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Understanding how sequences are learned and encoded is a key component to understanding cognition. We present a recruitment model in which sequences are learned via the hierarchical binding of features across time. Learning in the model is unsupervised and occurs within a single presentation of the input. The topology and learning mechanisms allow the network to exploit the temporal structure of the...
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