Top-Down Approach for a Synthetic Autobiographical Memory System

  • Conference paper
  • First Online:
Biomimetic and Biohybrid Systems (Living Machines 2015)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9222))

Included in the following conference series:

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.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  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. 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. Pouget, A., Beck, J.M., Ma, W.J., Latham, P.E.: Probabilistic brains: knowns and unknowns. Nature Neuroscience 16(9), 1170–1178 (2013)

    Article  Google Scholar 

  4. Rojas, R.: Neural networks: a systematic introduction. Springer Science & Business Media (1996)

    Google Scholar 

  5. Rasmussen, C.E., Williams, C.K.I.: Gaussian processes for machine learning. MIT Press, Cambridge (2006)

    MATH  Google Scholar 

  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)

    MathSciNet  MATH  Google Scholar 

  7. Bengio, Y., LeCun, Y.: Tutorial on Learning Deep Architectures. Videlectures.net, June 2009. http://videolectures.net/icml09_bengio_lecun_tldar/

  8. Nielsen, M. A.: Neural Networks and Deep Learning. Determination Press (2015)

    Google Scholar 

  9. Bishop, C. M.: Pattern Recognition and Machine Learning. Springer-Verlag (2006). ISBN 0387310738

    Google Scholar 

  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. 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)

    Chapter  Google Scholar 

  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)

    Chapter  Google Scholar 

  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)

    Article  Google Scholar 

  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. 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. Turk, M., Pentland, A.: Eigenfaces for recognition. Journal of Cognitive Neuroscience 3(1), 71–86 (1991)

    Article  Google Scholar 

  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. 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)

    Article  MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Andreas Damianou .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer International Publishing Switzerland

About this paper

Cite this paper

Damianou, A., Ek, C.H., Boorman, L., Lawrence, N.D., Prescott, T.J. (2015). A Top-Down Approach for a Synthetic Autobiographical Memory System. In: Wilson, S., Verschure, P., Mura, A., Prescott, T. (eds) Biomimetic and Biohybrid Systems. Living Machines 2015. Lecture Notes in Computer Science(), vol 9222. Springer, Cham. https://doi.org/10.1007/978-3-319-22979-9_28

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-22979-9_28

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-22978-2

  • Online ISBN: 978-3-319-22979-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics