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Hidden units are
recruited during so-called input phases. Output weights are frozen
(shown by solid large arrow). Eight candidate units each have initially
random, trainable connection weights from the input units (shown by dashed
large arrow). These input weights are adjusted in order to maximize a correlation
between their activation and network error at the output units, over the
training patterns.
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