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Cresceptron uses a hierarchical framework to grow neural networks automatically, adaptively, and incrementally through learning. At every level of the hierarchy, new concepts are detected automatically and the network grows by creating new neurons and synapses which memorize the new concepts and their context. The training samples are generalized to other perceptually equivalent items through hierarchical tolerance of deviation. The neural network recognizes the learned items and their variations by hierarchically associating the learned knowledge with the input. It segments the recognized items from the input through back training along the response paths.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">></ETX>
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Weng et al. (2003) studied this question.
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