A SELF-ORGANIZING NEURAL NETWORK THAT
DISCOVERS SURFACES IN RANDOM-DOT STEREOGRAMS
Suzanna Becker and Geoffrey E. Hinton
The standard form of backpropagation learning
Rumelhart, Hinton & Williams, 1986) 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 (see figure 1a). The modules may look at different
modalities (e.g. vision and touch), or the same modality at different times (e.g. the
consecutive 2-D views of a rotating 3-D object), or even spatially adjacent parts of the
same image. Our simulations show that when our learning procedure is applied to adjacent
patches of 2-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.