Self-organizing neural network that discovers surfaces in random-dot stereograms

Abstract

THE standard form of back-propagation learning1 is implausible as a model of perceptual learning because it requires an external teacher to specify the desired output of the network. We show how the external teacher can be replaced by internally derived teaching signals. These signals are generated by using the assumption that different parts of the perceptual input have common causes in the external world. Small modules that look at separate but related parts of the perceptual input discover these common causes by striving to produce outputs that agree with each other (Fig. la). The modules may look at different modalities (such as vision and touch), or the same modality at different times (for example, the consecutive two-dimensional views of a rotating three-dimensional object), or even spatially adjacent parts of the same image. Our simulations show that when our learning procedure is applied to adjacent patches of two-dimensional images, it allows a neural network that has no prior knowledge of the third dimension to discover depth in random dot stereograms of curved surfaces.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

References

  1. 1

    Rumelhart, D. E., Hinton, G. E. & Williams, R. J. Nature 323, 533–536 (1986).

    ADS  Article  Google Scholar 

  2. 2

    Hastie, T. J. & Tibshirani, R. J. Generalized Additive Models (Chapman and Hall, London, 1990).

    Google Scholar 

  3. 3

    Lehky S. R. & Sejnowski, T. J. J. Neurosci. 10, 2281–2299 (1990).

    CAS  Article  Google Scholar 

  4. 4

    Zemel, R. S. & Hinton, G. E. in Advances in Neural Information Processing Systems Vol. 3 (eds Lippman, R. P., Moody, J. E. & Touretzky, D. S.) 299–305 (Morgan Kaufmann, San Mateo, CA, 1991).

    Google Scholar 

Download references

Author information

Affiliations

Authors

Rights and permissions

Reprints and Permissions

About this article

Cite this article

Becker, S., Hinton, G. Self-organizing neural network that discovers surfaces in random-dot stereograms. Nature 355, 161–163 (1992). https://doi.org/10.1038/355161a0

Download citation

Further reading

Comments

By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.