  A generalized-statistics variational principle for source separation is
formulated by recourse to Tsallis' entropy subjected to the additive duality
and employing constraints described by normal averages. The variational
principle is amalgamated with Hopfield-like learning rules resulting in an
unsupervised learning model. The update rules are formulated with the aid of
q-deformed calculus. Numerical examples exemplify the efficacy of this model.
