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Abstract:
We first describe a hierarchical, generative model that can
be viewed as a non-linear generalization of factor analysis and can
be implemented in a neural network. The model performs perceptual
inference in a probabilistically consistent manner by using
top-down, bottom-up and lateral connections. These connections can
be learned using simple rules that require only locally available
information. We then show how to incorporate non-adaptive lateral
connections into the generative model. The model extracts a sparse,
distributed, hierarchical representation of depth from simplified
random-dot stereograms and the localized disparity detectors in the
first hidden layer form a topographic map.
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