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Ultra-Rapid Scene Categorization with a Wave of Spikes

  • Simon Thorpe
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2525)

Abstract

Recent experimental work has shown that the primate visual system can analyze complex natural scenes in only 100–150 ms. Such data, when combined with anatomical and physiological knowledge, seriously constrains current models of visual processing. In particular, it suggests that a lot of processing can be achieved using a single feed-forward pass through the visual system, and that each processing layer probably has no more than around 10 ms before the next stage has to respond. In this time, few neurons will have generated more than one spike, ruling out most conventional rate coding models. We have been exploring the possibility of using the fact that strongly activated neurons tend to fire early and that information can be encoded in the order in which a population of cells fire. These ideas have been tested using SpikeNet, a computer program that simulates the activity of very large networks of asynchronously firing neurons. The results have been extremely promising, and we have been able to develop artificial visual systems capable of processing complex natural scenes in real time using standard computer hardware (see http://www.spikenet-technology.com).

Keywords

Visual System Firing Rate Human Visual System Natural Image Natural Scene 
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.
    Potter, M.C., Meaning in visual search. Science, 187: (1975) 965–6.CrossRefGoogle Scholar
  2. 2.
    Potter, M.C., Short-term conceptual memory for pictures. J Exp Psychol (Hum Learn), 2: (1976) 509–22.CrossRefGoogle Scholar
  3. 3.
    Thorpe, S., Fize, D., Marlot, C., Speed of processing in the human visual system. Nature, 381: (1996) 520–2.CrossRefGoogle Scholar
  4. 4.
    VanRullen, R., Thorpe, S.J., Is it a bird? Is it a plane? Ultra-rapid visual categorisation of natural and artifactual objects. Perception, 30: (2001) 655–68.CrossRefGoogle Scholar
  5. 5.
    Delorme, A., Richard, G., Fabre-Thorpe, M., Ultra-rapid categorisation of natural scenes does not rely on colour cues: a study in monkeys and humans. Vision Res, 40: (2000) 2187–200.CrossRefGoogle Scholar
  6. 6.
    Fabre-Thorpe, M., Delorme, A., Marlot, C., Thorpe, S., A limit to the speed of processing in ultra-rapid visual categorization of novel natural scenes. J Cogn Neurosci, 13: (2001) 171–80.CrossRefGoogle Scholar
  7. 7.
    Thorpe, S.J., Gegenfurtner, K.R., Fabre-Thorpe, M., Bulthoff, H.H., Detection of animals in natural images using far peripheral vision. Eur J Neurosci, 14: (2001) 869–876.CrossRefGoogle Scholar
  8. 8.
    Rousselet, G.A., Fabre-Thorpe, M., Thorpe, S.J., Parallel processing in high level categorisation of natural images. Nature Neuroscience, 5: (2002) 629–30.Google Scholar
  9. 9.
    Li, F.F., VanRullen, R., Koch, C., Perona, P., Rapid natural scene categorization in the near absence of attention. Proc Natl Acad Sci U S A, 99: (2002) 9596–601.Google Scholar
  10. 10.
    Thorpe, S.J., Bacon, N., Rousselet, G., Macé, M.J.-M., Fabre-Thorpe, M., Rapid categorisation of natural scenes: feed-forward vs. feedback contribution evaluated by backwards masking. Perception, 31 suppl: (2002) 150.Google Scholar
  11. 11.
    Fabre-Thorpe, M., Richard, G., Thorpe, S.J., Rapid categorization of natural images by rhesus monkeys. NeuroReport, 9: (1998) 303–308.CrossRefGoogle Scholar
  12. 12.
    Nowak, L.G., Bullier, J., The timing of information transfer in the visual system, in J. Kaas, K. Rockland, and A. Peters, Editors. (eds) Extrastriate cortex in primates, Plenum: New York. (1997) 205–241.CrossRefGoogle Scholar
  13. 13.
    Logothetis, N.K., Sheinberg, D.L., Visual object recognition. Annu Rev Neurosci, 19: (1996) 577–621.CrossRefGoogle Scholar
  14. 14.
