Abstract
The learning and computing processes in a recursive neural network of the Hopfield type are identified as slow and fast phenomena. The corresponding dynamical equations are cast to fit into the framework of the theory of singular perturbations and time scales. The issues of degeneration and asymptotic expansions arising in obtaining approximate solutions are addressed. >
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