  The information conveyed by a hierarchical attractor neural network is
examined. The network learns sets of correlated patterns (the examples) in the
lowest level of the hierarchical tree and can categorize them at the upper
levels. A way to measure the non-extensive information content of the examples
is formulated. Curves showing the transition from a large retrieval information
to a large categorization information behavior, when the number of examples
increase, are displayed. The conditions for the maximal information are given
as functions of the correlation between examples and the load of concepts.
Numerical simulations support the analytical results.