    Rolls, E.T., Deco, G., Computational Neuroscience of Vision. Oxford: Oxford University Press (2002)Google Scholar
  15. 15.
    Oram, M.W., Perrett, D.I., Time course of neural responses discriminating different views of the face and head. J Neurophysiol, 68: (1992) 70–84.Google Scholar
  16. 16.
    Keysers, C., Xiao, D.K., Foldiak, P., Perrett, D.I., The speed of sight. J Cogn Neurosci, 13: (2001) 90–101.CrossRefGoogle Scholar
  17. 17.
    Thorpe, S.J., Imbert, M., Biological constraints on connectionist models., inR. Pfeifer, et al., Editors. (eds) Connectionism in Perspective., Elsevier: Amsterdam. (1989) 63–92.Google Scholar
  18. 18.
    Thorpe, S., Delorme, A., Van Rullen, R., Spike-based strategies for rapid processing. Neural Networks, 14: (2001) 715–25.CrossRefGoogle Scholar
  19. 19.
    van Rossum, M.C., Turrigiano, G.G., Nelson, S.B., Fast propagation of firing rates through layered networks of noisy neurons. J Neurosci, 22: (2002) 1956–66.Google Scholar
  20. 20.
    VanRullen, R., Thorpe, S.J., Rate coding versus temporal order coding: what the retinal ganglion cells tell the visual cortex. Neural Comput, 13: (2001) 1255–83.CrossRefzbMATHGoogle Scholar
  21. 21.
    Thorpe, S.J., Spike arrival times: A highly efficient coding scheme for neural networks., in R. Eckmiller, G. Hartman, and G. Hauske, Editors. (eds) Parallel processing in neural systems, Elsevier: North-Holland. (1990) 91–94.Google Scholar
  22. 22.
    Gautrais, J., Thorpe, S., Rate coding versus temporal order coding: a theoretical approach. Biosystems, 48: (1998) 57–65.CrossRefGoogle Scholar
  23. 23.
    Thorpe, S.J., Gautrais, J., Rank Order Coding, in J. Bower, Editor. (eds) Computational Neuroscience: Trends in Research 1998, Plenum Press: New York. (1998) 113–118.CrossRefGoogle Scholar
  24. 24.
    VanRullen, R., Gautrais, J., Delorme, A., Thorpe, S., Face processing using one spike per neurone. Biosystems, 48: (1998) 229–39.CrossRefGoogle Scholar
  25. 25.
    Delorme, A., Thorpe, S.J., Face identification using one spike per neuron: resistance to image degradations. Neural Networks, 14: (2001) 795–803.CrossRefGoogle Scholar
  26. 26.
    Giurfa, M., Menzel, R., Insect visual perception: complex abilities of simple nervous systems. Curr Opin Neurobiol, 7: (1997) 505–13.CrossRefGoogle Scholar
  27. 27.
    Troje, N.F., Huber, L., Loidolt, M., Aust, U., Fieder, M., Categorical learning in pigeons: the role of texture and shape in complex static stimuli. Vision Res, 39: (1999) 353–66.CrossRefGoogle Scholar
  28. 28.
    VanRullen, R., Delorme, A., Thorpe, S.J., Feed-forward contour integration in primary visual cortex based on asynchronous spike propagation. Neurocomputing, 38-40: (2001) 1003–1009.Google Scholar
  29. 29.
    Bullier, J., Integrated model of visual processing. Brain Res Brain Res Rev, 36: (2001) 96–107.CrossRefGoogle Scholar
  30. 30.
    Bullier, J., Hupe, J.M., James, A.C., Girard, P., The role of feedback connections in shaping the responses of visual cortical neurons. Prog Brain Res, 134: (2001) 193–204.CrossRefGoogle Scholar
  31. 31.
    Ullman, S., Vidal-Naquet, M., Sali, E., Visual features of intermediate complexity and their use in classification. Nat Neurosci, 5: (2002) 682–7.Google Scholar
  32. 32.
    Lamme, V.A., Roelfsema, P.R., The distinct modes of vision offered by feedforward and recurrent processing. Trends Neurosci, 23: (2000) 571–9.CrossRefGoogle Scholar
  33. 33.
    Roelfsema, P.R., Lamme, V.A., Spekreijse, H., Bosch, H., Figure-ground segregation in a recurrent network architecture. J Cogn Neurosci, 14: (2002) 525–37.CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2002

Authors and Affiliations

  • Simon Thorpe
    • 1
    • 2
  1. 1.Centre de Recherche Cerveau & CognitionToulouseFrance
  2. 2.SpikeNet Technology S.A.R.L.RevelFrance

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