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Abstract:
One approach to invariant object recognition employs a
recurrent neural network as an associative memory. In the standard
depiction of the network's state space, memories of objects are
stored as attractive fixed points of the dynamics. I argue for a
modification of this picture: if an object has a continuous family
of instantiations, it should be represented by a continuous
attractor. This idea is illustrated with a network that learns to
complete patterns. To perform the task of filling in missing
information, the network develops a continuous attractor that
models the manifold from which the patterns are drawn. From a
statistical viewpoint, the pattern completion task allows a
formulation of unsupervised learning in terms of regression rather
than density estimation.
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